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Introductory Econometrics with Applications

Опубликовано на портале: 30-07-2004
Fort Worth, TX: Harcount College Publishers, 2002
Тематические разделы:
Introductory Econometrics with Applications, Fifth Edition, by Ramu Ramanathan, provides the perfect blend between econometric theory and hands-on practical training for a B.A., M.A., or M.B.A. course on econometrics that does not use matrix algebra.
The book is self-contained, 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 real-world examples walk the students through model specification, estimation, and hypothesis testing, using a logical step-by-step 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 time-consuming 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 real-world 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 well-known 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 open-source econometrics software package, GRETL (Gnu Regression, Econometric, and Time-series 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) walk-through practical applications that show exactly how econometric methodology is applied to real-world 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.
Chapter 1 now includes a discussion of different types of data: experimental, observational, and sample survey. Because most of econometrics deals with modeling one or more variables for given values of other variables that influence the former, the theory part of Chapters 2 and 3 have undergone the most changes. In Chapter 2, the section on joint probabilities has an extensive discussion of conditional probability, conditional expectation, and conditional variance. Properties involving multivariate distributions have been moved from the appendix to the chapter, but these sections are marked with an asterisk so that they can be skipped, if desired, without loss of continuity. In the theoretical part of Chapter 3, all the assumptions on the error terms have been stated as conditional on given X. Proofs of unbiasedness and the Gauss-Markov Theorem are initially done with X given and then extended to the case where it is a random variable. Chapter 4 has a new section on generating the forecast and confidence interval for the dependent variable in a multiple regression model. Also added here is a section on interpreting regression coefficients, especially those for variables measured as percentages or proportions. This emphasis on the proper interpretation of the results has been re-inforced and extended in Chapter 6 with a discussion and a table summarizing the interpretation of regression coefficients in models involving logarithms of variables. Chapter 7 presents modified interpretation of dummy coefficients in the case of log-linear models. Chapter 10 has also undergone substantial changes with new sections on Vector Autoregressive (VAR) models and the treatment of panel data. Chapter 11 extends the forecast combination section to include serial correlation, ARCH effects, and time-varying weights. In Chapter 14 on the steps for carrying out empirical projects, there is now an extensive discussion of how index numbers are computed and the importance of dealin.

Contents

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 Answers to Selected Exercises
Appendix C Practice Computer Sessions
Appendix D Descriptions of the Data