EViews 8.1Estimation Forecasting Statistical Analysis

Graphics Data Management Simulation

Users Guide II

UIIEViews 8.1 Users Guide II

EViews 8.1 Users Guide IICopyAll Ri

ISBN

Thisreserthe ono pin a

Disc

The mangramgram

Trad

EVieare rtion.gramVictoreferJeanfile iuct ntive

IHS G4521IrvineTelepFax: (e-maweb:

Septright 19942014 IHS Global Inc.ghts Reserved

: 978-1-880411-09-4

software product, including program code and manual, is copyrighted, and all rights are ved by IHS Global Inc. The distribution and sale of this product are intended for the use of riginal purchaser only. Except as permitted under the United States Copyright Act of 1976, art of this product may be reproduced or distributed in any form or by any means, or stored database or retrieval system, without the prior written permission of IHS Global Inc.

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authors and IHS Global Inc. assume no responsibility for any errors that may appear in this ual or the EViews program. The user assumes all responsibility for the selection of the pro- to achieve intended results, and for the installation, use, and results obtained from the pro-.

emarks

ws is a registered trademark of IHS Global Inc. Windows, Excel, PowerPoint, and Access egistered trademarks of Microsoft Corporation. PostScript is a trademark of Adobe Corpora- X11.2 and X12-ARIMA Version 0.2.7, and X-13ARIMA-SEATS are seasonal adjustment pro-s developed by the U. S. Census Bureau. Tramo/Seats is copyright by Agustin Maravall and r Gomez. Info-ZIP is provided by the persons listed in the infozip_license.txt file. Please to this file in the EViews directory for more information on Info-ZIP. Zlib was written by -loup Gailly and Mark Adler. More information on zlib can be found in the zlib_license.txt n the EViews directory. Bloomberg is a trademark of Bloomberg Finance L.P. All other prod-ames mentioned in this manual may be trademarks or registered trademarks of their respec-

companies.

lobal Inc. Campus Drive, #336 CA, 92612-2621hone: (949) 856-3368949) 856-2044il: sales@eviews.com www.eviews.com

ember 21, 2014

Table of Contents

EVIEWS 8

PREFACE .

PART I. EV

CHAPTER 1.

What is

Installin

The EVie

Custom

Breaking

Closing

Where t

EViews

CHAPTER 2.

Getting D

Examini

Estimati

Specifica

Modifyin

Forecast

Addition

CHAPTER 3.

What is

Creating

The Wor

Saving a

Loading

Multi-pa

Compari

Addendu.1 USERS GUIDE I 1

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

IEWS FUNDAMENTALS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

EViews? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

g and Running EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

ws Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Edit Fields in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

or Canceling in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

o Go For Help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

A DEMONSTRATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

ata into EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

ng the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

ng a Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

tion and Hypothesis Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

g the Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

ing from an Estimated Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

al Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

WORKFILE BASICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

a Workfile? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

a Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

kfile Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

a Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

ge Workfiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

ng Workfiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

m: File Dialog Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

iiTable of Contents

CHAPTER 4. OBJECT BASICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

What is an Object? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .93

Basic Ob

The Obje

Working

CHAPTER 5.

Data Ob

Samples

Sample O

Importin

Exportin

Reading

Frequen

Importin

CHAPTER 6.

Numeric

Series .

Auto-ser

Groups

Scalars

CHAPTER 7.

Auto-Up

Alpha Se

Date Ser

Value M

CHAPTER 8.

Basic Lin

Creating

Working

CHAPTER 9.

Structuri

Resizing

Appendiject Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .97

ct Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .99

with Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .101

BASIC DATA HANDLING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .109

jects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .109

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .119

bjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .127

g Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .129

g Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .143

EViews Data using Other Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .145

cy Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151

g ASCII Text Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .156

WORKING WITH DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .165

Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .165

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .175

ies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .179

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .184

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .188

WORKING WITH DATA (ADVANCED) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .189

dating Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .189

ries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .194

ies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .202

aps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .205

SERIES LINKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .219

k Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .219

a Link . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .233

with Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .243

ADVANCED WORKFILES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .249

ng a Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .249

a Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .274

ng to a Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .277

Table of Contentsiii

Contracting a Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280

Copying from a Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280

Reshapin

Sorting a

Exportin

Referenc

CHAPTER 10

Database

Database

Working

Database

The Data

Queryin

Object A

Maintain

Foreign

Working

PART II. BA

CHAPTER 11

Series V

Spreadsh

Graph .

Descript

One-Wa

Correlog

Long-run

Unit Roo

Variance

BDS Ind

Label .

Propertie

Series A

Series Pr

Generate

Generateg a Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284

Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301

g from a Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302

. EVIEWS DATABASES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303

Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304

with Objects in Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308

Auto-Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315

base Registry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317

g the Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319

liases and Illegal Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327

ing the Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329

Format Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331

with DRIPro Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357

SIC DATA ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .365

. SERIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367

iews Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367

eet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368

ive Statistics & Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368

y Tabulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386

ram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387

Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390

t Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390

Ratio Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390

ependence Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391

s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391

djust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392

ocs Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394

by Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394

by Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394

ivTable of Contents

Resample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .399

Interpolate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .401

Seasonal

Exponen

Hodrick-

Frequen

Whiten D

Distribut

Referenc

CHAPTER 12

Group V

Group M

Spreadsh

Dated D

Graph .

Descript

Covarian

N-Way T

Tests of

Principa

Correlog

Cross Co

Long-run

Unit Roo

Cointegr

Granger

Label .

Group P

Referenc

CHAPTER 13

Quick St

Graphing

Graphing

The Grap

Basic Cu Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .404

tial Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .437

Prescott Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .463

cy (Band-Pass) Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .464

ata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .468

ion Plot Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .468

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .469

. GROUPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .473

iews Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .473

embers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .473

eet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .474

ata Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .479

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .496

ive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .496

ce Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .496

abulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .509

Equality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .513

l Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .514

rams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .527

rrelations and Correlograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .527

Covariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .527

t Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .533

ation Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .533

Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .533

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .535

rocedures Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .535

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .539

. GRAPHING DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .541

art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .542

a Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .545

Multiple Series (Groups) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .554

h Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .565

stomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .568

Table of Contentsv

Graph Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631

CHAPTER 14

Illustrati

Specifyin

PART III. C

CHAPTER 15

Creating

Combini

Customi

Modifyin

Printing

Saving G

Graph C

CHAPTER 16

Table Ob

Text Obj

CHAPTER 17

Creating

Managin

Customi

Editing O

Printing

Saving a

PART IV. ECreate Y

Add Men

Connect

CHAPTER 18

Embedd

Using OL

Opening. CATEGORICAL GRAPHS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633

ve Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633

g Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 650

USTOMIZING OUTPUT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .665

. GRAPH OBJECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667

Graph Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667

ng Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672

zing Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672

g Multiple Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693

Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696

raphs to a File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697

ommands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 698

. TABLE AND TEXT OBJECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699

jects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699

ects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708

. SPOOL OBJECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711

a Spool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711

g the Spool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712

zing the Spool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723

bjects in a Spool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 726

a Spool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729

Spool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 730

XTENDING EVIEWS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .733our Own Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733

u Items and Object Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733

with External Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734

. OBJECT LINKING AND EMBEDDING (OLE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737

ing vs. Linking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738

E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738

Documents with Linked Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765

viTable of Contents

APPENDIX A. GLOBAL OPTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .769

The Options Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .769

Print Set

APPENDIX B

Array Ex

INDEX . . . .

EVIEWS 8

PREFACE .

PART V. BA

CHAPTER 19

Equation

Specifyin

Estimatin

Equation

Working

Estimatio

Referenc

CHAPTER 20

Special E

Robust S

Weighte

Nonlinea

Stepwise

Referenc

CHAPTER 21

Backgrou

Two-stag

Nonlinea

Limited

Generali

IV Diagn

Referencup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .787

. ENHANCED SPREADSHEET EDITING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .791

pressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .791

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .801

.1 USERS GUIDE II 1

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

SIC SINGLE EQUATION ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

. BASIC REGRESSION ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

g an Equation in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

g an Equation in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

with Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

n Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

. ADDITIONAL REGRESSION TOOLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

quation Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

tandard Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32

d Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36

r Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40

Least Squares Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53

. INSTRUMENTAL VARIABLES AND GMM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

nd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55

e Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55

r Two-stage Least Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62

Information Maximum Likelihood and K-Class Estimation . . . . . . . . . . . . . . . . . . . . . .63

zed Method of Moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67

ostics and Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .78

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83

Table of Contentsvii

CHAPTER 22. TIME SERIES REGRESSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Serial Correlation Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Testing f

Estimati

ARIMA T

Estimati

ARMA E

Referenc

CHAPTER 23

Forecast

An Illust

Forecast

Forecast

Forecast

Forecast

Forecast

Referenc

CHAPTER 24

Backgro

Coefficie

Residual

Stability

Applicat

Referenc

PART VI. A

CHAPTER 25

Basic AR

Estimati

Working

Addition

Example

Referencor Serial Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

ng AR Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

heory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

ng ARIMA Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

quation Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

. FORECASTING FROM AN EQUATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

ing from Equations in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

ration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

s with Lagged Dependent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

ing with ARMA Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

ing from Equations with Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

ing with Nonlinear and PDL Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

. SPECIFICATION AND DIAGNOSTIC TESTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

und . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

nt Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

ions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

DVANCED SINGLE EQUATION ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .205

. ARCH AND GARCH ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

CH Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

ng ARCH Models in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

with ARCH Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

al ARCH Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220

s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228

viiiTable of Contents

CHAPTER 26. COINTEGRATING REGRESSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .231

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .231

Estimatin

Testing f

Working

Referenc

CHAPTER 27

Binary D

Ordered

Censored

Truncate

Heckma

Count M

Technica

Referenc

CHAPTER 28

Overview

How to E

Example

Working

Technica

Referenc

CHAPTER 29

Backgrou

Estimatin

An Illust

Referenc

CHAPTER 30

Backgrou

Estimatin

Working

Example

Referencg a Cointegrating Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .233

or Cointegration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .246

with an Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .255

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .257

. DISCRETE AND LIMITED DEPENDENT VARIABLE MODELS . . . . . . . . . . . . . . . . . . . . . . .259

ependent Variable Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .259

Dependent Variable Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .278

Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .285

d Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .295

n Selection Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .299

odels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .305

l Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .315

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .317

. GENERALIZED LINEAR MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .319

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .319

stimate a GLM in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .321

s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .325

with a GLM Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .333

l Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .337

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .348

. ROBUST LEAST SQUARES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .349

nd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .349

g Robust Regression in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .357

ration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .362

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .368

. LEAST SQUARES WITH BREAKPOINTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .369

nd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .369

g Least Squares with Breakpoints in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .371

with Breakpoint Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .374

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .381

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .387

Table of Contentsix

CHAPTER 31. SWITCHING REGRESSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389

Estimati

Estimati

Switchin

Switchin

Example

Referenc

CHAPTER 32

Estimati

Views an

Backgro

Referenc

CHAPTER 33

Overview

Specifica

Estimati

LogL Vie

LogL Pro

Troubles

Limitatio

Example

Referenc

PART VII. A

CHAPTER 34

Unit Roo

Panel Un

Variance

BDS Ind

Referenc

PART VIII.

CHAPTER 35

Backgrong Switching Regressions in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397

on Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402

g Views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404

g Procs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407

s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422

. QUANTILE REGRESSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423

ng Quantile Regression in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423

d Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428

und . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445

. THE LOG LIKELIHOOD (LOGL) OBJECT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447

tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449

on . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454

ws . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456

cs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457

hooting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459

ns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460

s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467

DVANCED UNIVARIATE ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .469

. UNIVARIATE TIME SERIES ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471

t Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471

it Root Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483

Ratio Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494

ependence Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508

MULTIPLE EQUATION ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .511

. SYSTEM ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513

und . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513

xTable of Contents

System Estimation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .514

How to Create and Specify a System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .516

Working

Technica

Referenc

CHAPTER 36

Vector A

Estimatin

VAR Est

Views an

Structura

Vector E

Bayesian

A Note o

Referenc

CHAPTER 37

Backgrou

Specifyin

Working

Converti

Technica

Referenc

CHAPTER 38

Overview

An Exam

Building

Working

Specifyin

Using Ad

Solving t

Working

Compari

Referenc With Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .527

l Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .540

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .550

. VECTOR AUTOREGRESSION AND ERROR CORRECTION MODELS . . . . . . . . . . . . . . . . . .553

utoregressions (VARs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .553

g a VAR in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .554

imation Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .554

d Procs of a VAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .556

l (Identified) VARs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .565

rror Correction (VEC) Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .572

VAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .578

n Version Compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .600

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .600

. STATE SPACE MODELS AND THE KALMAN FILTER . . . . . . . . . . . . . . . . . . . . . . . . . . . . .603

nd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .603

g a State Space Model in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .608

with the State Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .619

ng from Version 3 Sspace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .625

l Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .625

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .626

. MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .627

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .627

ple Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .630

a Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .645

with the Model Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .647

g Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .651

d Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .654

he Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .657

with the Model Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .675

ng Solution Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .679

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .681

Table of Contentsxi

PART IX. PANEL AND POOLED DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .683

CHAPTER 39

The Poo

The Poo

Pooled D

Setting u

Working

Pooled E

Referenc

CHAPTER 40

Structur

Panel W

Panel W

Working

Panel Da

Referenc

CHAPTER 41

Estimati

Panel Es

Panel Eq

Estimati

Referenc

CHAPTER 42

Backgro

Estimati

Working

Example

Technica

Referenc

CHAPTER 43

By-Statis

Panel Co

Panel Pr. POOLED TIME SERIES, CROSS-SECTION DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685

l Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685

l Object . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 686

ata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689

p a Pool Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691

with Pooled Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 698

stimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 706

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733

. WORKING WITH PANEL DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735

ing a Panel Workfile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735

orkfile Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738

orkfile Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739

with Panel Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743

ta Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 756

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 756

. PANEL ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759

ng a Panel Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759

timation Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 766

uation Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 780

on Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796

. PANEL COINTEGRATION ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797

und . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797

ng Panel Cointegration in EViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 798

With a Panel Cointegrating Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 804

s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805

l Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 816

. PANEL STATISTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819

tics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 822

variances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825

incipal Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 830

xiiTable of Contents

Panel Causality Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .836

Panel Long-run Variances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .838

Panel Un

Panel Co

Panel Re

Panel Sta

Referenc

PART X. AD

CHAPTER 44

Johansen

Single-Eq

Panel Co

Referenc

CHAPTER 45

Creating

Rotating

Estimatin

Factor V

Factor P

Factor D

An Exam

Backgrou

Referenc

APPENDIX C

Setting E

Optimiza

Nonlinea

Referenc

APPENDIX D

Gradient

Derivativ

Referencit Root Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .840

integration Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .842

sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .844

cked Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .844

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .845

VANCED MULTIVARIATE ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .847

. COINTEGRATION TESTING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .849

Cointegration Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .849

uation Cointegration Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .858

integration Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .862

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .867

. FACTOR ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .869

a Factor Object . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .870

Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .876

g Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .877

iews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .880

rocedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .884

ata Members . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .885

ple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .885

nd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .900

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .912

. ESTIMATION AND SOLUTION OPTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .915

stimation Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .915

tion Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .919

r Equation Solution Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .923

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .925

. GRADIENTS AND DERIVATIVES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .927

s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .927

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .930

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .934

Table of Contentsxiii

APPENDIX E. INFORMATION CRITERIA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935

Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935

Using In

Referenc

APPENDIX F

Technica

Kernel F

Referenc

INDEX . . . .formation Criteria as a Guide to Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937

. LONG-RUN COVARIANCE ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 939

l Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 939

unction Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 949

es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 950

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .951

xivTable of Contents

Preface

The desc

The toolsbe d

first volume of the EViews 8 Users Guide describes the basics of using EViews and ribes a number of tools for basic statistical analysis using series and group objects.

second volume of the EViews 8 Users Guide, offers a description of EViews interactive for advanced statistical and econometric analysis. The material in Users Guide II may ivided into several parts:

Part V. Basic Single Equation Analysis on page 3 discusses the use of the equation object to perform standard regression analysis, ordinary least squares, weighted least squares, nonlinear least squares, basic time series regression, specification testing and forecasting.

Part VI. Advanced Single Equation Analysis, beginning on page 205 documents two-stage least squares (TSLS) and generalized method of moments (GMM), autoregres-sive conditional heteroskedasticity (ARCH) models, single-equation cointegration equation specifications, discrete and limited dependent variable models, generalized linear models (GLM), robust least squares, least squares regression with breakpoints, switching regression, quantile regression, and user-specified likelihood estimation.

Part VII. Advanced Univariate Analysis, on page 469 describes advanced tools for univariate time series analysis, including unit root tests in both conventional and panel data settings, variance ratio tests, and the BDS test for independence.

Part VIII. Multiple Equation Analysis on page 511 describes estimation and forecast-ing with systems of equations (least squares, weighted least squares, SUR, system TSLS, 3SLS, FIML, GMM, multivariate ARCH), vector autoregression and error correc-tion models (VARs and VECs), state space models and model solution.

Part IX. Panel and Pooled Data on page 683 documents working with and estimat-ing models with time series, cross-sectional data. The analysis may involve small numbers of cross-sections, with series for each cross-section variable (pooled data) or large numbers systems of cross-sections, with stacked data (panel data).

Part X. Advanced Multivariate Analysis, beginning on page 847 describes tools for testing for cointegration and for performing Factor Analysis.

2Preface

Part V. Basic Single Equation Analysis

The serie

The heteAdv

Multtion

Partand following chapters describe the EViews features for basic single equation and single s analysis.

Chapter 19. Basic Regression Analysis, beginning on page 5 outlines the basics of ordinary least squares estimation in EViews.

Chapter 20. Additional Regression Tools, on page 23 discusses special equation terms such as PDLs and automatically generated dummy variables, robust standard errors, weighted least squares, and nonlinear least square estimation techniques.

Chapter 21. Instrumental Variables and GMM, on page 55 describes estimation of single equation Two-stage Least Squares (TSLS), Limited Information Maximum Like-lihood (LIML) and K-Class Estimation, and Generalized Method of Moments (GMM) models.

Chapter 22. Time Series Regression, on page 85 describes a number of basic tools for analyzing and working with time series regression models: testing for serial corre-lation, estimation of ARMAX and ARIMAX models, and diagnostics for equations esti-mated using ARMA terms.

Chapter 23. Forecasting from an Equation, beginning on page 111 outlines the fun-damentals of using EViews to forecast from estimated equations.

Chapter 24. Specification and Diagnostic Tests, beginning on page 139 describes specification testing in EViews.

chapters describing advanced single equation techniques for autoregressive conditional roskedasticity, and discrete and limited dependent variable models are listed in Part VI. anced Single Equation Analysis.

iple equation estimation is described in the chapters listed in Part VIII. Multiple Equa-Analysis.

IX. Panel and Pooled Data on page 683 describes estimation in pooled data settings panel structured workfiles.

4Part V. Basic Single Equation Analysis

Chapter 19. Basic Regression Analysis

Singniquestimmati

Substechels, els, abuild

You niqufoun

Whe

Equation

Singcreamateman

Nextand EVie

The any for wsquation le equation regression is one of the most versatile and widely used statistical tech-es. Here, we describe the use of basic regression techniques in EViews: specifying and ating a regression model, performing simple diagnostic analysis, and using your esti-

on results in further analysis.

equent chapters discuss testing and forecasting, as well as advanced and specialized niques such as weighted least squares, nonlinear least squares, ARIMA/ARIMAX mod-two-stage least squares (TSLS), generalized method of moments (GMM), GARCH mod-nd qualitative and limited dependent variable models. These techniques and models all upon the basic ideas presented in this chapter.

will probably find it useful to own an econometrics textbook as a reference for the tech-es discussed in this and subsequent documentation. Standard textbooks that we have d to be useful are listed below (in generally increasing order of difficulty):

Pindyck and Rubinfeld (1998), Econometric Models and Economic Forecasts, 4th edition.

Johnston and DiNardo (1997), Econometric Methods, 4th Edition.

Wooldridge (2000), Introductory Econometrics: A Modern Approach.

Greene (2008), Econometric Analysis, 6th Edition.

Davidson and MacKinnon (1993), Estimation and Inference in Econometrics.

re appropriate, we will also provide you with specialized references for specific topics.

Objects

le equation regression estimation in EViews is performed using the equation object. To te an equation object in EViews: select Object/New Object.../Equation or Quick/Esti- Equation from the main menu, or simply type the keyword equation in the com-

d window.

, you will specify your equation in the Equation Specification dialog box that appears, select an estimation method. Below, we provide details on specifying equations in ws. EViews will estimate the equation and display results in the equation window.

estimation results are stored as part of the equation object so they can be accessed at time. Simply open the object to display the summary results, or to access EViews tools orking with results from an equation object. For example, you can retrieve the sum-of-res from any equation, or you can use the estimated equation as part of a multi-equa-model.

6Chapter 19. Basic Regression Analysis

Specifying an Equation in EViews

Whe

You in thificaand estim

In thspecdentpendvariaformof splist expreasiewithcatioor m

Spe

The use ilowetion,Equa

Noteserieincluas a it ouserie

You defastoren you create an equation object, a specification dialog box is displayed.

need to specify three things is dialog: the equation spec-tion, the estimation method, the sample to be used in ation.

e upper edit box, you can ify the equation: the depen- (left-hand side) and inde-ent (right-hand side) bles and the functional . There are two basic ways ecifying an equation: by and by formula or by ession. The list method is r but may only be used unrestricted linear specifi-ns; the formula method is more general and must be used to specify nonlinear models odels with parametric restrictions.

cifying an Equation by List

simplest way to specify a linear equation is to provide a list of variables that you wish to n the equation. First, include the name of the dependent variable or expression, fol-d by a list of explanatory variables. For example, to specify a linear consumption func- CS regressed on a constant and INC, type the following in the upper field of the tion Specification dialog:

cs c inc

the presence of the series name C in the list of regressors. This is a built-in EViews s that is used to specify a constant in a regression. EViews does not automatically de a constant in a regression so you must explicitly list the constant (or its equivalent) regressor. The internal series C does not appear in your workfile, and you may not use tside of specifying an equation. If you need a series of ones, you can generate a new s, or use the number 1 as an auto-series.

may have noticed that there is a pre-defined object C in your workfile. This is the ult coefficient vectorwhen you specify an equation by listing variable names, EViews s the estimated coefficients in this vector, in the order of appearance in the list. In the

Specifying an Equation in EViews7

example above, the constant will be stored in C(1) and the coefficient on INC will be held in C(2).

LaggatingFor e

tellslaggof IN

You lags.

regrefirst

regre

You spac

speca tw

Typiregredepeon eall otionbox matistan

Spe

You enouyoured series may be included in statistical operations using the same notation as in gener- a new series with a formulaput the lag in parentheses after the name of the series. xample, the specification:

cs cs(-1) c inc

EViews to regress CS on its own lagged value, a constant, and INC. The coefficient for ed CS will be placed in C(1), the coefficient for the constant is C(2), and the coefficient C is C(3).

can include a consecutive range of lagged series by using the word to between the For example:

cs c cs(-1 to -4) inc

sses CS on a constant, CS(-1), CS(-2), CS(-3), CS(-4), and INC. If you don't include the lag, it is taken to be zero. For example:

cs c inc(to -2) inc(-4)

sses CS on a constant, INC, INC(-1), INC(-2), and INC(-4).

may include auto-series in the list of variables. If the auto-series expressions contain es, they should be enclosed in parentheses. For example:

log(cs) c log(cs(-1)) ((inc+inc(-1)) / 2)

ifies a regression of the natural logarithm of CS on a constant, its own lagged value, and o period moving average of INC.

ng the list of series may be cumbersome, especially if you are working with many ssors. If you wish, EViews can create the specification list for you. First, highlight the ndent variable in the workfile window by single clicking on the entry. Next, CTRL-click ach of the explanatory variables to highlight them as well. When you are done selecting f your variables, double click on any of the highlighted series, and select Open/Equa-, or right click and select Open/as Equation.... The Equation Specification dialog should appear with the names entered in the specification field. The constant C is auto-cally included in this list; you must delete the C if you do not wish to include the con-t.

cifying an Equation by Formula

will need to specify your equation using a formula when the list method is not general gh for your specification. Many, but not all, estimation methods allow you to specify equation using a formula.

8Chapter 19. Basic Regression Analysis

An equation formula in EViews is a mathematical expression involving regressors and coef-ficients. To specify an equation using a formula, simply enter the expression in the dialog in placetion

Whemula

is in

Equaexpr

The

EVie

If yoableis th

EViesiontyperepoequa

EVieC(2)usin

but s

The restrcienlead of the list of variables. EViews will add an implicit additive disturbance to this equa-and will estimate the parameters of the model using least squares.

n you specify an equation by list, EViews converts this into an equivalent equation for-. For example, the list,

log(cs) c log(cs(-1)) log(inc)

terpreted by EViews as:

log(cs) = c(1) + c(2)*log(cs(-1)) + c(3)*log(inc)

tions do not have to have a dependent variable followed by an equal sign and then an ession. The = sign can be anywhere in the formula, as in:

log(urate) - c(1)*dmr = c(2)

residuals for this equation are given by:

. (19.1)

ws will minimize the sum-of-squares of these residuals.

u wish, you can specify an equation as a simple expression, without a dependent vari- and an equal sign. If there is no equal sign, EViews assumes that the entire expression e disturbance term. For example, if you specify an equation as:

c(1)*x + c(2)*y + 4*z

ws will find the coefficient values that minimize the sum of squares of the given expres-, in this case (C(1)*X+C(2)*Y+4*Z). While EViews will estimate an expression of this , since there is no dependent variable, some regression statistics (e.g. R-squared) are not rted and the equation cannot be used for forecasting. This restriction also holds for any tion that includes coefficients to the left of the equal sign. For example, if you specify:

x + c(1)*y = c(2)*z

ws finds the values of C(1) and C(2) that minimize the sum of squares of (X+C(1)*Y*Z). The estimated coefficients will be identical to those from an equation specified g:

x = -c(1)*y + c(2)*z

ome regression statistics are not reported.

two most common motivations for specifying your equation by formula are to estimate icted and nonlinear models. For example, suppose that you wish to constrain the coeffi-ts on the lags on the variable X to sum to one. Solving out for the coefficient restriction s to the following linear model with parameter restrictions:

e urate( )log c 1( )dmr c 2( )=

Estimating an Equation in EViews9

y = c(1) + c(2)*x + c(3)*x(-1) + c(4)*x(-2) + (1-c(2)-c(3)-c(4)) *x(-3)

To ecallydeta

OneficieMatOK. manwith

You atedequa

Estimatin

Esti

HaviMeth

Stanformods

Equasioning Mand otheequaexprexpr

Esti

You withby estimate a nonlinear model, simply enter the nonlinear formula. EViews will automati- detect the nonlinearity and estimate the model using nonlinear least squares. For ils, see Nonlinear Least Squares on page 40.

benefit to specifying an equation by formula is that you can elect to use a different coef-nt vector. To create a new coefficient vector, choose Object/New Object and select rix-Vector-Coef from the main menu, type in a name for the coefficient vector, and click In the New Matrix dialog box that appears, select Coefficient Vector and specify how y rows there should be in the vector. The object will be listed in the workfile directory the coefficient vector icon (the little ).

may then use this coefficient vector in your specification. For example, suppose you cre- coefficient vectors A and BETA, each with a single row. Then you can specify your tion using the new coefficients in place of C:

log(cs) = a(1) + beta(1)*log(cs(-1))

g an Equation in EViews

mation Methods

ng specified your equation, you now need to choose an estimation method. Click on the od: entry in the dialog and you will see a drop-down menu listing estimation methods.

dard, single-equation regression is per-ed using least squares. The other meth-are described in subsequent chapters.

tions estimated by cointegrating regres-, GLM or stepwise, or equations includ-

A terms, may only be specified by list may not be specified by expression. All r types of equations (among others, ordinary least squares and two-stage least squares, tions with AR terms, GMM, and ARCH equations) may be specified either by list or ession. Note that equations estimated by quantile regression may be specified by ession, but can only estimate linear specifications.

mation Sample

should also specify the sample to be used in estimation. EViews will fill out the dialog the current workfile sample, but you can change the sample for purposes of estimation ntering your sample string or object in the edit box (see Samples on page 119 of Users

b

10Chapter 19. Basic Regression Analysis

Guide I for details). Changing the estimation sample does not affect the current workfile sample.

If anthe eEViethe e

Hereple. obse

You ple aor nothe p

If yople t

sincesampple s

Somobsesageple. adju

Esti

EViematiand y of the series used in estimation contain missing data, EViews will temporarily adjust stimation sample of observations to exclude those observations (listwise exclusion). ws notifies you that it has adjusted the sample by reporting the actual sample used in stimation results:

we see the top of an equation output view. EViews reports that it has adjusted the sam-Out of the 372 observations in the period 1959M011989M12, EViews uses the 340 rvations with valid data for all of the relevant variables.

should be aware that if you include lagged variables in a regression, the degree of sam-djustment will differ depending on whether data for the pre-sample period are available t. For example, suppose you have nonmissing data for the two series M1 and IP over eriod 1959M011989M12 and specify the regression as:

m1 c ip ip(-1) ip(-2) ip(-3)

u set the estimation sample to the period 1959M011989M12, EViews adjusts the sam-o:

data for IP(3) are not available until 1959M04. However, if you set the estimation le to the period 1960M011989M12, EViews will not make any adjustment to the sam-

ince all values of IP(-3) are available during the estimation sample.

e operations, most notably estimation with MA terms and ARCH, do not allow missing rvations in the middle of the sample. When executing these procedures, an error mes- is displayed and execution is halted if an NA is encountered in the middle of the sam-EViews handles missing data at the very start or the very end of the sample range by sting the sample endpoints and proceeding with the estimation procedure.

mation Options

ws provides a number of estimation options. These options allow you to weight the esti-ng equation, to compute heteroskedasticity and auto-correlation robust covariances, to control various features of your estimation algorithm. These options are discussed in

Dependent Variable: Y Method: Least Squares Date: 08/08/09 Time: 14:44 Sample (adjusted): 1959M01 1989M12 Included observations: 340 after adjustments

Dependent Variable: M1 Method: Least Squares Date: 08/08/09 Time: 14:45 Sample: 1960M01 1989M12 Included observations: 360

Equation Output11

detail in Estimation Options on page 42.

Equation

Whedowthe w

Usin

wheis a

-vhand

In th

Coe

Reg

The regre

T

T = Output

n you click OK in the Equation Specification dialog, EViews displays the equation win- displaying the estimation output view (the examples in this chapter are obtained using orkfile Basics.WF1):

g matrix notation, the standard regression may be written as:

(19.2)

re is a -dimensional vector containing observations on the dependent variable, matrix of independent variables, is a -vector of coefficients, and is a

ector of disturbances. is the number of observations and is the number of right- side regressors.

e output above, is log(M1), consists of three variables C, log(IP), and TB3, where and .

fficient Results

ression Coefficients

column labeled Coefficient depicts the estimated coefficients. The least squares ssion coefficients are computed by the standard OLS formula:

(19.3)

Dependent Variable: LOG(M1) Method: Least Squares Date: 08/08/09 Time: 14:51 Sample: 1959M01 1989M12 Included observations: 372

Variable Coefficient Std. Error t-Statistic Prob.

C -1.699912 0.164954 -10.30539 0.0000LOG(IP) 1.765866 0.043546 40.55199 0.0000

TB3 -0.011895 0.004628 -2.570016 0.0106

R-squared 0.886416 Mean dependent var 5.663717Adjusted R-squared 0.885800 S.D. dependent var 0.553903S.E. of regression 0.187183 Akaike info criterion -0.505429Sum squared resid 12.92882 Schwarz criterion -0.473825Log likelihood 97.00979 Hannan-Quinn criter. -0.492878F-statist ic 1439.848 Durbin-W atson stat 0.008687Prob(F-s tat istic) 0.000000

y Xb e+=

y T XT k b k e

T k

y X372 k 3=

b

b XX( ) 1 Xy=

12Chapter 19. Basic Regression Analysis

If your equation is specified by list, the coefficients will be labeled in the Variable column with the name of the corresponding regressor; if your equation is specified by formula, EVie

For tbutiofixedconsothethe rassu

Stan

The The the stribustanstan

The

wherootscova

t-Sta

The errortic, yis eq

In cainste

Prob

The as exdistr

Thisvaluws lists the actual coefficients, C(1), C(2), etc.

he simple linear models considered here, the coefficient measures the marginal contri-n of the independent variable to the dependent variable, holding all other variables . If you have included C in your list of regressors, the corresponding coefficient is the tant or intercept in the regressionit is the base level of the prediction when all of the r independent variables are zero. The other coefficients are interpreted as the slope of elation between the corresponding independent variable and the dependent variable, ming all other variables do not change.

dard Errors

Std. Error column reports the estimated standard errors of the coefficient estimates. standard errors measure the statistical reliability of the coefficient estimatesthe larger tandard errors, the more statistical noise in the estimates. If the errors are normally dis-ted, there are about 2 chances in 3 that the true regression coefficient lies within one dard error of the reported coefficient, and 95 chances out of 100 that it lies within two dard errors.

covariance matrix of the estimated coefficients is computed as:

(19.4)

re is the residual. The standard errors of the estimated coefficients are the square of the diagonal elements of the coefficient covariance matrix. You can view the whole riance matrix by choosing View/Covariance Matrix.

tistics

t-statistic, which is computed as the ratio of an estimated coefficient to its standard , is used to test the hypothesis that a coefficient is equal to zero. To interpret the t-statis-ou should examine the probability of observing the t-statistic given that the coefficient ual to zero. This probability computation is described below.

ses where normality can only hold asymptotically, EViews will often report a z-statistic ad of a t-statistic.

ability

last column of the output shows the probability of drawing a t-statistic (or a z-statistic) treme as the one actually observed, under the assumption that the errors are normally ibuted, or that the estimated coefficients are asymptotically normally distributed.

probability is also known as the p-value or the marginal significance level. Given a p-e, you can tell at a glance if you reject or accept the hypothesis that the true coefficient

var b( ) s2 XX( ) 1 s2; e e T k( ) e; y Xb= = =e

Equation Output13

is zero against a two-sided alternative that it differs from zero. For example, if you are per-forming the test at the 5% significance level, a p-value lower than 0.05 is taken as evidence to reappr

For tat thcoefsis a

The dom

Sum

R-sq

The of ththe fablethanFor etains

EVie

whe

Adju

Onedecrone

The whicpute

The ting ject the null hypothesis of a zero coefficient. If you want to conduct a one-sided test, the opriate probability is one-half that reported by EViews.

he above example output, the hypothesis that the coefficient on TB3 is zero is rejected e 5% significance level but not at the 1% level. However, if theory suggests that the ficient on TB3 cannot be positive, then a one-sided test will reject the zero null hypothe-t the 1% level.

p-values for t-statistics are computed from a t-distribution with degrees of free-. The p-value for z-statistics are computed using the standard normal distribution.

mary Statistics

uared

R-squared ( ) statistic measures the success of the regression in predicting the values e dependent variable within the sample. In standard settings, may be interpreted as raction of the variance of the dependent variable explained by the independent vari-s. The statistic will equal one if the regression fits perfectly, and zero if it fits no better the simple mean of the dependent variable. It can be negative for a number of reasons. xample, if the regression does not have an intercept or constant, if the regression con- coefficient restrictions, or if the estimation method is two-stage least squares or ARCH.

ws computes the (centered) as:

(19.5)

re is the mean of the dependent (left-hand) variable.

sted R-squared

problem with using as a measure of goodness of fit is that the will never ease as you add more regressors. In the extreme case, you can always obtain an of if you include as many independent regressors as there are sample observations.

adjusted , commonly denoted as , penalizes the for the addition of regressors h do not contribute to the explanatory power of the model. The adjusted is com-d as:

(19.6)

is never larger than the , can decrease as you add regressors, and for poorly fit-models, may be negative.

T k

R2

R2

R2

R2 1 e ey y( ) y y( )------------------------------------- y; yt

t 1=

T

T= =y

R2 R2

R2

R2 R2 R2

R2

R2 1 1 R2( )T 1T k-------------=

R2 R2

14Chapter 19. Basic Regression Analysis

Standard Error of the Regression (S.E. of regression)

The of th

Sum

The sente

Log

EVieerrorcondand

The

Whenot i

Durb

The com

See Jtribu

As aThe tion extenlatedstandard error of the regression is a summary measure based on the estimated variance e residuals. The standard error of the regression is computed as:

(19.7)

-of-Squared Residuals

sum-of-squared residuals can be used in a variety of statistical calculations, and is pre-d separately for your convenience:

(19.8)

Likelihood

ws reports the value of the log likelihood function (assuming normally distributed s) evaluated at the estimated values of the coefficients. Likelihood ratio tests may be ucted by looking at the difference between the log likelihood values of the restricted unrestricted versions of an equation.

log likelihood is computed as:

(19.9)

n comparing EViews output to that reported from other sources, note that EViews does gnore constant terms in the log likelihood.

in-Watson Statistic

Durbin-Watson statistic measures the serial correlation in the residuals. The statistic is puted as

(19.10)

ohnston and DiNardo (1997, Table D.5) for a table of the significance points of the dis-tion of the Durbin-Watson statistic.

rule of thumb, if the DW is less than 2, there is evidence of positive serial correlation. DW statistic in our output is very close to one, indicating the presence of serial correla-in the residuals. See Serial Correlation Theory, beginning on page 85, for a more sive discussion of the Durbin-Watson statistic and the consequences of serially corre- residuals.

s e eT k( )------------------=

e e yi Xib( )2t 1=

T

=

l T2---- 1 2p( )log e e T( )log+ +( )=

DW et et 1( )2t 2=

T

et2t 1=

T

=

Equation Output15

There are better tests for serial correlation. In Testing for Serial Correlation on page 86, we discuss the Q-statistic, and the Breusch-Godfrey LM test, both of which provide a more gen-eral

Mea

The

Akai

The

whe

The AIC the son p

Schw

The addi

Han

The

F-St

The the snary

Undtion testing framework than the Durbin-Watson test.

n and Standard Deviation (S.D.) of the Dependent Variable

mean and standard deviation of are computed using the standard formulae:

(19.11)

ke Information Criterion

Akaike Information Criterion (AIC) is computed as:

(19.12)

re is the log likelihood (given by Equation (19.9) on page 14).

AIC is often used in model selection for non-nested alternativessmaller values of the are preferred. For example, you can choose the length of a lag distribution by choosing pecification with the lowest value of the AIC. See Appendix E. Information Criteria, age 935, for additional discussion.

arz Criterion

Schwarz Criterion (SC) is an alternative to the AIC that imposes a larger penalty for tional coefficients: