Principles of Econometrics, 5ed, An Indian Adaptation
Description
Principles of Econometrics, Fifth Edition, is a comprehensive book tailored for undergraduate and postgraduate students in the fields of economics, commerce, business, statistics, and management. This edition equips students with a practical understanding of basic econometrics, enabling them to apply modeling, estimation, inference, and forecasting techniques to real-world economic issues. Additionally, readers will develop the ability to critically evaluate the findings of others' economic research and models, laying a solid foundation for advanced study in the field.
This Indian adaptation introduces new sections focusing on the Indian economic context, such as monetary policy, inequality of opportunity, and public health expenditure. Significant additions also include analyses of per-capita expenditure, private returns to education, and the impact of financial constraints on innovation in developing economies.
Table of Contents
1 An Introduction to Econometrics 1.1 Why Study Econometrics? 1.2 What Is Econometrics About? 1.2.1 Some Examples 1.3 The Econometric Model 1.3.1 Causality and Prediction 1.4 How Are Data Generated? 1.4.1 Experimental Data 1.4.2 Quasi-Experimental Data 1.4.3 Nonexperimental Data 1.5 Economic Data Types 1.5.1 Time-Series Data 1.5.2 Cross-Section Data 1.5.3 Panel or Longitudinal Data 1.6 The Research Process 1.7 Writing an Empirical Research Paper 1.7.1 Writing a Research Proposal 1.7.2 A Format for Writing a Research Report 1.8 Sources of Economic Data 1.8.1 Links to Economic Data on the Internet 1.8.2 Interpreting Economic Data 1.8.3 Obtaining the Data ANNEXURE 1 Important Databases in India
2 The Simple Linear Regression Model 2.1 An Economic Model 2.2 An Econometric Model 2.2.1 Data Generating Process 2.2.2 The Random Error and Strict Exogeneity 2.2.3 The Regression Function 2.2.4 Random Error Variation 2.2.5 Variation in x 2.2.6 Error Normality 2.2.7 Generalizing the Exogeneity Assumption 2.2.8 Error Correlation 2.2.9 Summarizing the Assumptions 2.3 Estimating the Regression Parameters 2.3.1 The Least Squares Principle 2.3.2 Other Economic Models 2.4 Assessing the Least Squares Estimators 2.4.1 The Estimator b2 2.4.2 The Expected Values of b1 and b2 2.4.3 Sampling Variation 2.4.4 The Variances and Covariance of b1 and b2 2.5 The Gauss–Markov Theorem 2.6 The Probability Distributions of the Least Squares Estimators 2.7 Estimating the Variance of the Error Term 2.7.1 Estimating the Variances and Covariance of the Least Squares Estimators 2.7.2 Interpreting the Standard Errors 2.8 Estimating Nonlinear Relationships 2.8.1 Quadratic Functions 2.8.2 Using a Quadratic Model 2.8.3 A Log-Linear Function 2.8.4 Using a Log-Linear Model 2.8.5 Choosing a Functional Form 2.9 Regression with Indicator Variables 2.10 The Independent Variable 2.10.1 Random and Independent x 2.10.2 Random and Strictly Exogenous x 2.10.3 Random Sampling Keywords Problems
3 Interval Estimation and Hypothesis Testing 3.1 Interval Estimation 3.1.1 The t-Distribution 3.1.2 Obtaining Interval Estimates 3.1.3 The Sampling Context 3.2 Hypothesis Tests 3.2.1 The Null Hypothesis 3.2.2 The Alternative Hypothesis 3.2.3 The Test Statistic 3.2.4 The Rejection Region 3.2.5 A Conclusion 3.3 Rejection Regions for Specific Alternatives 3.3.1 One-Tailed Tests with Alternative “Greater Than” (>) 3.3.2 One-Tailed Tests with Alternative “Less Than” (<) 3.3.3 Two-Tailed Tests with Alternative “Not Equal To” (≠) 3.4 Examples of Hypothesis Tests 3.5 The p-Value 3.6 Linear Combinations of Parameters 3.6.1 Testing a Linear Combination of Parameters Keywords Problems
4 Prediction, Goodness-of-Fit, and Modeling Issues 4.1 Least Squares Prediction 4.2 Measuring Goodness-of-Fit 4.2.1 Correlation Analysis 4.2.2 Correlation Analysis and R2 4.3 Modeling Issues 4.3.1 The Effects of Scaling the Data 4.3.2 Choosing a Functional Form 4.3.3 A Linear-Log Food Expenditure Model 4.3.4 Using Diagnostic Residual Plots 4.3.5 Are the Regression Errors Normally Distributed? 4.3.6 Identifying Influential Observations 4.4 Polynomial Models 4.4.1 Quadratic and Cubic Equations 4.5 Log-Linear Models 4.5.1 Prediction in the Log-Linear Model 4.5.2 A Generalized R2 Measure 4.5.3 Prediction Intervals in the Log-Linear Model 4.6 Log-Log Models Keywords Problems
5 The Multiple Regression Model 5.1 Introduction 5.1.1 The Economic Model 5.1.2 The Econometric Model 5.1.3 The General Model 5.1.4 Assumptions of the Multiple Regression Model 5.2 Estimating the Parameters of the Multiple Regression Model 5.2.1 Least Squares Estimation Procedure 5.2.2 Estimating the Error Variance σ2 5.2.3 Measuring Goodness-of-Fit 5.2.4 Frisch–Waugh–Lovell (FWL) Theorem 5.3 Finite Sample Properties of the Least Squares Estimator 5.3.1 The Variances and Covariances of the Least Squares Estimators 5.3.2 The Distribution of the Least Squares Estimators 5.4 Interval Estimation 5.4.1 Interval Estimation for a Single Coefficient 5.4.2 Interval Estimation for a Linear Combination of Coefficients 5.5 Hypothesis Testing 5.5.1 Testing the Significance of a Single Coefficient 5.5.2 One-Tailed Hypothesis Testing for a Single Coefficient 5.5.3 Hypothesis Testing for a Linear Combination of Coefficients 5.6 Nonlinear Relationships 5.7 Large Sample Properties of the Least Squares Estimator 5.7.1 Consistency 5.7.2 Asymptotic Normality 5.7.3 Relaxing Assumptions 5.7.4 Inference for a Nonlinear Function of Coefficients Keywords Problems
6 Further Inference in the Multiple Regression Model 6.1 Testing Joint Hypotheses: The F-Test 6.1.1 Testing the Significance of the Model 6.1.2 The Relationship Between t- and F-Tests 6.1.3 More General F-Tests 6.1.4 Using Computer Software 6.1.5 Large Sample Tests 6.2 The Use of Nonsample Information 6.3 Model Specification 6.3.1 Causality versus Prediction 6.3.2 Omitted Variables 6.3.3 Irrelevant Variables 6.3.4 Control Variables 6.3.5 Choosing a Model 6.3.6 RESET 6.4 Prediction 6.4.1 Predictive Model Selection Criteria 6.5 Poor Data, Collinearity, and Insignificance 6.5.1 The Consequences of Collinearity 6.5.2 Identifying and Mitigating Collinearity 6.5.3 Investigating Influential Observations 6.6 Nonlinear Least Squares Keywords Problems
7 Using Indicator Variables 7.1 Indicator Variables 7.1.1 Intercept Indicator Variables 7.1.2 Slope-Indicator Variables 7.2 Applying Indicator Variables 7.2.1 Interactions Between Qualitative Factors 7.2.2 Qualitative Factors with Several Categories 7.2.3 Testing the Equivalence of Two Regressions 7.2.4 Controlling for Time 7.3 Log-Linear Models 7.3.1 A Rough Calculation 7.3.2 An Exact Calculation 7.4 The Linear Probability Model 7.5 Treatment Effects 7.5.1 The Difference Estimator 7.5.2 Analysis of the Difference Estimator 7.5.3 The Differences-in-Differences Estimator 7.6 Treatment Effects and Causal Modeling 7.6.1 The Nature of Causal Effects 7.6.2 Treatment Effect Models 7.6.3 Decomposing the Treatment Effect 7.6.4 Introducing Control Variables 7.6.5 The Overlap Assumption 7.6.6 Regression Discontinuity Designs Keywords Problems
8 Heteroskedasticity 8.1 The Nature of Heteroskedasticity 8.2 Heteroskedasticity in the Multiple Regression Model 8.2.1 The Heteroskedastic Regression Model 8.2.2 Heteroskedasticity Consequences for the OLS Estimator 8.3 Heteroskedasticity Robust Variance Estimator 8.4 Generalized Least Squares: Known Form of Variance 8.4.1 Transforming the Model: Proportional Heteroskedasticity 8.4.2 Weighted Least Squares: Proportional Heteroskedasticity 8.5 Generalized Least Squares: Unknown Form of Variance 8.5.1 Estimating the Multiplicative Model 8.6 Detecting Heteroskedasticity 8.6.1 Residual Plots 8.6.2 The Goldfeld–Quandt Test 8.6.3 A General Test for Conditional Heteroskedasticity 8.6.4 The White Test 8.6.5 Model Specification and Heteroskedasticity 8.7 Heteroskedasticity in the Linear Probability Model Keywords Problems
9 Regression with Time-Series Data: Stationary Variables 9.1 Introduction 9.1.1 Modeling Dynamic Relationships 9.1.2 Autocorrelations 9.2 Stationarity and Weak Dependence 9.3 Forecasting 9.3.1 Forecast Intervals and Standard Errors 9.3.2 Assumptions for Forecasting 9.3.3 Selecting Lag Lengths 9.3.4 Testing for Granger Causality 9.4 Testing for Serially Correlated Errors 9.4.1 Checking the Correlogram of the Least Squares Residuals 9.4.2 Lagrange Multiplier Test 9.4.3 Durbin–Watson Test 9.5 Time-Series Regressions for Policy Analysis 9.5.1 Finite Distributed Lags 9.5.2 HAC Standard Errors 9.5.3 Estimation with AR(1) Errors 9.5.4 Infinite Distributed Lags Keywords Problems
10 Endogenous Regressors and Moment-Based Estimation 10.1 Least Squares Estimation with Endogenous Regressors 10.1.1 Large Sample Properties of the OLS Estimator 10.1.2 Why Least Squares Estimation Fails 10.1.3 Proving the Inconsistency of OLS 10.2 Cases in Which x and e Are Contemporaneously Correlated 10.2.1 Measurement Error 10.2.2 Simultaneous Equations Bias 10.2.3 Lagged-Dependent Variable Models with Serial Correlation 10.2.4 Omitted Variables 10.3 Estimators Based on the Method of Moments 10.3.1 Method of Moments Estimation of a Population Mean and Variance 10.3.2 Method of Moments Estimation in the Simple Regression Model 10.3.3 Instrumental Variables Estimation in the Simple Regression Model 10.3.4 The Importance of Using Strong Instruments 10.3.5 Proving the Consistency of the IV Estimator 10.3.6 IV Estimation Using Two-Stage Least Squares (2SLS) 10.3.7 Using Surplus Moment Conditions 10.3.8 Instrumental Variables Estimation in the Multiple Regression Model 10.3.9 Assessing Instrument Strength Using the First-Stage Model 10.3.10 Instrumental Variables Estimation in a General Model 10.3.11 Additional Issues When Using IV Estimation 10.4 Specification Tests 10.4.1 The Hausman Test for Endogeneity 10.4.2 The Logic of the Hausman Test 10.4.3 Testing Instrument Validity Keywords Problems
11 Simultaneous Equations Models 11.1 A Supply and Demand Model 11.2 The Reduced-Form Equations 11.3 The Failure of Least Squares Estimation 11.3.1 Proving the Failure of OLS 11.4 The Identification Problem 11.5 Two-Stage Least Squares Estimation 11.5.1 The General Two-Stage Least Squares Estimation Procedure 11.5.2 The Properties of the Two-Stage Least Squares Estimator Keywords Problems
12 Regression with Time-Series Data: Nonstationary Variables 12.1 Stationary and Nonstationary Variables 12.1.1 Trend Stationary Variables 12.1.2 The First-Order Autoregressive Model 12.1.3 Random Walk Models 12.2 Consequences of Stochastic Trends 12.3 Unit Root Tests for Stationarity 12.3.1 Unit Roots 12.3.2 Dickey–Fuller Tests 12.3.3 Dickey–Fuller Test with Intercept and No Trend 12.3.4 Dickey–Fuller Test with Intercept and Trend 12.3.5 Dickey–Fuller Test with No Intercept and No Trend 12.3.6 Order of Integration 12.3.7 Other Unit Root Tests 12.4 Cointegration 12.4.1 The Error Correction Model 12.5 Regression When There Is No Cointegration 12.6 Summary Keywords Problems
13 Vector Error Correction and Vector Autoregressive Models 13.1 VEC and VAR Models 13.2 Estimating a Vector Error Correction Model 13.3 Estimating a VAR Model 13.4 Impulse Responses and Variance Decompositions 13.4.1 Impulse Response Functions 13.4.2 Forecast Error Variance Decompositions Keywords Problems ANNEXURE 13 Local Projections for Impulse Response Functions
14 Time-Varying Volatility and ARCH Models 14.1 The ARCH Model 14.2 Time-Varying Volatility 14.3 Testing, Estimating, and Forecasting 14.4 Extensions 14.4.1 The GARCH Model—Generalized ARCH 14.4.2 Allowing for an Asymmetric Effect 14.4.3 GARCH-in-Mean and Time-Varying Risk Premium 14.4.4 Other Developments Keywords Problems
15 Panel Data Models 15.1 The Panel Data Regression Function 15.1.1 Further Discussion of Unobserved Heterogeneity 15.1.2 The Panel Data Regression Exogeneity Assumption 15.1.3 Using OLS to Estimate the Panel Data Regression 15.2 The Fixed Effects Estimator 15.2.1 The Difference Estimator: T = 2 15.2.2 The Within Estimator: T = 2 15.2.3 The Within Estimator: T > 2 15.2.4 The Least Squares Dummy Variable Model 15.3 Panel Data Regression Error Assumptions 15.3.1 OLS Estimation with Cluster-Robust Standard Errors 15.3.2 Fixed Effects Estimation with Cluster-Robust Standard Errors 15.4 The Random Effects Estimator 15.4.1 Testing for Random Effects 15.4.2 A Hausman Test for Endogeneity in the Random Effects Model 15.4.3 A Regression-Based Hausman Test 15.4.4 The Hausman–Taylor Estimator 15.4.5 Summarizing Panel Data Assumptions 15.4.6 Summarizing and Extending Panel Data Model Estimation Keywords Problems
16 Qualitative and Limited Dependent Variable Models 16.1 Introducing Models with Binary Dependent Variables 16.1.1 The Linear Probability Model 16.2 Modeling Binary Choices 16.2.1 The Probit Model for Binary Choice 16.2.2 Interpreting the Probit Model 16.2.3 Maximum Likelihood Estimation of the Probit Model 16.2.4 The Logit Model for Binary Choices 16.2.5 Wald Hypothesis Tests 16.2.6 Likelihood Ratio Hypothesis Tests 16.2.7 Robust Inference in Probit and Logit Models 16.2.8 Binary Choice Models with a Continuous Endogenous Variable 16.2.9 Binary Choice Models with a Binary Endogenous Variable 16.2.10 Binary Endogenous Explanatory Variables 16.2.11 Binary Choice Models and Panel Data 16.3 Multinomial Logit 16.3.1 Multinomial Logit Choice Probabilities 16.3.2 Maximum Likelihood Estimation 16.3.3 Multinomial Logit Postestimation Analysis 16.4 Conditional Logit 16.4.1 Conditional Logit Choice Probabilities 16.4.2 Conditional Logit Postestimation Analysis 16.5 Ordered Choice Models 16.5.1 Ordinal Probit Choice Probabilities 16.5.2 Ordered Probit Estimation and Interpretation 16.6 Models for Count Data 16.6.1 Maximum Likelihood Estimation of the Poisson Regression Model 16.6.2 Interpreting the Poisson Regression Model 16.7 Limited Dependent Variables 16.7.1 Maximum Likelihood Estimation of the Simple Linear Regression Model 16.7.2 Truncated Regression 16.7.3 Censored Samples and Regression 16.7.4 Tobit Model Interpretation 16.7.5 Sample Selection Keywords Problems ANNEXURE 16 Probit Models for Panel Data |