Undergraduate Econometrics, 2nd Edition,9780471331841

Undergraduate Econometrics, 2nd Edition

by ; ;
Edition: 2nd
ISBN13: 9780471331841
ISBN10: 0471331848
Format: Hardcover
Pub. Date: 2000-11-01
Publisher(s): Wiley
List Price: $207.35

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Summary

This text on Econometrics is designed specifically for undergraduates- it is shorter and more focused on selective topics, important results are demonstrated in a simple manner, and algebra and proofs are presented so that instructors can choose to include or exclude them.

Author Biography

R. Carter Hill, Louisiana State University;
William Griffiths, University of New England; and
George Judge, University of California, Berkeley

Table of Contents

Preface v
An Introduction to Econometrics
1(10)
Why Study Econometrics?
1(1)
What Is Econometrics?
2(2)
Some Examples
3(1)
The Econometric Model
4(1)
How Do We Obtain Data?
5(4)
Experimental Data
5(3)
Nonexperimental Data
8(1)
Statistical Inference
9(1)
A Research Format
9(2)
Some Basic Probability Concepts
11(31)
Experiments, Outcomes and Random Variables
11(3)
Controlled Experiments---Experimental Data
11(1)
Uncontrolled Experiments---Nonexperimental Data
12(1)
Discrete and Continuous Random Variables
13(1)
The Probability Distribution of a Random Variable
14(2)
Probability Distributions of Discrete Random Variables
14(1)
The Probability Density Function of a Continuous Random Variable
15(1)
Expected Values Involving a Single Random Variable
16(5)
The Rules of Summation
17(1)
The Mean of a Random Variable
18(2)
Expectation of a Function of a Random Variable
20(1)
The Variance of a Random Variable
21(1)
Using Joint Probability Density Functions
21(5)
Marginal Probability Density Functions
23(1)
Conditional Probability Density Functions
24(1)
Independent Random Variables
25(1)
The Expected Value of a Function of Several Random Variables: Covariance and Correlation
26(5)
The Mean of a Weighted Sum of Random Variables
30(1)
The Variance of a Weighted Sum of Random Variables
31(1)
The Normal Distribution
31(4)
Learning Objectives
35(1)
Exercises
36(6)
The Simple Linear Regression Model: Specification and Estimation
42(26)
An Economic Model
42(3)
An Econometric Model
45(5)
Introducing the Error Term
47(3)
Estimating the Parameters for the Expenditure Relationship
50(10)
The Least Squares Principle
51(4)
Estimates for the Food Expenditure Function
55(1)
Interpreting the Estimates
56(1)
Elasticities
56(1)
Prediction
57(1)
Examining Computer Output
57(1)
Other Economic Models
58(2)
Learning Objectives
60(1)
Exercises
61(7)
Properties of the Least Squares Estimators
68(22)
The Least Squares Estimators as Random Variables
68(1)
The Sampling Properties of the Least Squares Estimators
69(8)
The Expected Values of b1 and b2
70(1)
The Repeated Sampling Context
71(1)
Derivation of Equation 4.2.1
72(1)
The Variances and Covariance of b1 and b2
73(4)
Linear Estimators
77(1)
The Gauss-Markov Theorem
77(2)
The Probability Distributions of the Least Squares Estimators
79(1)
Estimating the Variance of the Error Term
80(4)
Estimating the Variances and Covariances of the Least Squares Estimators
81(1)
The Estimated Variances and Covariances for the Food Expenditure Example
81(1)
Sample Computer Output
82(2)
Learning Objectives
84(1)
Exercises
85(5)
Inference in the Simple Regression Model: Interval Estimation, Hypothesis Testing, and Prediction
90(31)
Interval Estimation
91(7)
The Theory
91(1)
The Chi-Square Distribution
92(1)
The Probability Distribution of σ2
93(1)
The t-Distribution
93(1)
A Key Result
94(1)
Obtaining Interval Estimates
95(1)
The Repeated Sampling Context
96(1)
An Illustration
97(1)
Hypothesis Testing
98(12)
The Null Hypothesis
99(1)
The Alternative Hypothesis
99(1)
The Test Statistic
100(1)
The Rejection Region
101(1)
The Food Expenditure Example
102(1)
Type I and Type II Errors
103(1)
The p-Value of a Hypothesis Test
104(1)
Tests of Significance
105(1)
A Significance Test in the Food Expenditure Model
105(2)
Reading Computer Output
107(1)
A Relationship Between Two-Tailed Hypothesis Tests and Interval Estimation
107(1)
One-Tailed Tests
108(1)
A Comment on Stating Null and Alternative Hypotheses
109(1)
The Least Squares Predictor
110(3)
Prediction in the Food Expenditure Model
113(1)
Learning Objectives
113(1)
Exercises
114(7)
The Simple Linear Regression Model: Reporting the Results and Choosing the Functional Form
121(24)
The Coefficient of Determination
121(5)
Analysis of Variance Table and R2 for Food Expenditure Example
124(1)
Correlation Analysis
125(1)
Correlation Analysis and R2
126(1)
Reporting Regression Results
126(2)
The Effects of Scaling the Data
127(1)
Choosing a Functional Form
128(10)
Some Commonly Used Functional Forms
129(3)
Examples Using Alternative Functional Forms
132(1)
The Food Expenditure Model
132(1)
Some Other Economic Models and Functional Forms
133(2)
Choosing a Functional Form: Empirical Issues
135(3)
Are the Residuals Normally Distributed?
138(1)
Learning Objectives
139(1)
Exercises
140(5)
The Multiple Regression Model
145(25)
Model Specification and the Data
145(5)
The Economic Model
145(2)
The Econometric Model
147(1)
The General Model
148(1)
The Assumptions of the Model
149(1)
Estimating the Parameters of the Multiple Regression Model
150(4)
Least Squares Estimation Procedure
151(1)
Least Squares Estimates Using Hamburger Chain Data
151(2)
Estimation of the Error Variance σ2
153(1)
Sampling Properties of the Least Squares Estimator
154(3)
The Variances and Covariances of the Least Squares Estimators
154(2)
The Properties of the Least Squares Estimators Assuming Normally Distributed Errors
156(1)
Interval Estimation
157(2)
Hypothesis Testing for a Single Coefficient
159(3)
Testing the Significance of a Single Coefficient
159(1)
One-Tailed Hypothesis Testing for a Single Coefficient
160(1)
Testing for Elastic Demand
161(1)
Testing Advertizing Effectiveness
161(1)
Measuring Goodness of Fit
162(2)
Learning Objectives
164(1)
Exercises
164(6)
Further Inference in the Multiple Regression Model
170(29)
The F-Test
170(4)
The F-Distribution: Theory
173(1)
Testing the Significance of a Model
174(3)
The Relationship Between Joint and Individual Tests
176(1)
An Extended Model
177(1)
Testing Some Economic Hypotheses
178(3)
The Significance of Advertising
178(1)
The Optimal Level of Advertising
179(2)
The Optimal Level of Advertising and Price
181(1)
The Use of Nonsample Information
181(3)
Model Specification
184(5)
Omitted and Irrelevant Variables
185(1)
Omitted Variable Bias: A Proof
186(1)
Testing for Model Misspecification: The Reset Test
187(2)
Collinear Economic Variables
189(2)
The Consequences of Collinearity
189(1)
Identifying and Mitigating Collinearity
190(1)
Prediction
191(1)
Learning Objectives
192(1)
Exercises
193(6)
Dummy (Binary) Variables
199(19)
Introduction
199(1)
The Use of Intercept Dummy Variables
200(2)
Slope Dummy Variables
202(1)
An Example: The University Effect on House Prices
203(2)
Common Applications of Dummy Variables
205(3)
Interactions Between Qualitative Factors
205(1)
Qualitative Variables with Several Categories
206(1)
Controlling for Time
207(1)
Seasonal Dummies
207(1)
Annual Dummies
207(1)
Regime Effects
208(1)
Testing the Existence of Qualitative Effects
208(1)
Testing for a Single Qualitative Effect
208(1)
Testing Jointly for the Presence of Several Qualitative Effects
209(1)
Testing the Equivalence of Two Regressions Using Dummy Variables
209(4)
The Chow Test
210(1)
An Empirical Example of the Chow Test
211(2)
Learning Objectives
213(1)
Exercises
213(5)
Nonlinear Models
218(17)
Polynomial and Interaction Variables
218(4)
Polynomial Terms in a Regression Model
219(1)
Interactions Between Two Continuous Variables
220(2)
A Simple Nonlinear-in-the-Parameters Model
222(2)
A Logistic Growth Curve
224(3)
Poisson Regression
227(2)
Learning Objectives
229(1)
Exercises
229(6)
Heteroskedasticity
235(23)
The Nature of Heteroskedasticity
235(3)
The Consequences of Heteroskedasticity for the Least Squares Estimator
238(3)
White's Approximate Estimator for the Variance of the Least Squares Estimator
240(1)
Proportional Heteroskadesticity
241(3)
Detecting Heteroskedasticity
244(2)
Residual Plots
244(1)
The Goldfeld-Quandt Test
245(1)
A Sample with a Heteroskedastic Partition
246(5)
Economic Model
246(2)
Generalized Least Squares Through Model Transformation
248(1)
Implementing Generalized Least Squares
249(1)
Testing the Variance Assumption
250(1)
Learning Objectives
251(1)
Exercises
251(7)
Autocorrelation
258(25)
The Nature of the Problem
258(3)
Area Response Model for Sugar Cane
259(1)
Least Squares Estimation
260(1)
First-Order Autoregressive Errors
261(2)
Properties of an AR(1) Error
262(1)
Consequences for the Least Squares Estimator
263(2)
Generalized Least Square
265(3)
A Transformation
265(2)
Transforming the First Observation
267(1)
Implementing Generalized Least Squares
268(3)
The Sugar Cane Example Revisited
269(2)
Testing for Autocorrelation
271(4)
The Durbin-Waston Test
271(2)
The Bounds Test
273(1)
A Lagrange Multiplier Test
274(1)
Prediction with AR(1) Errors
275(2)
Learning Objectives
277(1)
Exercises
277(6)
Random Regressors and Moment Based Estimation
283(21)
Introduction
283(1)
Linear Regression with Random x's
284(8)
The Finite (Small) Sample Properties of the Least Squares Estimator
285(1)
The Asymptotic (Large) Sample Properties of the Least Squares Estimator When x Is Not Random
285(1)
The Asymptotic (Large) Sample Properties of the Least Squares Estimator When x Is Random
286(1)
The Inconsistency of the Least Squares Estimator When cov (x,e) ≠0
287(1)
A Geometric Explanation of Why the Least Squares Estimator is Inconsistent When cov(x,e)≠0
287(1)
Algebraic Proof That the Least Squares Estimator Is Inconsistent When Cov(x,e)≠0
288(2)
Measurement Errors in Regression Equations
290(1)
An Example of the Consequences of Measurement Errors
291(1)
Estimators Based on the Method of Moments
292(7)
Method of Moments Estimation of the Mean and Variance of a Random Variable
292(1)
Method of Moments Estimation in the Simple Linear Regression Model
293(1)
Instrumental Variables Estimation in the Simple Linear Regression Model
294(1)
The Consistency of the Instrumental Variables Estimator
295(1)
An Empirical Example of Instrumental Variables Estimation
296(1)
Instrumental Variables Estimation When Surplus Instruments Are Available
297(2)
Testing for Correlation Between Explanatory Variables and the Error Term
299(1)
An Empirical Example of the Hausman Test
300(1)
Learning Objectives
300(1)
Exercises
301(3)
Simultaneous Equations Models
304(15)
Introduction
304(1)
A Supply and Demand Model
304(2)
The Reduced Form Equations
306(1)
The Failure of Least Squares Estimation in Simultaneous Equations Models
307(2)
An Intuitive Explanation of the Failure of Least Squares
307(1)
An Algebraic Explanation of the Failure of Least Squares
308(1)
The Identification Problem
309(2)
Two-Stage Least Squares Estimation
311(2)
The General Two-Stage Least Squares Estimation Procedure
312(1)
The Properties of the Two-Stage Least Squares Estimator
313(1)
An Example of Two-Stage Least Squares Estimation
313(3)
Identification
314(1)
The Reduced Form Equations
314(2)
Learning Objectives
316(1)
Exercises
317(2)
Distributed Lag Models
319(16)
Introduction
319(1)
Finite Distributed Lag Models
320(7)
An Economic Model
320(1)
The Econometric Model
321(1)
An Empirical Illustration
321(2)
Polynomial Distributed Lags
323(2)
Selection of the Length of the Finite Lag
325(2)
The Geometric Lag
327(1)
The Koyck Transformation
328(2)
Instrumental Variables Estimation of the Koyck Model
328(1)
Testing for Autocorrelation in Models with Lagged Dependent Variables
329(1)
Autoregressive Distributed Lags
330(2)
The Autoregressive Distributed Lag Model
330(1)
An Illustration of the ARDL Model
331(1)
Learning Objectives
332(1)
Exercises
332(3)
Regression with Time Series Data
335(16)
Stationary Time Series
335(3)
Spurious Regressions
338(3)
Checking Stationarity Using the Autocorrelation Function
341(2)
Unit Root Tests for Stationarity
343(3)
The Dickey-Fuller Tests
344(1)
The Dickey-Fuller Tests: An Example
345(1)
Cointegration
346(1)
An Example of a Cointegration Test
347(1)
Summarizing Estimation Strategies When Using Time Series Data
347(1)
Learning Objectives
348(1)
Exercises
349(2)
Pooling Time-Series and Cross-Sectional Data
351(17)
An Economic Model
351(1)
Seemingly Unrelated Regressions
352(5)
Estimating Separate Equations
353(1)
Joint Estimation of the Equation
354(1)
Separate or Joint Estimation
355(2)
Testing Cross-Equation Restrictions
357(1)
A Dummy Variable Specification
357(2)
The Model
358(1)
An Error Components Model
359(1)
Learning Objectives
360(1)
Exercises
361(7)
Qualitative and Limited Dependent Variable Models
368(15)
Introduction
368(1)
Models with Binary Dependent Variables
368(8)
The Linear Probability Model
369(1)
The Probit Model
370(2)
Maximum Likelihood Estimation of the Probit Model
372(1)
Interpretation of the Probit Model
373(1)
An Example
374(2)
The Logit Model for Binary Choice
376(1)
Other Models with Qualitative Dependent Variables
376(2)
Multinomial Choice Models
377(1)
Ordered Choice Models
377(1)
Count Data Models and Poisson Regression
378(1)
Limited Dependent Variable Models
378(1)
The Tobit Model
378(1)
Sample Selection
379(1)
Learning Objectives
379(1)
Exercises
380(3)
Writing an Empirical Research Report, and Sources of Economic Data
383(6)
Selecting a Topic for an Economics Project
383(1)
Choosing a Topic
383(1)
Writing an Abstract
384(1)
A Format for Writing a Research Report
384(2)
Sources of Economic Data
386(2)
Links to Economic Data on the Internet
386(1)
Economic Data on the Internet
387(1)
Traditional Sources of Economic Data
387(1)
Interpreting Economic Data
388(1)
Exercises
388(1)
Statistical Tables 389(8)
Index 397

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