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Basic Econometrics

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ISBN-10: 0070252149

ISBN-13: 9780070252141

Edition: 3rd 1995

Authors: Damodar N. Gujarati

List price: $101.56
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Description:

Gujarati's Basic Econometrics provides an elementary but comprehensive introduction to econometrics without resorting to matrix algebra, calculus, or statistics beyond the elementary level. Because of the way the book is organized, it may be used at a variety of levels of rigor. For example, if matrix algebra is used, theoretical exercises may be omitted. A CD of data sets is provided with the text.
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Book details

List price: $101.56
Edition: 3rd
Copyright year: 1995
Publisher: McGraw-Hill Higher Education
Binding: Hardcover
Pages: 768
Size: 6.76" wide x 9.56" long x 1.48" tall
Weight: 2.794
Language: English

Preface
Introduction
Single-Equation Regression Models
The Nature of Regression Analysis
Historical Origin of the Term "Regression"
The Modern Interpretation of Regression
Examples
Statistical vs. Deterministic Relationships
Regression vs. Causation
Regression vs. Correlation
Terminology and Notation
The Nature and Sources of Data for Econometric Analysis
Types of Data
The Sources of Data
The Accuracy of Data
Summary and Conclusions
Exercises
Appendix 1A
Sources of Economic Data
Sources of Financial Data
Two-Variable Regression Analysis: Some Basic Ideas
A Hypothetical Example
The Concept of Population Regression Function (PRF)
The Meaning of the Term "Linear"
Linearity in the Variables
Linearity in the Parameters
Stochastic Specification of PRF
The Significance of the Stochastic Disturbance Term
The Sample Regression Function (SRF)
Summary and Conclusions
Exercises
Two-Variable Regression Model: The Problem of Estimation
The Method of Ordinary Least Squares
The Classical Linear Regression Model: The Assumptions Underlying the Method of Least Squares
How Realistic Are These Assumptions?
Precision or Standard Errors of Least-Squares Estimates
Properties of Least-Squares Estimators: The Gauss-Markov Theorem
The Coefficient of Determination r2: A Measure of "Goodness of Fit"
A Numerical Example
Illustrative Examples
Coffee Consumption in the United States, 1970-1980
Keynesian Consumption Function for the United States, 1980-1991
Computer Output for the Coffee Demand Function
A Note on Monte Carlo Experiments
Summary and Conclusions
Exercises
Questions
Problems
Appendix 3A
Derivation of Least-Squares Estimates
Linearity and Unbiasedness Properties of Least-Squares Estimators
Variances and Standard Errors of Least-Squares Estimators
Covariance between B1 and B2
The Least-Squares Estimator of o2
Minimum-Variance Property of Least-Squares Estimators
SAS Output of the Coffee Demand Function (3.7.1)
The Normality Assumption: Classical Normal Linear Regression Model (CNLRM)
The Probability Distribution of Disturbances ui
The Normality Assumption
Properties of OLS Estimators under the Normality Assumption
The Method of Maximum Likelihood (ML)
Probability Distributions Related to the Normal Distribution: The t, Chi-square (X2), and F Distributions
Summary and Conclusions
Appendix 4A
Maximum Likelihood Estimation of Two-Variable Regression Model
Maximum Likelihood Estimation of the Consumption-Income Example
Appendix 4A Exercises
Two-Variable Regression: Interval Estimation and Hypothesis Testing
Statistical Prerequisites
Interval Estimation: Some Basic Ideas
Confidence Intervals for Regression Coefficients B1 and B2
Confidence Interval for B2
Confidence Interval for B1
Confidence Interval for B1 and B2 Simultaneously
Confidence Interval for o2
Hypothesis Testing: General Comments
Hypothesis Testing: The Confidence-Interval Approach
Two-Sided or Two-Tail Test
One-Sided or One-Tail Test
Hypothesis Testing: The Test-of-Significance Approach
Testing the Significance of Regression Coefficients: The t-Test
Testing the Significance of o2: the X2 Test
Hypothesis Testing: Some Practical Aspects
The Meaning of "Accepting" or "Rejecting" a Hypothesis
The "Zero" Null Hypothesis and the "2-t" Rule of Thumb
Forming the Null and Alternative Hypotheses
Choosing a, the Level of Significance
The Exact Level of Significance: The p Value
Statistical Significance versus Practical Significance
The Choice between Confidence-Interval and Test-of-Significance Approaches to Hypothesis Testing
Regression Analysis and Analysis of Variance
Application of Regression Analysis: The Problem of Prediction
Mean Prediction
Individual Prediction
Reporting the Results of Regression Analysis
Evaluating the Results of Regression Analysis
Normality Test
Other Tests of Model Adequacy
Summary and Conclusions
Exercises
Questions
Problems
Appendix 5A
Derivation of Equation (5.3.2)
Derivation of Equation (5.9.1)
Derivation of Equations (5.10.2) and (5.10.6)
Variance of Mean Prediction
Variance of Individual Prediction
Extensions of the Two-Variable Linear Regression Model
Regression through the Origin
r2 for Regression-through-Origin Model An Illustrative Example: The Characteristic Line of Portfolio Theory
Scaling and Units of Measurement
A Numerical Example: The Relationship between GPDI and GNP, United States, 1974-1983
A Word about Interpretation
Functional Forms of Regression Models
How to Measure Elasticity: The Log-Linear Model
An Illustrative Example: The Coffee Demand Function Revisited
Semilog Models: Log-Lin and Lin-Log Models
How to Measure the Growth Rate: The Log-Lin Model
The Lin-Log Model
Reciprocal Models
An Illustrative Example: The Phillips Curve for the United Kingdom, 1950-1966
Summary of Functional Forms
A Note on the Nature of the Stochastic Error Term: Additive versus Multiplicative Stochastic Error Term
Summary and Conclusions
Exercises
Questions
Problems
Appendix 6A
Derivation of Least-Squares Estimators for Regression through the Origin
SAS Output of the Characteristic Line (6.1.12)
SAS Output of the United Kingdom Phillips Curve Regression (6.6.2)
Multiple Regression Analysis: The Problem of Estimation
The Three-Variable Model: Notation and Assumptions
Interpretation of Multiple Regression Equation
The Meaning of Partial Regression Coefficients
OLS and ML Estimation of the Partial Regression Coefficients
OLS Estimators
Variances and Standard Errors of OLS Estimators
Properties of OLS Estimators
Maximum Likelihood Estimators
The Multiple Coefficient of Determination R2 and the Multiple Coefficient of Correlation R
Example 7.1: The Expectations-Augmented Phillips Curve for the United States, 1970-1982
Simple Regression in the Context of Multiple Regression: Introduction to Specification Bias
R2 and the Adjusted R2
Comparing Two R2 Values
Example 7.2: Coffee Demand Function Revisited
The "Game" of Maximizing R2
Partial Correlation Coefficients
Explanation of Simple and Partial Correlation Coefficients
Interpretation of Simple and Partial Correlation Coefficients
Example 7.3: The Cobb-Douglas Production Function: More on Functional Form
Polynomial Regression Models
Example 7.4: Estimating the Total Cost Function
Empirical Results
Summary and Conclusions
Exercises
Questions
Problems
Appendix 7A
Derivation of OLS Estimators Given in Equations (7.4.3) and (7.4.5)
Equality between a1 of (7.3.5) and B2 of (7.4.7)
Derivation of Equation (7.4.19)
Maximum Likelihood Estimation of the Multiple Regression Model
The Proof that E(b12) = B2 + B3b32 (Equation 7.7.4)
SAS Output of the Expectations-Augmented Phillips Curve (7.6.2)
SAS Output of the Cobb-Douglas Production Function (7.10.4)
Multiple Regression Analysis: The Problem of Inference
The Normality Assumption Once Again
Example 8.1: U.S. Personal Consumption and Personal Disposal Income Relation, 1956-1970
Hypothesis Testing in Multiple Regression: General Comments
Hypothesis Testing about Individual Partial Regression Coefficients
Testing the Overall Significance of the Sample Regression
The Analysis of Variance Approach to Testing the Overall Significance of an Observed Multiple Regression: The F Test
An Important Relationship between R2 and F
The "Incremental," or "Marginal," Contribution of an Explanatory Variable
Testing the Equality of Two Regression Coefficients
Example 8.2: The Cubic Cost Function Revisited
Restricted Least Squares: Testing Linear Equality Restrictions
The t Test Approach
The F Test Approach: Restricted Least Squares
Example 8.3: The Cobb-Douglas Production Function for Taiwanese Agricultural Sector, 1958-1972
General F Testing
Comparing Two Regressions: Testing for Structural Stability of Regression Models
Testing the Functional Form of Regression: Choosing between Linear and Log-Linear Regression Models
Example 8.5: The Demand for Roses
Prediction with Multiple Regression
The Troika of Hypothesis Tests: The Likelihood Ratio (LR), Wald (W), and Lagrange Multiplier (LM) Tests
Summary and Conclusions
The Road Ahead
Exercises
Questions
Problems
Appendix 8A
Likelihood Ratio (LR) Test
The Matrix Approach to Linear Regression Model
The k-Variable Linear Regression Model
Assumptions of the Classical Linear Regression Model in Matrix Notation
OLS Estimation
An Illustration
Variance-Covariance Matrix of B
Properties of OLS Vector B
The Coefficient of Determination R2 in Matrix Notation
The Correlation Matrix
Hypothesis Testing about Individual Regression Coefficients in Matrix Notation
Testing the Overall Significance of Regression: Analysis of Variance in Matrix Notation
Testing Linear Restrictions: General F Testing Using Matrix Notation
Prediction Using Multiple Regression: Matrix Formulation
Mean Prediction
Individual Prediction
Variance of Mean Prediction
Variance of Individual Prediction
Summary of the Matrix Approach: An Illustrative Example
Summary and Conclusions
Exercises
Appendix 9A
Derivation of k Normal or Simultaneous Equations
Matrix Derivation of Normal Equations
Variance-Covariance Matrix of B
Blue Property of OLS Estimators
Relaxing the Assumptions of the Classical Model
Multicollinearity and Micronumerosity
The Nature of Multicollinearity
Estimation in the Presence of Perfect Multicollinearity
Estimation in the Presence of "High" but "Imperfect" Multicollinearity
Multicollinearity: Much Ado about Nothing? Theoretical Consequences of Multicollinearity
Practical Consequences of Multicollinearity
Large Variances and Covariances of OLS Estimators
Wider Confidence Intervals
"Insignificant" t Ratios
A High R2 but Few Significant t Ratios
Sensitivity of OLS Estimators and Their Standard Errors to Small Changes in Data
Consequences of Micronumerosity
An Illustrative Example: Consumption Expenditure in Relation to Income and Wealth
Detection of Multicollinearity
Remedial Measures
Is Multicollinearity Necessarily Bad? Maybe Not If the Objective Is Prediction Only
Summary and Conclusions
Exercises
Questions
Problems
Heteroscedasticity
The Nature of Heteroscedasticity
OLS Estimation in the Presence of Heteroscedasticity
The Method of Generalized Least Squares (GLS)
Difference between OLS and GLS
Consequences of Using OLS in the Presence of Heteroscedasticity
OLS Estimation Allowing for Heteroscedasticity
OLS Estimation Disregarding Heteroscedasticity
Detection of Heteroscedasticity
Informal Methods
Formal Methods
Remedial Measures
When oi2 Is Known: The Method of Weighted Least Squares
When o12 Is Not Known
A Concluding Example
Summary and Conclusions
Exercises
Questions
Problems
Appendix 11A
Proof of Equation (11.2.2)
The Method of Weighted Least Squares
Autocorrelation
The Nature of the Problem
OLS Estimation in the Presence of Autocorrelation
The BLUE Estimator in the Presence of Autocorrelation
Consequences of Using OLS in the Presence of Autocorrelation
OLS Estimation Allowing for Autocorrelation
OLS Estimation Disregarding Autocorrelation
Detecting Autocorrelation
Graphical Method
The Runs Test
Durbin-Watson d Test
Additional Tests of Autocorrelation
Remedial Measures
When the Structure of Autocorrelation Is Known
When p Is Not Known
An Illustrative Example: The Relationship between Help-Wanted Index and the Unemployment Rate, United States: Comparison of the Methods
Autoregressive Conditional Heteroscedasticity (ARCH) Model
What to Do If ARCH Is Present?
A Word on the Durbin-Watson d Statistic and the ARCH Effect
Summary and Conclusions
Exercises
Questions
Problems
Appendix 12A
TSP Output of United States Wages (Y)-Productivity (X) Regression, 1960-1991
Econometric Modeling I: Traditional Econometric Methodology
The Traditional View of Econometric Modeling: Average Economic Regression (AER)
Types of Specification Errors
Consequences of Specification Errors
Omitting a Relevant Variable (Underfitting a Model)
Inclusion of an Irrelevant Variable (Overfitting a Model)
Tests of Specification Errors
Detecting the Presence of Unnecessary Variables
Tests for Omitted Variables and Incorrect Functional Form
Errors of Measurement
Errors of Measurement in the Dependent Variable Y
Errors of Measurement in the Explanatory Variable X
An Example
Measurement Errors in the Dependent Variable Y Only
Errors of Measurement in X
Summary and Conclusions
Exercises
Questions
Problems
Appendix 13A
The Consequences of Including an Irrelevant Variable: The Unbiasedness Property
Proof of (13.5.10)
Econometric Modeling II: Alternative Econometric Methodologies
Learner's Approach to Model Selection
Hendry's Approach to Model Selection
Selected Diagnostic Tests: General Comments
Tests of Nonnested Hypothesis
The Discrimination Approach
The Discerning Approach
Summary and Conclusions
Exercises
Questions
Problems
Topics in Econometrics
Regression on Dummy Variables
The Nature of Dummy Variables
Example 15.1: Professor's Salary by Sex
Regression on One Quantitative Variable and One Qualitative Variable with Two Classes, or Categories
Example 15.2: Are Inventories Sensitive to Interest Rates?
Regression on One Quantitative Variable and One Qualitative Variable with More than Two Classes
Regression on One Quantitative Variable and Two Qualitative Variables
Example 15.3: The Economics of "Moonlighting"
Testing for Structural Stability of Regression Models: Basic Ideas
Example 15.4: Savings and Income, United Kingdom, 1946-1963
Comparing Two Regressions: The Dummy Variable Approach
Comparing Two Regressions: Further Illustration
Example 15.5: The Behavior of Unemployment and Unfilled Vacancies: Great Britain, 1958-1971
Interaction Effects
The Use of Dummy Variables in Seasonal Analysis
Example 15.6: Profits-Sales Behavior in U.S. Manufacturing
Piecewise Linear Regression
Example 15.7: Total Cost in Relation to Output
The Use of Dummy Variables in Combining Time Series and Cross-Sectional Data
Pooled Regression: Pooling Time Series and Cross-Sectional Data
Example 15.8: Investment Functions for General Motors and Westinghouse Companies
Some Technical Aspects of Dummy Variable Technique
The Interpretation of Dummy Variables in Semilogarithmic Regressions
Example 15.9: Semilogarithmic Regression with Dummy Variable
Another Method of Avoiding the Dummy Variable Trap
Dummy Variables and Heteroscedasticity
Dummy Variables and Autocorrelation
Topics for Further Study
Summary and Conclusions
Exercises
Questions
Problems
Appendix 15A
Data Matrix for Regression (15.8.2)
Data Matrix for Regression (15.10.2)
Regression on Dummy Dependent Variable: The LPM, Logit, Probit, and Tobit Models
Dummy Dependent Variable
The Linear Probability Model (LPM)
Problems in Estimation of LPM
Nonnormality of the Disturbances ui
Heteroscedastic Variances of the Disturbances
Nonfulfillment of 0 [= E(Yi\X) [= 1
Questionable Value of R2 as a Measure of Goodness of Fit
LPM: A Numerical Example
Applications of LPM
Example 16.1: Cohen-Rea-Lerman study
Example 16.2: Predicting a Bond Rating
Example 16.3: Predicting Bond Defaults
Alternatives to LPM
The Logit Model
Estimation of the Logit Model
The Logit Model: A Numerical Example
The Logit Model: Illustrative Examples
Example 16.4: "An Application of Logit Analysis to Prediction of Merger Targets"
Example 16.5: Predicting a Bond Rating
The Probit Model
The Probit Model: A Numerical Example
Logit versus Probit
Comparing Logit and Probit Estimates
The Marginal Effect of a Unit Change in the Value of a Regressor
The Probit Model: Example 16.5
The Tobit Model
Summary and Conclusions
Exercises
Questions
Problems
Dynamic Econometric Model: Autoregressive and Distributed-Lag Models
The Role of "Time," or "Lag," in Economics
The Reasons for Lags
Estimation of Distributed-Lag Models
Ad Hoc Estimation of Distributed-Lag Models
The Koyck Approach to Distributed-Lag Models
The Median Lag
The Mean Lag
Rationalization of the Koyck Model: The Adaptive Expectations Model
Another Rationalization of the Koyck Model: The Stock Adjustment, or Partial Adjustment, Model
Combination of Adaptive Expectations and Partial Adjustment Models
Estimation of Autoregressive Models
The Method of Instrumental Variables (IV)
Detecting Autocorrelation in Autoregressive Models: Durbin h Test
A Numerical Example: The Demand for Money in India
Illustrative Examples
Example 17.7: The Fed and the Real Rate of Interest
Example 17.8: The Short- and Long-Run Aggregate Consumption Functions for the United States, 1946-1972
The Almon Approach to Distributed-Lag Models: The Almon or Polynomial Distributed Lag (PDL)
Causality in Economics: The Granger Test
The Granger Test
Empirical Results
Summary and Conclusions
Exercises
Questions
Problems
Simultaneous-Equation Models
Simultaneous-Equation Models
The Nature of Simultaneous-Equation Models
Examples of Simultaneous-Equation Models
Example 18.1: Demand-and-Supply Model
Example 18.2: Keynesian Model of Income Determination
Example 18.3: Wage-Price Models
Example 18.4: The IS Model of Macroeconomics
Example 18.5: The LM Model
Example 18.6: Econometric Models
The Simultaneous-Equation Bias: Inconsistency of OLS Estimators
The Simultaneous-Equation Bias: A Numerical Example
Summary and Conclusions
Exercises
Questions
Problems
The Identification Problem
Notations and Definitions
The Identification Problem
Underidentification
Just, or Exact, Identification
Overidentification
Rules for Identification
The Order Condition of Identifiability
The Rank Condition of Identifiability
A Test of Simultaneity
Hausman Specification Test
Example 19.5: Pindyck-Rubinfeld Model of Public Spending
Tests for Exogeneity
A Note on Causality and Exogeneity
Summary and Conclusions
Exercises
Simultaneous-Equation Methods
Approaches to Estimation
Recursive Models and Ordinary Least Squares
Estimation of a Just Identified Equation: The Method of Indirect Least Squares (ILS)
An Illustrative Example
Properties of ILS Estimators
Estimation of an Overidentified Equation: The Method of Two-Stage Least Squares (2SLS)
2SLS: A Numerical Example
Illustrative Examples
Example 20.1: Advertising, Concentration, and Price Margins
Example 20.2: Klein's Model I
Example 20.3: The Capital Asset Pricing Model Expressed as a Recursive System
Example 20.4: Revised Form of St. Louis Model
Summary and Conclusions
Exercises
Questions
Problems
Appendix 20A
Bias in the Indirect Least-Squares Estimators
Estimation of Standard Errors of 2SLS Estimators
Time Series Econometrics
Time Series Econometrics I: Stationarity, Unit Roots, and Cointegration
A Look at Selected U.S. Economic Time Series
Stationary Stochastic Process
Test of Stationarity Based on Correlogram
The Unit Root Test of Stationarity
Is the U.S. GDP Time Series Stationary?
Is the First-Differenced GDP Series Stationary?
Trend-Stationary (TS) and Difference-Stationary (DS) Stochastic Process
Spurious Regression
Cointegration
Engle-Granger (EG) or Augmented Engle-Granger (AEG) Test
Cointegrating Regression Durbin-Watson (CRDW) Test
Cointegration and Error Correction Mechanism (ECM)
Summary and Conclusions
Exercises
Questions
Problems
Appendix 21A
A Random Walk Model
Time Series Econometrics II: Forecasting with ARIMA and VAR Models
Approaches to Economic Forecasting
AR, MA, and ARIMA Modeling of Time Series Data
An Autoregressive (AR) Process
A Moving Average (MA) Process
An Autoregressive and Moving Average (ARMA) Process
An Autoregressive Integrated Moving Average (ARIMA) Process
The Box-Jenkins (BJ) Methodology
Identification
Estimation of the ARIMA Model
Diagnostic Checking
Forecasting
Further Aspects of the BJ Methodology
Vector Autoregression (VAR)
Estimation of VAR
Forecasting with VAR
Some Problems with VAR Modeling
An Application of VAR: A VAR Model of the Texas Economy
Summary and Conclusions
Exercises
Questions
Problems
Appendixes
A Review of Some Statistical Concepts
Rudiments of Matrix Algebra
A List of Statistical Computer Packages
Statistical Tables
Areas under the Standardized Normal Distribution
Percentage Points of the t Distribution
Upper Percentage Points of the F Distribution
Upper Percentage Points of the X2 Distribution
Durbin-Watson d Statistic: Significant Points of dL and dU at 0.05 and 0.01 Levels of Significance
Critical Values of Runs in the Runs Test
Selected Bibliography
Indexes
Name Index
Subject Index
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