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Introductory Econometrics A Modern Approach (with Economic Applications Online, Econometrics Data Sets with Solutions Manual Web Site Printed Access Card)

by
Edition:
3rd
ISBN13:

9780324289787

ISBN10:
0324289782
Format:
Hardcover
Pub. Date:
7/13/2005
Publisher(s):
Cengage Learning
List Price: $315.66
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Summary

Succeed in econometrics with INTRODUCTORY ECONOMETRICS and its accompanying resources! Easy-to-read and student-friendly, this economics text places an emphasis on examples that give a concrete reality to economic relationships. With study tools found throughout the text, exam preparation and class projects have never been easier. Coverage of important knowledge used for empirical work and carrying out research projects in a variety of applied social science fields gives you a solid foundation for social science research.

Table of Contents

The Nature of Econometrics and Economic Data
1(22)
What Is Econometrics?
1(1)
Steps in Empirical Economic Analysis
2(3)
The Structure of Economic Data
5(8)
Cross-Sectional Data
6(2)
Time Series Data
8(2)
Pooled Cross Sections
10(1)
Panel or Longitudinal Data
10(3)
A Comment on Data Structures
13(1)
Causality and the Notion of Ceteris Paribus in Econometric Analysis
13(10)
Summary
18(1)
Key Terms
19(1)
Problems
19(1)
Computer Exercises
20(3)
PART 1 Regression Analysis with Cross-Sectional Data
23(318)
The Simple Regression Model
24(49)
Definition of the Simple Regression Model
24(5)
Deriving the Ordinary Least Squares Estimates
29(9)
A Note on Terminology
38(1)
Properties of OLS on Any Sample of Data
38(6)
Fitted Values and Residuals
39(1)
Algebraic Properties of OLS Statistics
40(2)
Goodness-of-Fit
42(2)
Units of Measurement and Functional Form
44(6)
The Effects of Changing Units of Measurement on OLS Statistics
44(2)
Incorporating Nonlinearities in Simple Regression
46(3)
The Meaning of ``Linear'' Regression
49(1)
Expected Values and Variances of the OLS Estimators
50(13)
Unbiasedness of OLS
50(6)
Variances of the OLS Estimators
56(4)
Estimating the Error Variance
60(3)
Regression through the Origin
63(10)
Summary
64(1)
Key Terms
65(1)
Problems
66(3)
Computer Exercises
69(2)
Appendix 2A
71(2)
Multiple Regression Analysis: Estimation
73(50)
Motivation for Multiple Regression
73(5)
The Model with Two Independent Variables
73(3)
The Model with k Independent Variables
76(2)
Mechanics and Interpretation of Ordinary Least Squares
78(11)
Obtaining the OLS Estimates
78(2)
Interpreting the OLS Regression Equation
80(2)
On the Meaning of ``Holding Other Factors Fixed'' in Multiple Regression
82(1)
Changing More than One Independent Variable Simultaneously
82(1)
OLS Fitted Values and Residuals
83(1)
A ``Partialling Out'' Interpretation of Multiple Regression
83(1)
Comparison of Simple and Multiple Regression Estimates
84(1)
Goodness-of-Fit
85(3)
Regression through the Origin
88(1)
The Expected Value of the OLS Estimators
89(10)
Including Irrelevant Variables in a Regression Model
94(1)
Omitted Variable Bias: The Simple Case
95(3)
Omitted Variable Bias: More General Cases
98(1)
The Variance of the OLS Estimators
99(9)
The Components of the OLS Variances: Multicollinearity
101(4)
Variances in Misspecified Models
105(1)
Estimating σ2: Standard Errors of the OLS Estimators
106(2)
Efficiency of OLS: The Gauss-Markov Theorem
108(15)
Summary
109(2)
Key Terms
111(1)
Problems
111(5)
Computer Exercises
116(3)
Appendix 3A
119(4)
Multiple Regression Analysis: Inference
123(53)
Sampling Distributions of the OLS Estimators
123(3)
Testing Hypotheses about a Single Population Parameter: The t Test
126(19)
Testing against One-Sided Alternatives
129(5)
Two-Sided Alternatives
134(2)
Testing Other Hypotheses about βj
136(3)
Computing p-Values for t Tests
139(3)
A Reminder on the Language of Classical Hypothesis Testing
142(1)
Economic, or Practical, versus Statistical Significance
142(3)
Confidence Intervals
145(2)
Testing Hypotheses about a Single Linear Combination of the Parameters
147(3)
Testing Multiple Linear Restrictions: The F Test
150(13)
Testing Exclusion Restrictions
150(7)
Relationship between F and t Statistics
157(1)
The R-Squared Form of the F Statistic
158(1)
Computing p-Values for F Tests
159(1)
The F Statistic for Overall Significance of a Regression
160(1)
Testing General Linear Restrictions
161(2)
Reporting Regression Results
163(13)
Summary
165(2)
Key Terms
167(1)
Problems
168(5)
Computer Exercises
173(3)
Multiple Regression Analysis: OLS Asymptotics
176(16)
Consistency
176(5)
Deriving the Inconsistency in OLS
179(2)
Asymptotic Normality and Large Sample Inference
181(6)
Other Large Sample Tests: The Lagrange Multiplier Statistic
185(2)
Asymptotic Efficiency of OLS
187(5)
Summary
189(1)
Key Terms
189(1)
Problems
190(1)
Computer Exercises
190(1)
Appendix 5A
191(1)
Multiple Regression Analysis: Further Issues
192(38)
Effects of Data Scaling on OLS Statistics
192(5)
Beta Coefficients
195(2)
More on Functional Form
197(9)
More on Using Logarithmic Functional Forms
197(3)
Models with Quadratics
200(4)
Models with Interaction Terms
204(2)
More on Goodness-of-Fit and Selection of Regressors
206(8)
Adjusted R-Squared
208(1)
Using Adjusted R-Squared to Choose between Nonnested Models
209(2)
Controlling for Too Many Factors in Regression Analysis
211(2)
Adding Regressors to Reduce the Error Variance
213(1)
Prediction and Residual Analysis
214(16)
Confidence Intervals for Predictions
214(3)
Residual Analysis
217(1)
Predicting y When log(y) Is the Dependent Variable
218(3)
Summary
221(1)
Key Terms
222(1)
Problems
222(2)
Computer Exercises
224(6)
Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables
230(41)
Describing Qualitative Information
230(2)
A Single Dummy Independent Variable
232(7)
Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable Is log(y)
237(2)
Using Dummy Variables for Multiple Categories
239(5)
Incorporating Ordinal Information by Using Dummy Variables
240(4)
Interactions Involving Dummy Variables
244(8)
Interactions among Dummy Variables
244(1)
Allowing for Different Slopes
245(4)
Testing for Differences in Regression Functions across Groups
249(3)
A Binary Dependent Variable: The Linear Probability Model
252(6)
More on Policy Analysis and Program Evaluation
258(13)
Summary
260(1)
Key Terms
261(1)
Problems
261(4)
Computer Exercises
265(6)
Heteroskedasticity
271(33)
Consequences of Heteroskedasticity for OLS
271(1)
Heteroskedasticity-Robust Inference after OLS Estimation
272(6)
Computing Heteroskedasticity-Robust LM Tests
276(2)
Testing for Heteroskedasticity
278(6)
The White Test for Heteroskedasticity
282(2)
Weighted Least Squares Estimation
284(11)
The Heteroskedasticity Is Known up to a Multiplicative Constant
284(6)
The Heteroskedasticity Function Must Be Estimated: Feasible GLS
290(5)
The Linear Probability Model Revisited
295(9)
Summary
297(1)
Key Terms
298(1)
Problems
298(2)
Computer Exercises
300(4)
More on Specification and Data Problems
304(37)
Functional Form Misspecification
304(6)
RESET as a General Test for Functional Form Misspecification
308(1)
Tests against Nonnested Alternatives
309(1)
Using Proxy Variables for Unobserved Explanatory Variables
310(8)
Using Lagged Dependent Variables as Proxy Variables
315(2)
A Different Slant on Multiple Regression
317(1)
Properties of OLS under Measurement Error
318(7)
Measurement Error in the Dependent Variable
318(3)
Measurement Error in an Explanatory Variable
321(4)
Missing Data, Nonrandom Samples, and Outlying Observations
325(16)
Missing Data
325(1)
Nonrandom Samples
326(2)
Outliers and Influential Observations
328(5)
Summary
333(1)
Key Terms
334(1)
Problems
334(2)
Computer Exercises
336(5)
PART 2 Regression Analysis with Time Series Data
341(106)
Basic Regression Analysis with Time Series Data
342(38)
The Nature of Time Series Data
342(2)
Examples of Time Series Regression Models
344(3)
Static Models
344(1)
Finite Distributed Lag Models
344(3)
A Convention about the Time Index
347(1)
Finite Sample Properties of OLS under Classical Assumptions
347(8)
Unbiasedness of OLS
347(4)
The Variances of the OLS Estimators and the Gauss-Markov Theorem
351(2)
Inference under the Classical Linear Model Assumptions
353(2)
Functional Form, Dummy Variables, and Index Numbers
355(8)
Trends and Seasonality
363(17)
Characterizing Trending Time Series
363(3)
Using Trending Variables in Regression Analysis
366(2)
A Detrending Interpretation of Regressions with a Time Trend
368(1)
Computing R-Squared When the Dependent Variable Is Trending
369(2)
Seasonality
371(2)
Summary
373(2)
Key Terms
375(1)
Problems
375(2)
Computer Exercises
377(3)
Further Issues in Using OLS with Time Series Data
380(32)
Stationary and Weakly Dependent Time Series
380(5)
Stationary and Nonstationary Time Series
381(1)
Weakly Dependent Time Series
382(3)
Asymptotic Properties of OLS
385(7)
Using Highly Persistent Time Series in Regression Analysis
392(8)
Highly Persistent Time Series
392(5)
Transformations on Highly Persistent Time Series
397(1)
Deciding Whether a Time Series Is (1)
398(2)
Dynamically Complete Models and the Absence of Serial Correlation
400(3)
The Homoskedasticity Assumption for Time Series Models
403(9)
Summary
403(2)
Key Terms
405(1)
Problems
405(3)
Computer Exercises
408(4)
Serial Correlation and Heteroskedasticity in Time Series Regressions
412(35)
Properties of OLS with Serially Correlated Errors
412(4)
Unbiasedness and Consistency
412(1)
Efficiency and Inference
413(1)
Goodness-of-Fit
414(1)
Serial Correlation in the Presence of Lagged Dependent Variables
415(1)
Testing for Serial Correlation
416(8)
A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressors
416(3)
The Durbin-Watson Test under Classical Assumptions
419(1)
Testing for AR(1) Serial Correlation Without Strictly Exogenous Regressors
420(2)
Testing for Higher Order Serial Correlation
422(2)
Correcting for Serial Correlation with Strictly Exogenous Regressors
424(7)
Obtaining the Best Linear Unbiased Estimator in the AR(1) Model
424(1)
Feasible GLS Estimation with AR(1) Errors
425(3)
Comparing OLS and FGLS
428(2)
Correcting for Higher Order Serial Correlation
430(1)
Differencing and Serial Correlation
431(1)
Serial Correlation-Robust Inference after OLS
432(4)
Heteroskedasticity in Time Series Regressions
436(11)
Heteroskedasticity-Robust Statistics
436(1)
Testing for Heteroskedasticity
436(2)
Autoregressive Conditional Heteroskedasticity
438(2)
Heteroskedasticity and Serial Correlation in Regression Models
440(1)
Summary
441(1)
Key Terms
442(1)
Problems
442(1)
Computer Exercises
443(4)
PART 3 Advanced Topics
447(260)
Pooling Cross Sections across Time: Simple Panel Data Methods
448(37)
Pooling Independent Cross Sections across Time
449(5)
The Chow Test for Structural Change across Time
454(1)
Policy Analysis with Pooled Cross Sections
454(6)
Two-Period Panel Data Analysis
460(7)
Organizing Panel Data
467(1)
Policy Analysis with Two-Period Panel Data
467(3)
Differencing with More Than Two Time Periods
470(15)
Potential Pitfalls in First-Differencing Panel Data
475(1)
Summary
476(1)
Key Terms
476(1)
Problems
476(2)
Computer Exercises
478(5)
Appendix 13A
483(2)
Advanced Panel Data Methods
485(25)
Fixed Effects Estimation
485(8)
The Dummy Variable Regression
489(2)
Fixed Effects or First Differencing?
491(1)
Fixed Effects with Unbalanced Panels
492(1)
Random Effects Models
493(5)
Random Effects or Fixed Effects?
497(1)
Applying Panel Data Methods to Other Data Structures
498(12)
Summary
500(1)
Key Terms
501(1)
Problems
501(2)
Computer Exercises
503(4)
Appendix 14A
507(3)
Instrumental Variables Estimation and Two Stage Least Squares
510(42)
Motivation: Omitted Variables in a Simple Regression Model
511(10)
Statistical Inference with the IV Estimator
514(5)
Properties of IV with a Poor Instrumental Variable
519(1)
Computing R-Squared after IV Estimation
520(1)
IV Estimation of the Multiple Regression Model
521(4)
Two Stage Least Squares
525(5)
A Single Endogenous Explanatory Variable
525(3)
Multicollinearity and 2SLS
528(1)
Multiple Endogenous Explanatory Variables
528(1)
Testing Multiple Hypotheses after 2SLS Estimation
529(1)
IV Solutions to Errors-in-Variables Problems
530(2)
Testing for Endogeneity and Testing Overidentifying Restrictions
532(3)
Testing for Endogeneity
532(1)
Testing Overidentification Restrictions
533(2)
2SLS with Heteroskedasticity
535(1)
Applying 2SLS to Time Series Equations
536(2)
Applying 2SLS to Pooled Cross Sections and Panel Data
538(14)
Summary
540(1)
Key Terms
541(1)
Problems
541(3)
Computer Exercises
544(5)
Appendix 15A
549(3)
Simultaneous Equations Models
552(30)
The Nature of Simultaneous Equations Models
552(5)
Simultaneity Bias in OLS
557(2)
Identifying and Estimating a Structural Equation
559(6)
Identification in a Two-Equation System
559(5)
Estimation by 2SLS
564(1)
Systems with More Than Two Equations
565(2)
Identification in Systems with Three or More Equations
566(1)
Estimation
567(1)
Simultaneous Equations Models with Time Series
567(4)
Simultaneous Equations Models with Panel Data
571(11)
Summary
573(1)
Key Terms
574(1)
Problems
574(3)
Computer Exercises
577(5)
Limited Dependent Variable Models and Sample Selection Corrections
582(50)
Logit and Probit Models for Binary Response
583(12)
Specifying Logit and Probit Models
583(3)
Maximum Likelihood Estimation of Logit and Probit Models
586(1)
Testing Multiple Hypotheses
587(1)
Interpreting the Logit and Probit Estimates
588(7)
The Tobit Model for Corner Solution Responses
595(9)
Interpreting the Tobit Estimates
597(5)
Specification Issues in Tobit Models
602(2)
The Poisson Regression Model
604(5)
Censored and Truncated Regression Models
609(7)
Censored Regression Models
610(3)
Truncated Regression Models
613(3)
Sample Selection Corrections
616(16)
When Is OLS on the Selected Sample Consistent?
616(2)
Incidental Truncation
618(4)
Summary
622(1)
Key Terms
623(1)
Problems
624(1)
Computer Exercises
625(5)
Appendix 17A
630(2)
Advanced Time Series Topics
632(46)
Infinite Distributed Lag Models
633(6)
The Geometric (or Koyck) Distributed Lag
635(2)
Rational Distributed Lag Models
637(2)
Testing for Unit Roots
639(6)
Spurious Regression
645(2)
Cointegration and Error Correction Models
647(7)
Cointegration
647(5)
Error Correction Models
652(2)
Forecasting
654(24)
Types of Regression Models Used for Forecasting
656(1)
One-Step-Ahead Forecasting
657(4)
Comparing One-Step-Ahead Forecasts
661(1)
Multiple-Step-Ahead Forecasts
662(3)
Forecasting Trending, Seasonal, and Integrated Processes
665(5)
Summary
670(1)
Key Terms
671(1)
Problems
671(3)
Computer Exercises
674(4)
Carrying Out an Empirical Project
678(29)
Posing a Question
678(2)
Literature Review
680(1)
Data Collection
681(4)
Deciding on the Appropriate Data Set
681(1)
Entering and Storing Your Data
682(2)
Inspecting, Cleaning, and Summarizing Your Data
684(1)
Econometric Analysis
685(4)
Writing an Empirical Paper
689(18)
Introduction
689(1)
Conceptual (or Theoretical) Framework
689(1)
Econometric Models and Estimation Methods
690(2)
The Data
692(1)
Results
693(1)
Conclusions
694(1)
Style Hints
694(3)
Summary
697(1)
Key Terms
697(1)
Sample Empirical Projects
698(5)
List of Journals
703(1)
Data Sources
704(3)
APPENDICES
APPENDIX A Basic Mathematical Tools
707(21)
A.1 The Summation Operator and Descriptive Statistics
707(3)
A.2 Properties of Linear Functions
710(3)
A.3 Proportions and Percentages
713(1)
A.4 Some Special Functions and Their Properties
714(8)
Quadratic Functions
715(2)
The Natural Logarithm
717(4)
The Exponential Function
721(1)
A.5 Differential Calculus
722(6)
Summary
725(1)
Key Terms
725(1)
Problems
725(3)
APPENDIX B Fundamentals of Probability
728(35)
B.1 Random Variables and Their Probability Distributions
728(5)
Discrete Random Variables
729(2)
Continuous Random Variables
731(2)
B.2 Joint Distributions, Conditional Distributions, and Independence
733(4)
Joint Distributions and Independence
734(2)
Conditional Distributions
736(1)
B.3 Features of Probability Distributions
737(7)
A Measure of Central Tendency: The Expected Value
737(2)
Properties of Expected Values
739(1)
Another Measure of Central Tendency: The Median
740(1)
Measures of Variability: Variance and Standard Deviation
741(1)
Variance
742(1)
Standard Deviation
743(1)
Standardizing a Random Variable
744(1)
B.4 Features of Joint and Conditional Distributions
744(9)
Measures of Association: Covariance and Correlation
744(1)
Covariance
745(1)
Correlation Coefficient
746(1)
Variance of Sums of Random Variables
747(1)
Conditional Expectation
748(2)
Properties of Conditional Expectation
750(3)
Conditional Variance
753(1)
B.5 The Normal and Related Distributions
753(10)
The Normal Distribution
753(1)
The Standard Normal Distribution
754(3)
Additional Properties of the Normal Distribution
757(1)
The Chi-Square Distribution
757(1)
The t Distribution
758(1)
The F Distribution
759(1)
Summary
760(1)
Key Terms
761(1)
Problems
761(2)
APPENDIX C Fundamentals of Mathematical Statistics
763(45)
C.1 Populations, Parameters, and Random Sampling
763(1)
Sampling
764(1)
C.2 Finite Sample Properties of Estimators
764(8)
Estimators and Estimates
765(1)
Unbiasedness
766(2)
The Sampling Variance of Estimators
768(3)
Efficiency
771(1)
C.3 Asymptotic or Larger Sample Properties of Estimators
772(5)
Consistency
772(3)
Asymptotic Normality
775(2)
C.4 General Approaches to Parameter Estimation
777(3)
Method of Moments
777(1)
Maximum Likelihood
778(1)
Least Squares
779(1)
C.5 Interval Estimation and Confidence Intervals
780(8)
The Nature of Interval Estimation
780(2)
Confidence Intervals for the Mean from a Normally Distributed Population
782(4)
A Simple Rule of Thumb for a 95% Confidence Interval
786(1)
Asymptotic Confidence Intervals for Nonnormal Populations
787(1)
C.6 Hypothesis Testing
788(12)
Fundamentals of Hypothesis Testing
788(2)
Testing Hypotheses about the Mean in a Normal Population
790(4)
Asymptotic Tests for Nonnormal Populations
794(1)
Computing and Using p-Values
794(4)
The Relationship between Confidence Intervals and Hypothesis Testing
798(1)
Practical versus Statistical Significance
799(1)
C.7 Remarks on Notation
800(8)
Summary
801(1)
Key Terms
801(1)
Problems
802(6)
APPENDIX D Summary of Matrix Algebra
808(11)
D.1 Basic Definitions
808(1)
D.2 Matrix Operations
809(4)
Matrix Addition
809(1)
Scalar Multiplication
810(1)
Matrix Multiplication
810(1)
Transpose
811(1)
Partitioned Matrix Multiplication
812(1)
Trace
812(1)
Inverse
813(1)
D.3 Linear Independence and Rank of a Matrix
813(1)
D.4 Quadratic Forms and Positive Definite Matrices
814(1)
D.5 Idempotent Matrices
814(1)
D.6 Differentiation of Linear and Quadratic Forms
815(1)
D.7 Moments and Distributions of Random Vectors
815(4)
Expected Value
816(1)
Variance-Covariance Matrix
816(1)
Multivariate Normal Distribution
816(1)
Chi-Square Distribution
817(1)
t Distribution
817(1)
F Distribution
817(1)
Summary
817(1)
Key Terms
818(1)
Problems
818(1)
APPENDIX E The Linear Regression Model in Matrix Form
819(15)
E.1 The Model and Ordinary Least Squares Estimation
819(3)
E.2 Finite Sample Properties of OLS
822(4)
E.3 Statistical Inference
826(1)
E.4 Some Asymptotic Analysis
827(7)
Wald Statistics for Testing Multiple Hypotheses
830(1)
Summary
831(1)
Key Terms
831(1)
Problems
832(2)
APPENDIX F Answers to Chapter Questions
834(13)
APPENDIX G Statistical Tables
847(7)
References 854(5)
Glossary 859(14)
Index 873


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