Applied Econometrics 4th Edition by Dimitrios Asteriou, Stephen Hall 1352012022 9781352012026

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ISBN 10: 1352012022

ISBN 13: 9781352012026

Author: Dimitrios Asteriou; Stephen G. Hall

This trusted textbook returns in its 4th edition with even more exercises to help consolidate understanding – and a companion website featuring additional materials, including a solutions manual for instructors. Offering a unique blend of theory and practical application, it provides ideal preparation for doing applied econometric work as it takes students from a basic level up to an advanced understanding in an intuitive, step-by-step fashion. Clear presentation of economic tests and methods of estimation is paired with practical guidance on using several types of software packages. Using real world data throughout, the authors place emphasis upon the interpretation of results, and the conclusions to be drawn from them in econometric work. This book will be essential reading for economics undergraduate and master’s students taking a course in applied econometrics. Its practical nature makes it ideal for modules requiring a research project. New to this Edition: – Additional practical exercises throughout to help consolidate understanding – A freshly-updated companion website featuring a new solutions manual for instructors

Applied Econometrics 4th Table of contents:

Part I: Statistical Background and Basic Data Handling

1. Fundamental Concepts

Introduction

A simple example

A statistical framework

Properties of the sampling distribution of the mean

Hypothesis testing and the central limit theorem

Conclusion

2. The Structure of Economic Data and Basic Data Handling

The structure of economic data

Cross-sectional data

Time series data

Panel data

Basic data handling

Looking at raw data

Graphical analysis

Summary statistics

Questions

Part II: The Classical Linear Regression Model

3. Simple Regression

Introduction to regression: the classical linear regression model (CLRM)

Why do we do regressions?

The classical linear regression model

The ordinary least squares (OLS) method of estimation

Alternative expressions for ˆB

The assumptions of the CLRM

General

The assumptions

Violations of the assumptions

Properties of the OLS estimators

Linearity

Unbiasedness

Efficiency and BLUEness

Consistency

The overall goodness of fit

Problems associated with R2

Hypothesis testing and confidence intervals

Testing the significance of the OLS coefficients

Confidence intervals

How to estimate a simple regression in EViews and Stata

Simple regression in Stata

Reading the Stata simple regression results output

Reading the EViews simple regression results output

Presentation of regression results

Economic theory applications

Application 1: the demand function

Application 2: the production function

Application 3: Okun’s law

Application 4: the Keynesian consumption function

Computer example: the Keynesian consumption function

Solution

Questions and exercises

4. Multiple Regression

Introduction

Derivation of multiple regression coefficients

The three-variable model

The k-variables case

Derivation of the coefficients with matrix algebra

The structure of the X�X and X�Y matrices

The assumptions of the multiple regression model

The variance–covariance matrix of the errors

Properties of multiple regression model OLS estimators

Linearity

Unbiasedness

Consistency

BLUEness

R2 and adjusted R2

General criteria for model selection

Multiple regression estimation in EViews and Stata

Multiple regression in EViews

Multiple regression in Stata

Reading the EViews multiple regression results output

Hypothesis testing

Testing individual coefficients

Testing linear restrictions

The F-form of the likelihood ratio test

Testing the joint significance of the Xs

F-test for overall significance in EViews

Adding or deleting explanatory variables

Omitted and redundant variables test in EViews

How to perform the Wald test in EViews

The t-test (a special case of the Wald procedure)

The Lagrange multiplier (LM) test

The LM test in EViews

Computer example: Wald, omitted and redundant variables tests

A Wald test of coefficient restrictions

A redundant variable test

An omitted variable test

Computer example: commands for Stata

Financial econometrics application: the capital asset pricing model in action

A few theoretical remarks regarding the CAPM

The empirical application of the CAPM

EViews programming and the CAPM application

Advanced EViews programming and the CAPM application

Questions and exercises

Part III: Violating the Assumptions of the CLRM

5. Multicollinearity

Learning objectives

Introduction

Perfect multicollinearity

Consequences of perfect multicollinearity

Imperfect multicollinearity

Consequences of imperfect multicollinearity

Detecting problematic multicollinearity

Simple correlation coefficient

R2 from auxiliary regressions

Computer examples

Example 1: induced multicollinearity

Example 2: with the use of real economic data

Questions and exercises

Questions

6. Heteroskedasticity

Learning objectives

Introduction: what is heteroskedasticity?

Consequences of heteroskedasticity for OLS estimators

A general approach

A mathematical approach

Detecting heteroskedasticity

The informal way

The Breusch–Pagan LM test

The Glesjer LM test

The Harvey–Godfrey LM test

The Park LM test

Criticism of the LM tests

The Goldfeld–Quandt test

White’s test

Computer example: heteroskedasticity tests

The Breusch–Pagan test

The Glesjer test

The Harvey–Godfrey test

The Park test

The Goldfeld–Quandt test

White’s test

Commands for the computer example in Stata

Engle’s ARCH test*

Computer example of the ARCH-LM test

Resolving heteroskedasticity

Generalized (or weighted) least squares

Computer example: resolving heteroskedasticity

Questions and exercises

7. Autocorrelation

Learning objectives

Introduction: what is autocorrelation?

What causes autocorrelation?

First- and higher-order autocorrelation

Consequences of autocorrelation for the OLS estimators

A general approach

A more mathematical approach

Detecting autocorrelation

The graphical method

Example: detecting autocorrelation using the graphical method

The Durbin–Watson test

Computer example of the DW test

The Breusch–Godfrey LM test for serial correlation

Computer example of the Breusch–Godfrey test

Durbin’s h-test in the presence of lagged dependent variables

Computer example of Durbin’s h-test

Resolving autocorrelation

When ? is known

Computer example of the generalized differencing approach

When ? is unknown

Computer example of the iterative procedure

Resolving autocorrelation in Stata

Questions and exercises

Appendix

8. Misspecification: Wrong Regressors, Measurement Errors and Wrong Functional Forms

Learning objectives

Introduction

Omitting influential or including non-influential explanatory variables

Consequences of omitting influential variables

Including a non-influential variable

Omission and inclusion of relevant and irrelevant variables at the same time

The plug-in solution in the omitted variable bias

Various functional forms

Introduction

Linear-log functional form

Reciprocal functional form

Polynomial functional form

Functional form including interaction terms

Log-linear functional form

The double-log functional form

The Box–Cox transformation

Measurement errors

Measurement error in the dependent variable

Measurement error in the explanatory variable

Tests for misspecification

Normality of residuals

The Ramsey RESET test for general misspecification

Tests for non-nested models

Computer example: the Box–Cox transformation in EViews

Approaches in choosing an appropriate model

The traditional view: average economic regression

The Hendry ‘general to specific approach’

Questions and exercises

Part IV: Topics in Econometrics

9. Dummy Variables

Learning objectives

Introduction: the nature of qualitative information

The use of dummy variables

Constant dummy variables

Slope dummy variables

The combined effect of intercept and slope dummies

Computer example of the use of dummy variables

Using a constant dummy

Using a slope dummy

Using both dummies together

Special cases of the use of dummy variables

Using dummy variables with multiple categories

Using more than one dummy variable

Using seasonal dummy variables

Computer example of dummy variables with multiple categories

Financial econometrics application: the January effect in emerging stock markets

Tests for structural stability

The dummy variable approach

The Chow test for structural stability

Financial econometrics application: the day-of-the-week effect in action

How to create daily dummies in Stata

Questions and exercises

10. Dynamic Econometric Models

Learning objectives

Introduction

Distributed lag models

The Koyck transformation

The Almon transformation

Other models of lag structures

Autoregressive models

The partial adjustment model

Computer example of the partial adjustment model

The adaptive expectations model

Tests of autocorrelation in autoregressive models

Exercises

11. Simultaneous Equation Models

Learning objectives

Introduction: basic definitions

Consequences of ignoring simultaneity

The identification problem

Basic definitions

Conditions for identification

Example of the identification procedure

A second example: the macroeconomic model of a closed economy

Estimation of simultaneous equation models

Estimation of an exactly identified equation: the ILS method

Estimation of an over-identified equation: the TSLS method

Computer example: the IS–LM model

Estimation of simultaneous equations in Stata

Exercises

12. Limited Dependent Variable Regression Models

Learning objectives

Introduction

The linear probability model

Problems with the linear probability model

ˆD is not bounded by the (0,1) range

Non-normality and heteroskedasticity of the disturbances

The coefficient of determination as a measure of overall fit

The logit model

A general approach

Interpretation of the estimates in logit models

Goodness of fit

A more mathematical approach

The probit model

A general approach

A more mathematical approach

Multinomial and ordered logit and probit models

Multinomial logit and probit models

Ordered logit and probit models

The Tobit model

Computer example: probit and logit models in EViews and Stata

Logit and probit models in EViews

Logit and probit models in Stata

Exercises

Part V: Time Series Econometrics

13. ARIMA Models and the Box–Jenkins Methodology

Learning objectives

An introduction to time series econometrics

ARIMA models

Stationarity

Autoregressive time series models

The AR(1) model

The AR(p) model

Properties of the AR models

Moving average models

The MA(1) model

The MA(q) model

Invertibility in MA models

Properties of the MA models

ARMA models

Integrated processes and the ARIMA models

An integrated series

Example of an ARIMA model

Box–Jenkins model selection

Identification

Estimation

Diagnostic checking

The Box–Jenkins approach step by step

Computer example: the Box–Jenkins approach

The Box–Jenkins approach in EViews

The Box–Jenkins approach in Stata

Questions and exercises

14. Modelling the Variance: ARCH–GARCH Models

Learning objectives

Introduction

The ARCH model

The ARCH(1) model

The ARCH(q) model

Testing for ARCH effects

Estimation of ARCH models by iteration

Estimating ARCH models in EViews

A more mathematical approach

The GARCH model

The GARCH(p, q) model

The GARCH(1,1) model as an infinite ARCH process

Estimating GARCH models in EViews

Alternative specifications

The GARCH in mean, or GARCH-M, model

Estimating GARCH-M models in EViews

The threshold GARCH (TGARCH) model

Estimating TGARCH models in EViews

The exponential GARCH (EGARCH) model

Estimating EGARCH models in EViews

Adding explanatory variables in the mean equation

Adding explanatory variables in the variance equation

Estimating ARCH/GARCH-type models in Stata

Advanced EViews programming for the estimation of GARCH-type models

Application: a GARCH model of UK GDP and the effect of socio-political instability

Questions and exercises

15. Vector Autoregressive (VAR) Models and Causality Tests

Learning objectives

Vector autoregressive (VAR) models

The VAR model

Pros and cons of the VAR models

Causality tests

The Granger causality test

The Sims causality test

Financial econometrics application: financial development and economic growth – what is the causal

Estimating VAR models and causality tests in EViews and Stata

Estimating VAR models in EViews

Estimating VAR models in Stata

Exercises

16. Non-Stationarity and Unit-Root Tests

Learning objectives

Introduction

Unit roots and spurious regressions

What is a unit root?

Spurious regressions

Explanation of the spurious regression problem

Testing for unit roots

Testing for the order of integration

The simple Dickey–Fuller (DF) test for unit roots

The augmented Dickey–Fuller (ADF) test for unit roots

The Phillips–Perron (PP) test

Unit-root tests in EViews and Stata

Performing unit-root tests in EViews

Performing unit-root tests in Stata

Application: unit-root tests on various macroeconomic variables

Financial econometrics application: unit-root tests for the financial development and economic growt

Questions and exercises

17. Cointegration and Error-Correction Models

Learning objectives

Introduction: what is cointegration?

Cointegration: a general approach

Cointegration: a more mathematical approach

Cointegration and the error-correction mechanism (ECM): a general approach

The problem

Cointegration (again)

The error-correction model (ECM)

Advantages of the ECM

Cointegration and the error-correction mechanism: a more mathematical approach

A simple model for only one lagged term of X and Y

A more general model for large numbers of lagged terms

Testing for cointegration

Cointegration in single equations: the Engle–Granger approach

Drawbacks of the EG approach

The EG approach in EViews and Stata

Cointegration in multiple equations and the Johansen approach

Advantages of the multiple-equation approach

The Johansen approach (again)

The steps of the Johansen approach in practice

The Johansen approach in EViews and Stata

Financial econometrics application: cointegration tests for the financial development and economic g

Monetization ratio

Turnover ratio

Claims and currency ratios

A model with more than one financial development proxy variable

Questions and exercises

18. Identification in Standard and Cointegrated Systems

Learning objectives

Introduction

Identification in the standard case

The order condition

The rank condition

Identification in cointegrated systems

A worked example

Computer example of identification

Conclusion

Questions and exercises

19. Solving Models

Learning objectives

Introduction

Solution procedures

Model add factors

Simulation and impulse responses

Stochastic model analysis

Setting up a model in EViews

Conclusion

Exercises

20. Time-Varying Coefficient Models: A New Way of Estimating Bias-Free Parameters

Learning objectives

Introduction

TVC estimation

Theorem 1

Coefficient drivers

Assumption 1 (auxiliary information)

Assumption 2

Choosing coefficient drivers

First requirement: selecting the complete driver set

Second requirement: splitting the driver set

Financial econometrics application: rating agencies’ decisions and the sovereign bond spread betwe

Conclusion

Questions and exercises

Part VI: Panel Data Econometrics

21. Traditional Panel Data Models

Learning objectives

Introduction: the advantages of panel data

The linear panel data model

Different methods of estimation

The common constant method

The fixed effects method

The random effects method

The Hausman test

Computer examples with panel data

Inserting panel data in EViews

Estimating a panel data regression in EViews

The Hausman test in EViews

Estimating a panel data regression in Stata

The Hausman test in Stata

22. Dynamic Heterogeneous Panels

Learning objectives

Introduction

Bias in dynamic panels

Bias in the simple OLS estimator

Bias in the fixed effects model

Bias in the random effects model

Solutions to the bias problem (caused by the dynamic nature of the panel)

Bias of heterogeneous slope parameters

Solutions to heterogeneity bias: alternative methods of estimation

The mean group (MG) estimator

The pooled mean group (PMG) estimator

Application: the effects of uncertainty in economic growth and investment

Evidence from traditional panel data estimation

Mean group and pooled mean group estimates

23. Non-Stationary Panels

Learning objectives

Introduction

Panel unit-root tests

The Levin and Lin (LL) test

The Im, Pesaran and Shin (IPS) test

The Maddala and Wu (MW) test

Computer examples of panel unit-root tests

Panel cointegration tests

Introduction

The Kao test

The McCoskey and Kao test

The Pedroni tests

The Larsson et al. test

Computer examples of panel cointegration tests

Part VII: Using Econometric Software

24. Practicalities of Using EViews and Stata

About EViews

Starting up with EViews

Creating a workfile and importing data

Copying and pasting data

Verifying and saving the data

Examining the data

Commands, operators and functions

About Stata

Starting up with Stata

The Stata menu and buttons

Creating a file when importing data

Copying/pasting data

Cross-sectional and time series data in Stata

First way – time series data with no time variable

Second way – time series data with time variable

Time series – daily frequency

Time series – monthly frequency

All frequencies

Saving data

Basic commands in Stata

Understanding command syntax in Stata

Appendix: Statistical Tables

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