Introductory Econometrics with Applications
Introductory Econometrics with Applications, Fifth Edition, by Ramu Ramanathan, provides
the perfect blend between econometric theory and handson practical training for
a B.A., M.A., or M.B.A. course on econometrics that does not use matrix algebra.
The book is selfcontained, with background information on mathematics, probability, and statistics, covered in Chapter 2 and later chapters so that students need not refer to their old notes or textbooks. Practical applications are emphasized without sacrificing theoretical underpinnings. Numerous realworld examples walk the students through model specification, estimation, and hypothesis testing, using a logical stepbystep approach. Instead of showing only final polished results, the book shows intermediate failures and unexpected results, along with suggestions about how model specification and estimation can be improved by using diagnostic testing. Annotated computer outputs are included in the text so that students can see exactly how econometric techniques are implemented in practice. An entire chapter is devoted to the various steps involved in carrying out an empirical research project, namely, selection of a topic, literature review, model formulation, data gathering, estimation, hypothesis testing, and report writing. Because an empirical study is timeconsuming and a student need not wait to read the whole book to start the project, suggestions are given in selected chapters so that the process is smooth and orderly and does not cause unnecessary delays. 94 data sets on realworld topics are available in the disk accompanying the text as well as on the Web page set up for the book. The data are in ASCII, B34S, EVIEWS, EXCEL, GRETL, PcGive, and SHAZAM formats so that a user can access them easily from a variety of wellknown regression programs to reproduce the examples in the book and to carry out additional analyses. Professor Allin Cottrell of Wake Forest University has graciously agreed to include a free opensource econometrics software package, GRETL (Gnu Regression, Econometric, and Timeseries Library), in the disk that accompanies each copy of the book. Appendix C has more information on this program. PREFACE
It is indeed quite flattering that this book is in its fifth edition and for that
I am grateful to the numerous professors, both in and outside the U.S., who have
found the balance between the theoretical foundations of econometrics and practical
applications presented in this book appropriate for their courses. This edition continues
the tradition established in the previous editions, namely, (1) a clear specification
of the theoretical assumptions, with proofs of properties mostly in appendices, (2)
detailed steps for the estimation of models and for diagnostic testing to improve
model specification and/or the estimation procedures, (3) walkthrough practical
applications that show exactly how econometric methodology is applied to realworld
data, and (4) intuitive interpretation of the results. Other salient features of
the book are in the link About the book and need not be repeated here.

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 Chisquare, t, and Fdistributions
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 GaussMarkov 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 ThreeVariable 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 Linearlog 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 Loglinear Relationship (or Semilog Model)
6.9 Comparison of Rsquared values between Models
6.10 The Doublelog (or LogLog) 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 CROSSSECTION 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 FirstOrder 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(1rhohat)
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 DickeyFuller 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, Expost, and Exante 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
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 B Answers to Selected Exercises
Appendix C Practice Computer Sessions
Appendix D Descriptions of the Data
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