2
MRSyllabus_E0211.v1 COURSE Time Series Econometrics Academic Year: 2012/2013 Trimester | Semester: 1|2 (or 2|2) Instructor(s):João Valle e Azevedo Course Content : 1. Introduction 1.1 Nature of time series 1.2 Stationary Time Series 1.3 Auto-Regressive (AR) and Moving Average (MA) Processes 1.4 Distributed lag Models 2. Linear Regression Model with Time series 2.1 Standard Assumptions 2.2 Properties of OLS 2.3 Time trends and seasonality 2.4 Stationarity and Non-stationarity: consequences 2.5 Unit Roots tests 2.6 Introduction to Cointegration 3. Violation of Standard Assumptions: Serial Correlation and Heteroskedasticity 3.1 Properties of OLS 3.2 Detecting and correcting serial correlation 3.3 Robust inference 4. Vector Autoregressions 4.1 Basic Notions 4.2 Estimation 4.3 Granger Causality 4.4 Unit Roots and Cointegration 5. Time Series in the Frequency Domain (time allowing) 5.1 Spectral Representation of Stationary Time Series 5.2 Spectral Density 5.3 Linear Filters, Gain and Phase 5.4 HP filter and Band-Pass filters 5.5 Bivariate characterization: Coherence and Phase ____________________________________________________________________________ Course Objectives : To provide students with the basic tools to describe, analyze and model time series data.

COURSE Time Series Econometrics - CATÓLICA-LISBON

  • Upload
    others

  • View
    5

  • Download
    0

Embed Size (px)

Citation preview

MRSyllabus_E0211.v1

COURSE Time Series Econometrics

Academic Year: 2012/2013 Trimester | Semester: 1|2 (or 2|2) Instructor(s):João Valle e Azevedo Course Content:

1. Introduction

1.1 Nature of time series

1.2 Stationary Time Series

1.3 Auto-Regressive (AR) and Moving Average (MA) Processes

1.4 Distributed lag Models

2. Linear Regression Model with Time series

2.1 Standard Assumptions

2.2 Properties of OLS

2.3 Time trends and seasonality

2.4 Stationarity and Non-stationarity: consequences

2.5 Unit Roots tests

2.6 Introduction to Cointegration

3. Violation of Standard Assumptions: Serial Correlation and Heteroskedasticity

3.1 Properties of OLS

3.2 Detecting and correcting serial correlation

3.3 Robust inference

4. Vector Autoregressions

4.1 Basic Notions

4.2 Estimation

4.3 Granger Causality

4.4 Unit Roots and Cointegration

5. Time Series in the Frequency Domain (time allowing)

5.1 Spectral Representation of Stationary Time Series

5.2 Spectral Density

5.3 Linear Filters, Gain and Phase

5.4 HP filter and Band-Pass filters

5.5 Bivariate characterization: Coherence and Phase ____________________________________________________________________________ Course Objectives: To provide students with the basic tools to describe, analyze and model time series data.

MRSyllabus_E0211.v1

__________________________________________________________________________________ Grading: 70% Final Exam 30% Group Assignment _______________________________________________________________________________ Bibliography:

Hamilton, J. , Time Series Analysis, Princeton University Press

Cochrane, J. (2005) Time Series for Macroeconomics and Finance,

http://faculty.chicagobooth.edu/john.cochrane/research/Papers/time_series_book.pdf __________________________________________________________________________________ Biography: João Valle e Azevedo is an Economist at the Research Department of Banco de Portugal since 2007, being also Invited Assistant Professor at Universidade NOVA de Lisboa. He has previously worked as a Researcher at the Free University of Amsterdam. João received a Licenciatura in Mathematics Applied to Economics and Business (Technical University of Lisbon) an M.Sc. in Statistics (London School of Economics and Political Science) and a Ph.D. in Economics (Stanford University). His work has been published at the Journal of Business and Economic Statistics, Oxford Bulletin of Economics and Statistics and Journal of the Royal Statistical Society. __________________________________________________________________________________ Contact(s) and Office hours: [email protected] 213130163 Office hours by appointment (flexible) __________________________________________________________________________________