A Guide to Modern Econometrics 1st edition by Marno Verbeek ISBN 1119401117 978-1119401117

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ISBN 10: 1119401117
ISBN 13: 978-1119401117
Author: Marno Verbeek 

A Guide to Modern Econometrics, 5th Edition has become established as a highly successful textbook. It serves as a guide to alternative techniques in econometrics with an emphasis on intuition and the practical implementation of these approaches. This fifth edition builds upon the success of its predecessors. The text has been carefully checked and updated, taking into account recent developments and insights. It includes new material on causal inference, the use and limitation of p-values, instrumental variables estimation and its implementation, regression discontinuity design, standardized coefficients, and the presentation of estimation results.

A Guide to Modern Econometrics 1st Table of contents:

1 Introduction

• About Econometrics

• The Structure of This Book

• Illustrations and Exercises

2 An Introduction to Linear Regression

• Ordinary Least Squares as an Algebraic Tool

  • Ordinary Least Squares

  • Simple Linear Regression

  • Example: Individual Wages

  • Matrix Notation

• The Linear Regression Model

• Small Sample Properties of the OLS Estimator

  • The Gauss–Markov Assumptions

  • Properties of the OLS Estimator

  • Example: Individual Wages (Continued)

• Goodness-of-Fit

• Hypothesis Testing

  • A Simple t-Test

  • Example: Individual Wages (Continued)

  • Testing One Linear Restriction

  • A Joint Test of Significance of Regression Coefficients

  • Example: Individual Wages (Continued)

  • The General Case

  • Size, Power and p-Values

  • Reporting Regression Results

• Asymptotic Properties of the OLS Estimator

  • Consistency

  • Asymptotic Normality

  • Small Samples and Asymptotic Theory

• Illustration: The Capital Asset Pricing Model

  • The CAPM as a Regression Model

  • Estimating and Testing the CAPM

  • The World’s Largest Hedge Fund

• Multicollinearity

  • Example: Individual Wages (Continued)

• Missing Data, Outliers and Influential Observations

  • Outliers and Influential Observations

  • Robust Estimation Methods

  • Missing Observations

• Prediction

• Wrap-up

• Exercises

3 Interpreting and Comparing Regression Models

• Interpreting the Linear Model

• Selecting the Set of Regressors

  • Misspecifying the Set of Regressors

  • Selecting Regressors

  • Comparing Non-nested Models

• Misspecifying the Functional Form

  • Nonlinear Models

  • Testing the Functional Form

  • Testing for a Structural Break

• Illustration: Explaining House Prices

• Illustration: Predicting Stock Index Returns

  • Model Selection

  • Forecast Evaluation

• Illustration: Explaining Individual Wages

  • Linear Models

  • Loglinear Models

  • The Effects of Gender

  • Some Words of Warning

• Wrap-up

• Exercises

4 Heteroskedasticity and Autocorrelation

• Consequences for the OLS Estimator

• Deriving an Alternative Estimator

• Heteroskedasticity

  • Introduction

  • Estimator Properties and Hypothesis Testing

  • When the Variances Are Unknown

  • Heteroskedasticity-consistent Standard Errors for OLS

  • Multiplicative Heteroskedasticity

  • Weighted Least Squares with Arbitrary Weights

• Testing for Heteroskedasticity

  • Testing for Multiplicative Heteroskedasticity

  • The Breusch–Pagan Test

  • The White Test

  • Which Test?

• Illustration: Explaining Labour Demand

• Autocorrelation

  • First-order Autocorrelation

  • Unknown 𝜌

• Testing for First-order Autocorrelation

  • Asymptotic Tests

  • The Durbin–Watson Test

• Illustration: The Demand for Ice Cream

• Alternative Autocorrelation Patterns

  • Higher-order Autocorrelation

  • Moving Average Errors

• What to Do When You Find Autocorrelation?

  • Misspecification

  • Heteroskedasticity-and-autocorrelation-consistent Standard Errors for OLS

• Illustration: Risk Premia in Foreign Exchange Markets

  • Notation

  • Tests for Risk Premia in the 1-Month Market

  • Tests for Risk Premia Using Overlapping Samples

• Wrap-up

• Exercises

5 Endogenous Regressors, Instrumental Variables and GMM

• A Review of the Properties of the OLS Estimator

• Cases Where the OLS Estimator Cannot Be Saved

  • Autocorrelation with a Lagged Dependent Variable

  • Measurement Error in an Explanatory Variable

  • Endogeneity and Omitted Variable Bias

  • Simultaneity and Reverse Causality

• The Instrumental Variables Estimator

  • Estimation with a Single Endogenous Regressor and a Single Instrument

  • Back to the Keynesian Model

  • Back to the Measurement Error Problem

  • Multiple Endogenous Regressors

• Illustration: Estimating the Returns to Schooling

• Alternative Approaches to Estimate Causal Effects

• The Generalized Instrumental Variables Estimator

  • Multiple Endogenous Regressors with an Arbitrary Number of Instruments

  • Two-stage Least Squares and the Keynesian Model Again

  • Specification Tests

  • Weak Instruments

  • Implementing and Reporting Instrumental Variables Estimators

• Institutions and Economic Development

• The Generalized Method of Moments

  • Example

  • The Generalized Method of Moments

  • Some Simple Examples

  • Weak Identification

• Illustration: Estimating Intertemporal Asset Pricing Models

• Wrap-up

• Exercises

6 Maximum Likelihood Estimation and Specification Tests

• An Introduction to Maximum Likelihood

  • Some Examples

  • General Properties

  • An Example (Continued)

  • The Normal Linear Regression Model

  • The Stochastic Frontier Model

• Specification Tests

  • Three Test Principles

  • Lagrange Multiplier Tests

  • An Example (Continued)

• Tests in the Normal Linear Regression Model

  • Testing for Omitted Variables

  • Testing for Heteroskedasticity

  • Testing for Autocorrelation

• Quasi-maximum Likelihood and Moment Conditions Tests

  • Quasi-maximum Likelihood

  • Conditional Moment Tests

  • Testing for Normality

• Wrap-up

• Exercises

7 Models with Limited Dependent Variables

• Binary Choice Models

  • Using Linear Regression?

  • Introducing Binary Choice Models

  • An Underlying Latent Model

  • Estimation

  • Goodness-of-Fit

  • Illustration: The Impact of Unemployment Benefits on Recipiency

  • Specification Tests in Binary Choice Models

  • Relaxing Some Assumptions in Binary Choice Models

• Multiresponse Models

  • Ordered Response Models

  • About Normalization

  • Illustration: Explaining Firms’ Credit Ratings

  • Illustration: Willingness to Pay for Natural Areas

  • Multinomial Models

• Models for Count Data

  • The Poisson and Negative Binomial Models

  • Illustration: Patents and R&D Expenditures

• Tobit Models

  • The Standard Tobit Model

  • Estimation

  • Illustration: Expenditures on Alcohol and Tobacco (Part 1)

  • Specification Tests in the Tobit Model

• Extensions of Tobit Models

  • The Tobit II Model

  • Estimation

  • Further Extensions

  • Illustration: Expenditures on Alcohol and Tobacco (Part 2)

• Sample Selection Bias

  • The Nature of the Selection Problem

  • Semi-parametric Estimation of the Sample Selection Model

• Estimating Treatment Effects

  • Regression-based Estimators

  • Regression Discontinuity Design

  • Weighting and Matching

• Duration Models

  • Hazard Rates and Survival Functions

  • Samples and Model Estimation

  • Illustration: Duration of Bank Relationships

• Wrap-up

• Exercises

8 Univariate Time Series Models

• Introduction

  • Some Examples

  • Stationarity and the Autocorrelation Function

• General ARMA Processes

  • Formulating ARMA Processes

  • Invertibility of Lag Polynomials

  • Common Roots

• Stationarity and Unit Roots

• Testing for Unit Roots

  • Testing for Unit Roots in a First-order Autoregressive Model

  • Testing for Unit Roots in Higher-Order Autoregressive Models

  • Extensions

  • Illustration: Stock Prices and Earnings

• Illustration: Long-run Purchasing Power Parity (Part 1)

• Estimation of ARMA Models

  • Least Squares

  • Maximum Likelihood

• Choosing a Model

  • The Autocorrelation Function

  • The Partial Autocorrelation Function

  • Diagnostic Checking

  • Criteria for Model Selection

• Illustration: The Persistence of Inflation

• Forecasting with ARMA Models

  • The Optimal Forecast

  • Forecast Accuracy

  • Evaluating Forecasts

• Illustration: The Expectations Theory of the Term Structure

• Autoregressive Conditional Heteroskedasticity

  • ARCH and GARCH Models

  • Estimation and Prediction

  • Illustration: Volatility in Daily Exchange Rates

• What about Multivariate Models?

• Wrap-up

• Exercises

9 Multivariate Time Series Models

• Dynamic Models with Stationary Variables

• Models with Nonstationary Variables

  • Spurious Regressions

  • Cointegration

  • Cointegration and Error-correction Mechanisms

• Illustration: Long-run Purchasing Power Parity (Part 2)

• Vector Autoregressive Models

• Cointegration: the Multivariate Case

  • Cointegration in a VAR

  • Example: Cointegration in a Bivariate VAR

  • Testing for Cointegration

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