Download pdf - Modeling and Forecasting

8/7/2019 Modeling and Forecasting

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TERMINOLOGIES

Econometrics: Econometrics means economic measurement or measurement of economic

concepts and indicators. The econometrics may be defined as the social science in which the

tools of economic theory, mathematics and statistical inference are applied to the analysis of

economic phenomena. Econometrics is an amalgamation of economics, mathematical and

statistics properties used in the structural and empirical study of experimental and non-

experimental in scientific manner.

Ceteris Paribus: The assumption of nothing else changing.

Non-experimental Data: The data which are not collected through controlled experiment on

individual, firms or segment of economy. Non-experimental data is also known as observational

or retrospective data

Experimental Data: The data which are collected through controlled experiment in laboratory

environment in the natural sciences.

Empirical Analysis/Econometric Analysis: In an empirical analysis we use data to test a theory

or to estimate a relationship between results of data and predefined theory.

Structural Analysis: A structural analysis uses data having definite structure to test a theory and

estimate a structural relationship between variables.

Policy Analysis: A Policy analysis uses data to form a policy frame for government and

business.

Financial Modeling and Forecasting: Financial modeling is simply a financial theory derived

on the basis of economic (financial) data.


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FOUNDATION

1. Basics Statistics: Descriptive Statistics, Correlation, Regression and Time Series.2. Basic Mathematics: Matrix Algebra and Calculus.3. Basics of Economic and Financial Market:Market, Institutions, Instruments and Operation

of economic and financial variables (examples of all market instruments).

4. Basics of Model and Model Derivation: Concept and forms of model (logical,mathematical and econometric). Essentials Components of model derivation - Constant,

Variable, Intercept, Coefficient, Factors, Dummy, Weight and Error Term. Importance and

Uses of different components in model derivation. Model Derivation from - Quantitative

data, Qualitative data (Logit/Probit), and from Quantitative plus Qualitative Data.

5. Basics of Forecasting: Concept of Forecasting, Quantitative methods and qualitativemethods. Forecasting of variables (e.g. demand, price, risk and return of equity) in form of

logical, mathematical & econometric. Forecasting of simple, cross-sectional and panel data.

6. Uses of FMF: for data analysis, research, modeling and forecasting of Individual, Firm,Market, Industry, Sector, National and Global data.

7. Basics of SPSS: Concepts & Operations of SPSS. Applications of SPSS in Data Analysis.8. SPSS Exercise : Exercise Frequencies, Descriptive Statistics,Mean Comparison (one sample

TT, Independent Sample TT, One Way Analysis of Variance (ANOVA)). General Linear

Model (GLM) - Univariate analysis (UVA), Multivariate analysis (MVA), Repeatedvar

Analysis (RVA). LinearMix Model (LMM) Subject and Repeated Variable for Factor

Analysis, unstructured and structured data analysis. Correlation analysis (simple, partial,

bivariate, multiple and serial). Regression Analysis Linear Regression, Curve Estimation,Binary Logistic Regression, Multinomial Regression, Ordinal Regression, Probit Analysis,

General Loglinear and Logit analysis, Logistic Regression. Factor Analysis. Discriminant

Analysis (Descriptive and Fishers Coefficient). Test of data and result Z, T, F, Chi and

DW. Non-Parametric Test - Run, FKC, KS, Independent & Related Test (2&K).


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I. TYPES OF DATA

(Types of data for modeling & forecasting)

A. Classification on the basis of the properties of mathematical statistics -1. Two Variable Data2. Multiple Variable DataB. Classification on the basis of the properties of economics -1. Cross-Sectional Data2. Time Series Data3. Pooled or Panel DataC. Classification on the basis of the combined properties of Mathematical Statistics & Economics-1. Two Variables Cross-Sectional Data2. Multiple Variables Cross-Sectional Data3. Two Variables Time Series Data4. Multiple Variable Time Series Data5. Two Variables Pooled or Panel Data6. Multiple Variables Pooled or Panel Data

II. DERIVATION OF OLS ESTIMATES - TWO VARIABLES & MULTIPLE VARIABLES

Estimates of Parameters :

Financial Data Estimation on the basis of predefined model. Estimation of intercept parameter (e.g. ) and

slope parameter (e.g. ). IfY = f a + bX + u or Y = f + X + u

Here Y is dependent variable and X is independent variable.

1. Intercept parameter = YMean - (Beata x X Mean)

2. Slope parameter = Cov. Dx.dy / 2x

(dy.dx) / N

or = ---------------

(dx)2/N

We may N or N-1 depends on structure of data, the answer will be same.

II.A. : OLS ESTIMATES - TWO VARIABLES DATA

Two variables data is known as simple data which has two variables used in formation of complete

equation. Following are the examples in form of mathematical/statistical estimate and in form of

econometric estimate


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Y= f a + bX

orY= f a + bX + u

CGPA Variable 1st

Y =No. of APIM Students

Variable 2nd

X = No. of Non-APIM Students

A+ 10 12

A 12 15

B+ 14 18

B 16 21

C 18 24

orY= f a + bX

orY= f a + bX + u

Name of

City

Variable 1st

Y = Crime Rate

Variable 2nd

X = Weighted Score of the Factors of Crime

Delhi 24 7

Noida 33 8

GBad 37 86

Gurgaon 29 81

FBad 35 8

orY= f a + bX

orY= f a + bX + u

Year Variable 1st

Y = Disposable Income

Variable 2nd

C = Consumption

1stYr 10000 8000

2nd Yr 12000 9600

3rdYr 14000 11200

4th Yr 16000 12800

5th Yr 18000 14000


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= f + C + u

Year Variable 1st, =Revenue

e.g. Revenue of ABC Ltd.

Variable 2nd, C=Cost of Production

e.g. Cost of Production of ABC Ltd.

1st

Yr 10000 9000

2nd

Yr 12000 10800

3rdYr 14000 12600

4th Yr 16000 14400

5th

Yr 18000 16200

i = f + rj + u

Types of

Security

Variable 1st

i = Return of TCS Security

Variable 2nd

rj =Risk involved in TCS Security

S1 10 25

S2 12 30

S3 14 35

S4 16 40

S5 18 45

i = f + Cj+ u

Types of

Security

Variable 1st

i =Return of TCS Security

Variable 2nd

Cj = Credit Rating of TCS Security

S1 10 75

S2 12 70

S3 14 65

S4 16 60

S5 18 55


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Ri = f + Rm + u

Types of

Security

Variable 1st

Ri = Return of stock i

Suppose i = TCS Security

Variable 2nd

Rm =Risk of the sector.

Suppose Rm = ITES Sector

S1 10 12

S2 12 16

S3 14 24

S4 16 12

S5 18 15

Ri = f + Rmi + u

Types of

Security

Variable 1stRi = Return of stock i

Suppose i = a TCS Security

Variable 2ndRmi =Risk of the sector.

Suppose Rmi = Sensex Index

S1 10 12

S2 12 16

S3 14 24

S4 16 12

S5 18 15

II. B. : OLS ESTIMATES - MULTIPLE VARIABLE DATA

Multiple variables data is known as data which has more than two and more independent

variables used in formation of model. For Example -

Y= f a + (b1X1, b2X2)+ u

CGPA Variable 1st

Y =No. of APIM Students

Variable 2nd

X1 = No. of Students

from B Grade B. Schools

Variable 3rd

X2 = No. of Students from B+

Grade B. School

A+ 10 12

A 12 15

B+ 14 18

B 16 21

C 18 24 23


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Y= f + 1X1,2X2,, u

Name of

City

Variable 1st

Y = Crime Rate

Variable 2nd

X1 = Wt. Score of the Factors

of Personal Income

Variable 3rd

X2 = Wt. Score of the

level of Education

Delhi 24 22

Noida 33 16

GBad 37 15 12

Gurgaon 29 20 18

FBad 35 16

Y= f +( 1X1,2X2,) + u

Year

Variable 1st

Y = Total Disposable Income

Variable 2nd

X1 = Income from Salary

Variable 3rd

X1 = Income from Other

Sources

1st

Yr 10000 8000 2000

2nd Yr 12000 9600 2400

3rdYr 14000 11200 2800

4th

Yr 16000 12800 3200

5th Yr 18000 14000 4000

= + 1fc, 2vc, u

Year Variable 1st, =Revenue

e.g. Revenue of ABC Ltd.

Variable 2nd

fc=Fixed Cost of ABC Ltd.

Variable 3rd

vc = variable cost of ABC

Ltd.

1st

Yr 11000 1000 9000

2nd Yr 13000 1000 10800

3rdYr 15000 1000 12600

4th

Yr 17000 1000 14400

5th Yr 19000 1000 16200


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= + 1Rif, 2Ris, u

Types of

Security

Variable 1st

i = Return of TCS Security

Variable 2nd

Rif=Risk involved in the firm

(e.g. TCS)

Variable 3rd

ris =Risk involved in the

sector (e.g. ITES Sector)

S1 10 25 27

S2 12 30 30

S3 14 35 33

S4 16 40 36

S5 18 45 39

= + 1C, 2Rs, 3Rm, 4Reco, u

Types of

Security

Variable 1st

i = Return

of TCS

Security

Variable 2nd

Rif = Credit

Rating of Firm

(e.g. TCS)

Variable 3rd

Ris =Risk

involved in the

sector (e.g. ITES

Sector)

Variable 3rd

Rm =Risk

involved in the

market (e.g.

Sensex)

Variable 3rd

REco =Country

Risk

S1 10 25 27 27 27

S2 12 30 30 30 30

S3 14 35 33 33 33

S4 16 40 36 36 36

S5 18 45 39 39 39


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III. TERM PAPER ON - MODELING & FORECASTING OF FINANCIAL MARKET

INSTRUMENTS

OR

USES OF OLS ESTIMATES IN MODEL DERIVATION AND FORECASTING OF

FINANCIAL INSTRUMENTS (CASE WORK)

III. A. : CREDIT MARKET

1. Interest Rate on Credit and Deposit2. Sanction and Disbursement Ratio3. Interest Rate and Economic Development4. Disbursement and Economic Development5. Impact of macroeconomic factors on Behavior of credit market instrumentsIII. B. : CAPITAL MARKET

1. PrimaryMarket - QuantityMovement and Price Movement2. Secondary Market Instrument - QuantityMovement and Price Movement3. Risk-Return behavior of stocks in Primary Market4. Risk-Return behavior of stocks in SecondaryMarket5. Impact ofMacroeconomic Factors and Non-Economic Factors on Behavior of capital market

instrument.

III. C. : MONEYMARKET

1. MoneyMarket Instrument (RR, R-RR TB, Bond, Securities)2. Commercial Deposit - QuantitativeMovement, Price and Risk-Return Behavior3. Commercial Paper - QuantitativeMovement, Price and Risk-Return Behavior4. Treasury Bills (different days) - QuantitativeMovement, Price and Risk-Return Behavior5. CallMoney (different days)- Quantitative Movement, Price and Risk-Return Behavior6. Impact of Economic Factors and Non-Economic Factors on money market instruments.


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IV: PROPERTIES OF OLS ESTIMATES

V : LEAST SQUARE ESTIMATES - R, R2, ADJUSTED R

2.

VI : PARTIAL CORRELATION

VII : MULTIPLE CORRELATIONS

VIII : DUMMY VARIABLES

IX : STANDARD ERROR OF ESTIMATES

X : STANDARD ERROR OF REGRESSION COEFFICIENT

XI : CASE WORK / APPLICATION WORK ON SPSS

XII : MULTICONLINEARITY

XIII : HETEROSKEDASTICITY

XIV : HOMOSKEDASTICITY

XV : AUTOCORRELATION

XVI : CONCEPTS, FORMULA, DERIVATION AND USES OF TIME SERIES

MODELING.

XVII : TIME SERIES MODELING TWO VARIABLES AND MULTIPLE

VARIABLES.

XVIII : USES OF TIME SERIES IN MODEL DERIVATION AND FORECASTING OF

FINANCIAL INSTRUMENTS (DAILY, SEASONAL AND ANNUAL).

XIX : CASE ON TIME SERIES DATA AND USING SPSS

XX : CASE ON PANEL DATA AND USING SPSS