by RAMU RAMANATHAN

TABLE OF CONTENTS

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PART I:
BACKGROUND
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Chapter 1 Introduction
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1.1 What is Econometrics? 1.2 Basic Ingredients of an Empirical Study 1.3 Empirical Project Summary Key Terms Exercises

**Chapter 2 Review of Probability and
Statistics**

2.1 Random Variables and Probability Distributions 2.2 Mathematical Expectation, Mean, and Variance 2.3 Joint Probabilities, Covariance, and Correlation 2.4 Random Sampling and Sampling Distributions 2.5 Procedures for the Estimation of Parameters 2.6 Properties of Estimators 2.7 The Chi-square, t-, and F-distributions 2.8 Testing Hypotheses 2.9 Interval Estimation Key Terms References Practice Computer Sessions Exercises 2.A Appendix: Miscellaneous Derivations 2.A.1 Certain Useful Results on Summations 2.A.2 Multivariate Distributions 2.A.3 Maximization and Minimization 2.A.4 More on Estimation

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PART II:
BASICS
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Chapter 3 The Simple Linear Regression Model
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3.1 The Basic Model 3.2 Estimation of the Basic Model by the Method of Ordinary Least Squares (OLS) 3.3 Properties of Estimators 3.4 The Precision of the Estimators and the Goodness of Fit 3.5 Tests of Hypotheses 3.6 Scaling and Units of Measurement 3.7 Application: Estimating an Engel Curve Relation Between Expenditure on Health Care and Income 3.8 Confidence Intervals 3.9 Forecasting 3.10 Causality in a Regression Model 3.11 Application: Relation Between Patents and the Expenditures on Research and Development (R&D) Summary Key Terms References Exercises 3.A Appendix: Miscellaneous Derivations 3.A.1 Three Dimensional Representation of the Simple Linear Model 3.A.2 More Results on Summations 3.A.3 Derivation of the Normal Equations by Least Squares 3.A.4 Best Linear Unbiased Estimation (BLUE) and the Gauss-Markov Theorem 3.A.5 Maximum Likelihood Estimation 3.A.6 Derivation of the Variances of the Estimators 3.A.7 Unbiased Estimator of the Variance of the Error Term 3.A.8 Derivation of Equation 3.26 3.A.9 Derivation of Equation 3.27a 3.A.10 Proof that rsquare(x,y) = Rsquared for a Simple Regression Model 3.A.11 Derivation of Equation 3.29 3.A.12 Derivation of Equation 3.30

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Chapter 4 Multiple Regression Models
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4.1 Normal Equations 4.2 Goodness of fit 4.3 General Criteria for Model Selection 4.4 Testing Hypotheses 4.5 Specification Errors 4.6 Application: The Determinants of the Demand for Bus Travel 4.7 Application: Women's Labor Force Participation 4.8 Empirical Example: Net Migration Rates and the Quality of Life 4.9 Empirical Project Summary Key Terms References Exercises 4.A Appendix: Miscellaneous Derivations 4.A.1 The Three-Variable Regression Model 4.A.2 Bias Due to the Omission of a Relevant Variable 4.A.3 Proof of Property 4.4

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Chapter 5 Multicollinearity
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5.1 Examples of Multicollinearity 5.2 Exact Multicollinearity 5.3 Near Multicollinearity 5.4 Applications Summary Key Terms References Exercises 5.A Appendix: Derivation of Equations (5.4) through (5.6)

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PART III:
EXTENSIONS
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Chapter 6 Choosing Functional Forms and Testing for Model Specification
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6.1 Review of Exponential and Logarithmic Functions 6.2 Linear-log Relationship 6.3 Reciprocal Transformation 6.4 Polynomial Curve Fitting 6.5 Interaction Terms 6.6 Lags in Behavior (Dynamic Models) 6.7 Application: Relation Between Patents and R&D Expenses Revisited 6.8 Log-linear Relationship (or Semilog Model) 6.9 Comparison of Rsquared values between Models 6.10 The Double-log (or Log-Log) Model 6.11 Application: Estimating Elasticities of Demand for Bus Travel 6.12 Miscellaneous Other Models 6.13 The Hendry/LSE Approach of Modeling from "General to Simple" 6.14 "Simple to General" Modeling Using the Lagrange Multiplier Test 6.15 Ramsey's RESET Procedure for Regression Specification Error Summary Key Terms References Exercises 6.A Appendix: More Details on LR, Wald, and LM Tests 6.A.1 Likelihood Ratio Test 6.A.2 The Wald Test 6.A.3 The Lagrange Multiplier Test

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Chapter 7 Qualitative (or Dummy) Independent Variables
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7.1 Qualitative Variables with Two Categories only 7.2 Qualitative Variables with Many Categories 7.3 The Effect of Qualitative Variables on the Slope Term (Analysis of Covariance) 7.4 Application: Covariance Analysis of the Wage Model 7.5 Estimating Seasonal Effects 7.6 Testing for Structural Change 7.7 Empirical Example: Motor Carrier Deregulation 7.8 Application: The Demand for a Sealant Used in Construction 7.9 Empirical Project Summary Key Terms References Exercises

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PART IV:
SOME SPECIAL ISSUES WITH CROSS-SECTION AND TIME SERIES DATA
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Chapter 8 Heteroscedasticity
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8.1 Consequences of Ignoring Heteroscedasticity 8.2 Testing for Heteroscedasticity 8.3 Estimation Procedures 8.4 Application: A Model of the Expenditure on Health Care in the U.S. 8.5 Empirical Project Summary Key Terms References Exercises

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Chapter 9 Serial Correlation
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9.1 Serial Correlation of the First Order 9.2 Consequences of Ignoring Serial Correlation 9.3 Testing for First-Order Serial Correlation 9.4 Treatment of Serial Correlation 9.5 Higher Order Serial Correlation 9.6 Engle's ARCH Test 9.7 Application: Demand for Electricity Summary Key Terms References Exercises 9.A Appendix: Miscellaneous Derivations 9.A.1 Proof that the DW d is approximately 2(1-rhohat) 9.A.2 Properties of uhat sub t when it is AR(1) 9.A.3 Treatment of the First Observation under AR(1)

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Chapter 10 Distributed Lag Models
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10.1 Lagged Independent Variables 10.2 Lagged Dependent Variables 10.3 Lagged Dependent Variables and Serial Correlation 10.4 Estimation of Models with Lagged Dependent Variables 10.5 Application: A Dynamic Model of Consumption Expenditures in the United Kingdom 10.6 Application: Hourly Electricity Load Model Revisited 10.7 Unit Roots and the Dickey-Fuller Tests 10.8 Error Correction Models (ECM) 10.9 Application: An Error Correction Model of U.S. Defense Expenditures 10.10 Cointegration 10.11 Causality 10.12 Pooling Cross Section and Time Series Data (or Panel Data) 10.13 Empirical Project Summary Key Terms References Exercises

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PART V:
SPECIAL TOPICS
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Chapter 11 Forecasting
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11.1 Fitted Values, Ex-post, and Ex-ante Forecasts 11.2 Evaluation of Models 11.3 Conditional and Unconditional Forecasts 11.4 Forecasting from Time Trends 11.5 Combining Forecasts 11.6 Forecasting from Econometric Models 11.7 Forecasting from Time Series Models Summary Key Terms References Exercises

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Chapter 12 Qualitative and Limited Dependent Variables
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12.1 Linear Probability (or Binary Choice) Models 12.2 The Probit Model 12.3 The Logit Model 12.4 Limited Dependent Variables Summary Key Terms References Exercises

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Chapter 13 Simultaneous Equation Models
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13.1 Structure and Reduced Forms of Simultaneous Equation Models 13.2 Consequences of Ignoring Simultaneity 13.3 The Identification Problem 13.4 Estimation Procedures 13.5 Empirical Example: Regulation in the Contact Lens Industry 13.6 Application: A Simple Keynesian Model Summary Key Terms References Exercises 13.A Appendix: Derivation of the Limits for OLS Estimates

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PART VI:
PRACTICE
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Chapter 14 Carrying out an Empirical Project
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14.1 Selecting a Topic 14.2 Review of Literature 14.3 Formulating a General Model 14.4 Collecting the Data 14.5 Empirical Analysis Keyterms

Appendix AStatistical TablesAppendix BAnswers to Selected ExercisesAppendix CPractice Computer SessionsAppendix DDescriptions of the Data