INTRODUCTORY ECONOMETRICS WITH APPLICATIONS, 5ed
by RAMU RAMANATHAN

PART I: BACKGROUND

Chapter 1 Introduction

```   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
```

PART II: BASICS

Chapter 3 The Simple Linear Regression Model

```      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
```

Chapter 4 Multiple Regression Models

```      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
```

Chapter 5 Multicollinearity

```      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)
```

PART III: EXTENSIONS

Chapter 6 Choosing Functional Forms and Testing for Model Specification

```      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
```

Chapter 7 Qualitative (or Dummy) Independent Variables

```      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
```

PART IV: SOME SPECIAL ISSUES WITH CROSS-SECTION AND TIME SERIES DATA

Chapter 8 Heteroscedasticity

```      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
```

Chapter 9 Serial Correlation

```      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)
```

Chapter 10 Distributed Lag Models

```      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
```

PART V: SPECIAL TOPICS

Chapter 11 Forecasting

```      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
```

Chapter 12 Qualitative and Limited Dependent Variables

```      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
```

Chapter 13 Simultaneous Equation Models

```      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
```

PART VI: PRACTICE

Chapter 14 Carrying out an Empirical Project

```      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 A
Statistical Tables

Appendix B