An introduction to econometrics: a self-contained approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass.[u.a.]
MIT Press
2013
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes index. |
| Beschreibung: | XVI, 874 S. graph. Darst. |
| ISBN: | 9780262019224 |
Internformat
MARC
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| 100 | 1 | |a Westhoff, Frank H. |d 1946- |e Verfasser |0 (DE-588)171355156 |4 aut | |
| 245 | 1 | 0 | |a An introduction to econometrics |b a self-contained approach |c Frank Westhoff |
| 264 | 1 | |a Cambridge, Mass.[u.a.] |b MIT Press |c 2013 | |
| 300 | |a XVI, 874 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 650 | 4 | |a Econometrics | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
How to Use This Book
xvii
Descriptive Statistics
1
Chapter
1
Prep Questions
1
1.1
Describing a Single Data Variable
2
1.1.1
Introduction to Distributions
2
1.1.2
Measure of the Distribution Center: Mean (Average)
3
1.1.3
Measures of the Distribution Spread: Range, Variance, and Standard Deviation
8
1.1.4
Histogram: Visual Illustration of a Data Variable s Distribution
11
1.2
Describing the Relationship between Two Data Variables
13
1.2.1
Scatter Diagram: Visual Illustration of How Two Data Variables Are Related
13
1.2.2
Correlation of Two Variables
13
1.2.3
Measures of Correlation: Covariance
13
1.2.4
Independence of Two Variables
19
1.2.5
Measures of Correlation: Correlation Coefficient
22
1.2.6
Correlation and Causation
27
1.3
Arithmetic of Means, Variances, and Covariances
27
Chapter
1
Review Questions
29
Chapter
1
Exercises
30
Appendix
1.1:
The Arithmetic of Means, Variances, and Covariances
40
Essentials of Probability and Estimation Procedures
45
Chapter
2
Prep Questions
46
2.1
Random Processes and Probability Random Process: A Process Whose Outcome Cannot Be Predicted with
Certainty
46
2.1.1
Random Process: A Process Whose Outcome Cannot Be Predicted with Certainty
46
2.1.2
Probability: The Likelihood of a Particular Outcome of a Random Process
47
2.1.3
Random Variable: A Variable That Is Associated with an Outcome of a Random Process
48
2.2
Discrete Random Variables and Probability Distributions
48
2.2.1
Probability Distribution Describes the Probability for All Possible Values of a Random
Variable
48
2.2.2
A Random Variable s Bad News and Good News
50
2.2.3
Relative Frequency Interpretation of Probability
50
2.2.4
Relative Frequency Interpretation of Probability Summary
52
vi
Contents
2.3
Describing a Probability Distribution of ,i Random Variable
53
2.3.1
Center ot the Probability Distribution: Mean {Expected Value) of the Random Variable
53
2.3.2
Spread of the Probability Distribution: Variance of the Random Variable
54
2.4
Continuous Random Variables and Probability Distributions
58
2.5
Estimation Procedures: Populations and Samples
61
2.5.1
Clint s Dilemma: Assessing Clint s Political Prospects
62
2.5.2
Usefulness of Simulations
63
2.5.3
Center of the Probability Distribution: Mean of the Random Variable
66
2.5.4
Spread of the Probability Distribution: Variance of the Random Variable
67
2.6
Mean, Variance, and Covanance: Data Variables and Random Variables
77
Chapter
2
Review Questions
77
Chapter
2
Exercises
11
Interval Estimates and the Central Limit Theorem
87
Chapter
3
Prep Questions
88
3.1
Review
88
3.1.1
Random Variables
88
3.1.2
Relative Frequency Interpretation of Probability
89
3.2
Populations, Samples, Estimation Procedures, and the Estimate s Probability Distribution
89
3.2.1
Measure of the Probability Distribution Center: Mean of the Random Variable
90
3.2.2
Measure of the Probability Distribution Spread: Variance of the Random Variable
91
3.2.3
Why Is the Mean of the Estimate s Probability Distribution Important? Biased and Unbiased
Estimation Procedures
93
3.2.4
Why Is the Variance of the Estimate s Probability Distribution Important? Reliability of Unbiased
Estimation Procedures
96
3.3
Interval Estimates
98
3.4
Relative Frequency Interpretation of Probability
98
3.5
Central Limit Theorem
100
3.6
The Normal Distribution: A Way to Estimate Probabilities
102
3.6.1
Properties of the Normal Distribution:
105
3.6.2
Using the Normal Distribution Table: An Example
105
3.6.3
Justifying the Use of the Normal Distribution
107
3.6.4
Normal Distribution s Rules of Thumb
110
Chapter
3
Review Questions
112
Chapter
3
Exercises
112
Appendix
3.1:
Normal Distribution Right-Tail Probabilities
117
Estimation Procedures, Estimates, and Hypothesis Testing
119
Chapter
4
Prep Questions
119
4.1
Clint s Dilemma and Estimation Procedures
121
4.1.1
Clint s Opinion Poll and His Dilemma
121
4.1.2
Clint s Estimation Procedure: The General and the Specific
123
4.1.3
Taking Stock and Our Strategy to Assess the Reliability of Clint s Poll Results: Use the General
Properties of the Estimation Procedure to Assess the Reliability of the One Specific
Application
123
4.1.4
Importance of the Mean (Center) of the Estimate s Probability Distribution
124
vii
Contents
4.1.5
Importance
of the Variance (Spread) of the Estimate s Probability Distribution for an Unbiased
Estimation Procedure
125
4.2
Hypothesis Testing
126
4.2.1
Motivating Hypothesis Testing: The Evidence and the Cynic
126
4.2.2
Formalizing Hypothesis Testing: Five Steps
130
4.2.3
Significance Levels and Standards of Proof
133
4.2.4
Type I and Type II Errors: The Trade-Offs
135
Chapter
4
Review Questions
138
Chapter
4
Exercises
139
Ordinary Least Squares Estimation Procedure—The Mechanics
145
Chapter
5
Prep Questions
146
5.1
Best Fitting Line
148
5.2
Clint s Assignment
150
5.3
Simple Regression Model
151
5.3.1
Parameters of the Model
151
5.3.2
Error Term and Random Influences
151
5.3.3
What Is Simple about the Simple Regression Model?
152
5.3.4
Best Fitting Line
152
5.3.5
Needed: A Systematic Procedure to Determine the Best Fitting Line
153
5.4
Ordinary Least Squares (OLS) Estimation Procedure
154
5.4.1
Sum of Squared Residuals Criterion
155
5.4.2
Finding the Best Fitting Line
156
5.5
Importance of the Error Term
164
5.5.1
Absence of Random Influences: A What If Question
165
5.5.2
Presence of Random Influences: Back to Reality
168
5.6
Error Terms and Random Influences: A Closer Look
171
5.7
Standard Ordinary Least Squares (OLS) Premises
173
5.8
Clint s Assignment: The Two Parts
173
Chapter
5
Review Questions
174
Chapter
5
Exercises
174
Ordinary Least Squares Estimation Procedure—The Properties
181
Chapter
6
Prep Questions
182
6.1
Clint s Assignment: Assess the Effect of Studying on Quiz Scores
182
6.2
Review
183
6.2.1
Regression Model
183
6.2.2
The Error Term
183
6.2.3
Ordinary Least Squares (OLS) Estimation Procedure
184
6.2.4
The Estimates,
beo«;
and bx, Are Random Variables
185
6.3
Strategy: General Properties and a Specific Application
185
6.3.1
Review: Assessing Clint s Opinion Poll Results
185
6.3.2
Preview: Assessing Professor Lord s Quiz Results
187
6.4
Standard Ordinary Least Squares (OLS) Regression Premises
189
6.5
New Equation for the Ordinary Least Squares (OLS) Coefficient Estimate
190
6.6
General Properties: Describing the Coefficient Estimate s Probability Distribution
190
viii Contents
6.6.1
Mean (Center) of the Coefficient Estimate s Probability Distribution
190
6.6.2
Variance (Spread) of the Coefficient Estimate s Probability Distribution
194
6.7
Estimation Procedures and the Estimate s Probability Distribution
198
6.8
Reliability of the Coefficient Estimate
199
6.8.1
Estimate Reliability and the Variance of the Error Term s Probability Distribution
199
6.8.2
Estimate Reliability and the Sample Size
201
6.8.3
Estimate Reliability and the Range of x s
202
6.8.4
Reliability Summary
204
6.9
Best Linear Unbiased Estimation Procedure (BLUE)
205
Chapter
6
Review Questions
208
Chapter
6
Exercises
208
Appendix
6.1:
New Equation for the OLS Coefficient Estimate
213
Appendix
6.2:
Gauss-Markov Theorem
215
Estimating the Variance of an Estimate s Probability Distribution
221
Chapter
7
Prep Questions
222
7.1
Review
223
7.1.1
Clint s Assignment
223
7.1.2
General Properties of the Ordinary Least Squares (OLS) Estimation Procedure
223
7.1.3
Importance of the Coefficient Estimate s Probability Distribution
224
7.2
Strategy to Estimate the Variance of the Coefficient Estimate s Probability Distribution
225
7.3
Step
1:
Estimate the Variance of the Error Term s Probability Distribution
226
7.3.1
First Attempt; Variance of the Error Term s Numerical Values
227
7.3.2
Second Attempt: Variance of the Residual s Numerical Values
231
7.3.3
Third Attempt: Adjusted Variance of the Residual s Numerical Values
235
7.4
Step
2:
Use the Estimated Variance of the Error Term s Probability Distribution to Estimate the Variance of
the Coefficient Estimate s Probability Distribution
237
7.5
Tying up a Loose End: Degrees of Freedom
241
7.5.1
Reviewing our Second and Third Attempts to Estimate the Variance of the Error Term s Probability
Distribution
241
7.5.2
How Do We Calculate an Average?
242
7.6
Summary: The Ordinary Least Squares (OLS) Estimation Procedure
245
7.6.1
Three Important Parts
245
7.6.2
Regression Results
246
Chapter
7
Review Questions
247
Chapter
7
Exercises
248
Interval Estimates and Hypothesis Testing
251
Chapter
8
Prep Questions
251
8.1
Clint s Assignment: Taking Stock
253
8.2
Estimate Reliability: Interval Estimate Question
254
8.2.1
Normal Distribution versus the Student
ŕ-Distribution:
One Last Complication
256
8.2.2
Assessing the Reliability of a Coefficient Estimate
258
8.3
Theory Assessment: Hypothesis Testing
262
8.3.1
Motivating Hypothesis Testing: The Cynic
262
8.3.2
Formalizing Hypothesis Testing: The Steps
268
¡x
Contents
8.4
Summary: The Ordinary Least Squares (OLS) Estimation Procedure
270
8.4.1
Regression Model and the Role of the Error Term
270
8.4.2
Standard Ordinary Least Squares (OLS) Premises
271
8.4.3
Ordinary Least Squares (OLS) Estimation Procedure: Three Important Estimation Procedures
271
8.4.4
Properties of the Ordinary Least Squares (OLS) Estimation Procedure and the Standard Ordinary
Least Squares (OLS) Premises
272
Chapter
8
Review Questions
273
Chapter
8
Exercises
273
Appendix
8.1 :
Student
ř-Distribution
Table—Right-Tail Critical Values
278
Appendix
8.2:
Assessing the Reliability of a Coefficient Estimate Using the Student ¿-Distribution Table
280
9
One-Tailed Tests, Two-Tailed Tests, and Logarithms
285
Chapter
9
Prep Questions
286
9.1
A One-Tailed Hypothesis Test: The Downward Sloping Demand Curve
286
9.2
One-Tailed versus Two-Tailed Tests
291
9.3
A Two-Tailed Hypothesis Test: The Budget Theory of Demand
291
9.4
Hypothesis Testing Using Clever Algebraic Manipulations
299
9.5
Summary: One-Tailed and Two-Tailed Tests
303
9.6
Logarithms: A Useful Econometric Tool to Fine Tuning Hypotheses—The Math
303
9.6.1
Interpretation of the Coefficient Estimate:
Esty
=
b(amt
+
Ьхх
305
9.6.2
Differential Approximation: Ay
=
(dyfdx)Ax
305
9.6.3
Derivative of a Natural Logarithm:
d
log(z)/dz
-
1/z
305
9.6.4
Dependent Variable Logarithm:
у
=
og(z)
306
9.6.5
Explanatory Variable Logarithm of
ζ: χ
-
og(z)
307
9.7
Using Logarithms—An Illustration: Wages and Education
307
9.7.1
Linear Model: Waget
= ßComt + ßcHSEduct +
e,
308
9.7.2
Log Dependent Variable Model: LogWage,
= ßConit + ßEHSEduct +
e,
309
9.7.3
Log Explanatory Variable Model: Waget
= ßComi + ßELogHSEduct +
e,
310
9.7.4
Log-Log (Constant Elasticity) Model: LogWaget
= ßComt + ßLLogHSEduq +
e,
311
9.8
Summary: Logarithms and the Interpretation of Coefficient Estimates
311
Chapter
9
Review Questions
312
Chapter
9
Exercises
312
10
Multiple Regression Analysis—Introduction
317
Chapter
10
Prep Questions
317
10.1
Simple versus Multiple Regression Analysis
318
10.2
Coal of Multiple Regression Analysis
318
10.3
A One-Tailed Test: Downward Sloping Demand Theory
318
10.4
A Two-Tailed Test: No Money Illusion Theory
329
10.4.1
Linear Demand Model and the No Money Illusion Theory
331
10.4.2
Constant Elasticity Demand Model and the No Money Illusion Theory
333
10.4.3
Calculating Prob[Results IF Ho true]: Clever Algebraic Manipulation
340
Chapter
10
Review Questions
344
Chapter
10
Exercises
345
Contents
11
Hypothesis Testing and the
Wald Test 349
Chapter
11
Prep Questions
349
11.1
No Money Illusion Theory: Taking Stock
351
11.2
No Money Illusion Theory: Calculating ProbfResults IF Ho True]
353
11.2.1
Clever Algebraic Manipulation
353
11.2.2 Wald (f-Distribution)
Test
353
11.2.3
Calculating ProbfResults IF
Н„
true]: Let the Software Do the Work
364
11.3
Testing the Significance of the Entire Model
366
11.4
Equivalence of Two-Tailed ¿-Tests and
Wald
Tests (f-Tests)
369
11.4.1
Two-Tailed
ŕ-Test
369
11.4.2 Wald
Test
371
11.5
Three Important Distributions
374
Chapter
11
Review Questions
375
Chapter
11
Exercises
376
12
Model Specification and Development
381
Chapter
12
Prep Questions
381
12.1
Model Specification: Ramsey REgression Specification Error Test (RESET)
384
12.1.1
RESET Logic
384
12.1.2
Linear Demand Model
386
12.1.3
Constant Elasticity Demand Model
389
12.2
Model Development: The Effect of Economic Conditions on Presidential Elections
391
12.2.1
General Theory: It s the economy stupid
391
12.2.2
Generate Relevant Variables
392
12.2.3
Data Oddities
393
12.2.4
Model Formulation and Assessment: An Iterative Process
395
12.2.5
Specific Voting Models
395
Chapter
12
Review Questions
404
Chapter
12
Exercises
404
13
Dummy and Interaction Variables
409
Chapter
13
Prep Questions
409
13.1
Preliminary Mathematics: Averages and Regressions Including Only a Constant
413
13.2
An Example: Discrimination in Academe
414
13.2.1
Average Salaries
414
13.2.2
Dummy Variables
414
13.2.3
Models
415
13.2.4
Beware of Implicit Assumptions
424
13.2.5
Interaction Variables
425
13.2.6
Conclusions
427
13.3
An Example: Internet and Television Use
428
13.3.1
Similarities and Differences
428
13.3.2
Interaction Variable: Economic and Political Interaction
432
Chapter
13
Review Questions
434
Chapter
13
Exercises
434
xi Contents
14
Omitted Explanatory Variables, Multicollinearity, and Irrelevant Explanatory Variables
439
Chapter
14
Prep Questions
439
14.1
Review
442
14.1.1
Unbiased Estimation Procedures
442
14.1.2
Correlated and Independent (Uncorrelated) Variables
444
14.2
Omitted Explanatory Variables
445
14.2.1
A Puzzle: Baseball Attendance
446
14.2.2
Goal of Multiple Regression Analysis
448
14.2.3
Omitted Explanatory Variables and Bias
448
14.2.4
Resolving the Baseball Attendance Puzzle
454
14.2.5
Omitted Variable Summary
456
14.3
Multicollinearity
457
14.3.1
Perfectly Correlated Explanatory Variables
457
14.3.2
Highly Correlated Explanatory Variables
458
14.3.3
Earmarks of Multicollinearity
464
14.4
Irrelevant Explanatory Variables
466
Chapter
14
Review Questions
469
Chapter
14
Exercises
469
15
Other Regression Statistics and Pitfalls
473
Chapter
15
Prep Questions
473
15.1
Two-Tailed Confidence Intervals
478
15.1.1
Confidence Interval Approach: Which Theories Are Consistent with the Data?
478
15.1.2
A Confidence Interval Example: Television Growth Rates
479
15.1.3
Calculating Confidence Intervals with Statistical Software
489
15.2
Coefficient of Determination, R-Squared (R2)
490
15.3
Pitfalls
494
15.3.1
Explanatory Variable Has the Same Value for All Observations
495
15.3.2
One Explanatory Variable Is a Linear Combination of Other Explanatory Variables
497
15.3.3
Dependent Variable Is a Linear Combination of Explanatory Variables
498
15.3.4
Outlier Observations
499
15.3.5
Dummy Variable Trap
500
Chapter
15
Review Questions
506
Chapter
15
Exercises
506
16
Heteroskedasticity
513
Chapter
16
Prep Questions
513
16.1
Review
515
16.1.1
Regression Model
515
16.1.2
Standard Ordinary Least Squares (OLS) Premises
516
16.1.3
Estimation Procedures Embedded within the Ordinary Least Squares (OLS) Estimation
Procedure
516
16.2
What Is Heteroskedasticity?
517
16.3
Heteroskedasticity and the Ordinary Least Squares (OLS) Estimation Procedure: The Consequences
519
16.3.1
The Mathematics
519
16.3.2
Our Suspicions
522
16.3.3
Confirming Our Suspicions
522
xii Contents
16.4
Accounting for Heteroskedasticity: An Example
526
16.5
Justifying the Generalized Least Squares (GLS) Estimation Procedure
536
16.6
Robust Standard Errors
538
Chapter
16
Review Questions
541
Chapter
16
Exercises
541
17
Autocorrelation (Serial Correlation)
545
Chapter
17
Prep Questions
545
17.1
Review
548
17.1.1
Regression Model
548
17.1.2
Standard Ordinary Least Squares (OLS) Premises
548
17.1.3
Estimation Procedures Embedded within the Ordinary Least Squares (OLS) Estimation
Procedure
548
17.1.4
Covariance and Independence
549
17.2
What Is Autocorrelation (Serial Correlation)?
551
17.3
Autocorrelation and the Ordinary Least Squares (OLS) Estimation Procedure: The Consequences
554
17.3.1
The Mathematics
554
17.3.2
Our Suspicions
559
17.3.3
Confirming Our Suspicions
560
17.4
Accounting for Autocorrelation: An Example
561
17.5
Justifying the Generalized Least Squares (GLS) Estimation Procedure
573
17.6
Robust Standard Errors
574
Chapter
17
Review Questions
575
Chapter
17
Exercises
575
18
Explanatory Variable/Error Term Independence Premise, Consistency, and Instrumental Variables
579
Chapter
18
Prep Questions
580
18.1
Review
583
18.1.1
Regression Model
583
18.1.2
Standard Ordinary Least Squares (OLS) Premises
583
18.1.3
Estimation Procedures Embedded within the Ordinary Least Squares (OLS) Estimation
Procedure
584
18.2
Taking Stock and a Preview: The Ordinary Least Squares (OLS) Estimation Procedure
584
18.3
A Closer Look at the Explanatory Variable/Error Term Independence Premise
586
18.4
Explanatory Variable/Error Term Correlation and Bias
588
18.4.1
Geometric Motivation
588
18.4.2
Confirming Our Logic
590
18.5
Estimation Procedures: Large and Small Sample Properties
592
18.5.1
Unbiased and Consistent Estimation Procedure
595
18.5.2
Unbiased but Inconsistent Estimation Procedure
596
18.5.3
Biased but Consistent Estimation Procedure
598
18.6
The Ordinary Least Squares (OLS) Estimation Procedure, and Consistency
601
18.7
Instrumental Variable (IV) Estimation Procedure: A Two Regression Procedure
602
18.7.1
Motivation of the Instrumental Variables Estimation Procedure
602
18.7.2
Mechanics
603
18.7.3
The Good Instrument Conditions
603
18.7.4
Justification of the Instrumental Variables Estimation Procedure
604
xiii Contents
Chapter
18 Review
Questions
607
Chapter
18
Exercises
607
19
Measurement Error and the Instrumental Variables Estimation Procedure
611
Chapter
19
Prep Questions
612
19.1
Introduction to Measurement Error
613
19.1.1
What Is Measurement Error?
614
19.1.2
Modeling Measurement Error
614
19.2
The Ordinary Least Squares (OLS) Estimation Procedure and Dependent Variable Measurement Error
614
19.3
The Ordinary Least Squares (OLS) Estimation Procedure and Explanatory Variable Measurement Error
617
19.3.1
Summary: Explanatory Variable Measurement Error Bias
620
19.3.2
Explanatory Variable Measurement Error: Attenuation (Dilution) Bias
621
19.3.3
Might the Ordinary Least Squares (OLS) Estimation Procedure Be Consistent?
622
19.4
Instrumental Variable (IV) Estimation Procedure: A Two Regression Procedure
623
19.4.1
Mechanics
623
19.4.2
The Good Instrument Conditions
624
19.5
Measurement Error Example: Annual, Permanent, and Transitory Income
625
19.5.1
Definitions and Theory
625
19.5.2
Might the Ordinary Least Squares (OLS) Estimation Procedure Suffer from a Serious Econometric
Problem?
627
19.6
Instrumental Variable (IV) Approach
628
19.6.1
The Mechanics
628
19.6.2
Comparison of the Ordinary Least Squares (OLS) and the Instrumental Variables (IV)
Approaches
630
19.6.3
Good Instrument Conditions Revisited
631
19.7
Justifying the Instrumental Variable (IV) Estimation Procedure
631
Chapter
19
Review Questions
633
Chapter
19
Exercises
634
20
Omitted Variables and the Instrumental Variable Estimation Procedure
637
Chapter
20
Prep Questions
637
20.1
Revisit Omitted Explanatory Variable Bias
639
20.1.1
Review of Our Previous Explanation of Omitted Explanatory Variable Bias
639
20.1.2
Omitted Explanatory Variable Bias and the Explanatory Variable/Error Term Independence
Premise
641
20.2
The Ordinary Least Squares Estimation Procedure, Omitted Explanatory Variable Bias, and
Consistency
642
20.3
Instrumental Variable Estimation Procedure: A Two Regression Estimation Procedure
644
20.3.1
Mechanics
644
20.3.2
The Good Instrument Conditions
645
20.4
Omitted Explanatory Variables Example:
2008
Presidential Election
646
20.5
Instrument Variable (IV) Application:
2008
Presidential Election
648
20.5.1
The Mechanics
648
20.5.2
Good Instrument Conditions Revisited
649
20.6
Justifying the Instrumental Variable (IV) Estimation Procedure
650
Chapter
20
Review Questions
654
Chapter
20
Exercises
654
xiv Contents
21
Panel Data and Omitted Variables
657
Chapter
21
Prep Questions
657
21.1
Taking Stock: Ordinary Least Squares (OLS) Estimation Procedure
660
21.1.1
Standard Ordinary Least Squares (OLS) Premises
660
21.2
Preview: Panel Data Examples and Strategy
661
21.3
First Differences and Fixed Effects (Dummy Variables)
662
21.3.1
Math Quiz Score Model
663
21.3.2
Ordinary Least Squares (OLS) Pooled Regression
665
21.3.3
First Differences
668
21.3.4
Cross-sectional Fixed Effects (Dummy Variables)
670
21.4
Period Fixed Effects (Dummy Variables)
673
21.4.1
Chemistry Score Model
674
21.4.2
Ordinary Least Squares (OLS) Pooled Regression
676
21.4.3
Period Fixed Effects (Dummy Variables)
679
21.5
Cross-sectional Random Effects
681
21.5.1
Art Project Model
682
21.5.2
Ordinary Least Squares (OLS) Pooled Regression
684
21.5.3
Cross-sectional Random Effects
686
21.6
Random Effects Critical Assumptions
688
Chapter
21
Review Questions
688
Chapter
21
Exercises
688
22
Simultaneous Equations Models—Introduction
693
Chapter
22
Prep Questions
694
22.1
Review: Explanatory Variable/Error Term Correlation
696
22.2
Simultaneous Equations Models: Demand and Supply
698
22.2.1
Endogenous versus Exogenous Variables
699
22.2.2
Single Equation versus Simultaneous Equations Models
699
22.2.3
Demand Model
700
22.2.4
Supply Model
703
22.2.5
Summary: Endogenous Explanatory Variable Problem
706
22.3
An Example: The Market for Beef
706
22.3.1
Demand and Supply Models
706
22.3.2
Ordinary Least Squares (OLS) Estimation Procedure
707
22.3.3
Reduced Form (RF) Estimation Procedure: The Mechanics
708
22.3.4
Comparing Ordinary Least Squares (OLS) and Reduced Form (RF) Estimates
715
22.4
Justifying the Reduced Form (RF) Estimation Procedure
715
22.5
Two Paradoxes
717
22.6
Resolving the Two Paradoxes: Coefficient Interpretation Approach
717
22.6.1
Review: Goal of Multiple Regression Analysis and the Interpretation of the Coefficients
717
22.6.2
Paradox: Demand Model Price Coefficient Depends on the Reduced Form (RF) Feed Price
Coefficients
720
22.6.3
Paradox: Supply Model Price Coefficient Depends on the Reduced Form (RF) Income
Coefficients
722
22.7
The Coefficient Interpretation Approach: A Bonus
724
Chapter
22
Review Questions
725
Chapter
22
Exercises
725
Appendix
22.1:
Algebraic Derivation of the Reduced Form Equations
730
xv Contents
23
Simultaneous Equations Models—Identification
733
Chapter
23
Prep Questions
734
23.1
Review
737
23.1.1
Demand and Supply Models
737
23.1.2
Ordinary Least Squares (OLS) Estimation Procedure
738
23.2
Two-Stage Least Squares (TSLS): An Instrumental Variable (IV) Two-Step Approach—A Second Way to Cope
with Simultaneous Equations Models
741
23.2.1
First Stage: Exogenous Explanatory Variable(s) Used to Estimate the Endogenous Explanatory
Variable(s)
742
23.2.2
Second Stage: In the Original Model, the Endogenous Explanatory Variable Replaced with Its
Estimate
743
23.3
Comparison of Reduced Form (RF) and Two-Stage Least Squares (TSLS) Estimates
744
23.4
Statistical Software and Two-Stage Least Squares (TSLS)
745
23.5
Identification of Simultaneous Equations Models: Order Condition
746
23.5.1
Taking Stock
746
23.5.2
Underidentification
749
23.5.3
Overidentification
757
23.5.4
Overidentification and Two-Stage Least Squares (TSLS)
760
23.6
Summary of Identification Issues
762
Chapter
23
Review Questions
762
Chapter
23
Exercises
762
24
Binary and Truncated Dependent Variables
767
Chapter
24
Prep Questions
767
24.1
Introduction
769
24.2
Binary Dependent Variables
770
24.2.1
Electoral College: Red and Blue States
770
24.2.2
Linear Probability Model
772
24.2.3
Probit
Probability Model: Correcting the Linear Model s Intrinsic Problems
774
24.3
Truncated (Censored) Dependent Variables
783
24.3.1
Ordinary Least Squares (OLS) Estimation Procedure
787
24.3.2
Tobit Estimation Procedure
787
Chapter
24
Review Questions
789
Chapter
24
Exercises
789
25
Descriptive Statistics, Probability, and Random Variables—A Closer Look
793
Chapter
25
Prep Questions
794
25.1
Descriptive Statistics: Other Measures of the Distribution Center
794
25.1.1
Measure of the Distribution Center: Mode
796
25.1.2
Measure of the Distribution Center: Median
796
25.1.3
Relationship between the Mean and Median
798
25.2
Event Trees: A Tool to Calculate Probabilities
798
25.3
Calculating the Probability of a Combination of Different Outcomes
806
25.4
Nonconditional, Conditional, and Joint Probabilities
807
25.5
Conditional/Joint Probability Relationship
807
25.6
The Monty Hall Problem: Mathematicians Eat Humble Pie
809
xvi Contents
25.7
Correlation
816
25.7.1
Correlated Events
816
25.7.2
Correlated Random Variables and Covariance
818
25.8
Independence
820
25.8.1
Independent Events
820
25.8.2
Independent Random Variables and Covariance
821
25.9
Summary of Correlation and Independence
825
25.9.1
Correlation
825
25.9.2
Independence
826
25.10
Describing Probability Distributions of Continuous Random Variables
827
Chapter
25
Review Questions
827
Chapter
25
Exercises
827
26
Estimating the Mean of a Population
833
Chapter
26
Prep Questions
834
26.1
Estimation Procedure for the Population Mean
834
26.2
Estimated Mean s Probability Distribution
839
26.2.1
Measure of the Probability Distribution s Center: Mean
839
26.2.2
Measure of the Probability Distribution s Spread: Variance
840
26.3
Taking Stock: What We Know versus What Clint Knows
844
26.4
Estimation Procedures: Importance of the Probability Distribution s Mean (Center) and Variance
(Spread)
845
26.5
Strategy to Estimate the Variance of the Estimated Mean s Probability Distribution
846
26.6
Step
1:
Estimate the Variance of the Population
848
26.6.1
First Attempt: Variance of Clint s Four Numerical Values Based on the Actual
Population Mean
848
26.6.2
Second Attempt: Variance of Clint s Four Numerical Values Based on the Estimated Population
Mean
850
26.6.3
Third Attempt: Adjusted Variance of Clint s Four Numerical Values Based on the Estimated
Population Mean
853
26.7
Step
2:
Use the Estimated Variance of the Population to Estimate the Variance of the Estimated Mean s
Probability Distribution
854
26.8
Clint s Assessment of the Key West Tourist Bureau s Claim
855
26.9
Normal Distribution and the Student ¿-Distribution
856
26.10
Tying Up a Loose End: Degrees of Freedom
859
Chapter
26
Review Questions
861
Chapter
26
Exercises
861
Index
867
|
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| discipline | Wirtschaftswissenschaften |
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| indexdate | 2024-07-10T00:56:15Z |
| institution | BVB |
| isbn | 9780262019224 |
| language | English |
| lccn | 2012049424 |
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| spelling | Westhoff, Frank H. 1946- Verfasser (DE-588)171355156 aut An introduction to econometrics a self-contained approach Frank Westhoff Cambridge, Mass.[u.a.] MIT Press 2013 XVI, 874 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes index. Econometrics Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s b DE-604 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026862007&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Westhoff, Frank H. 1946- An introduction to econometrics a self-contained approach Econometrics Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4123623-3 |
| title | An introduction to econometrics a self-contained approach |
| title_auth | An introduction to econometrics a self-contained approach |
| title_exact_search | An introduction to econometrics a self-contained approach |
| title_full | An introduction to econometrics a self-contained approach Frank Westhoff |
| title_fullStr | An introduction to econometrics a self-contained approach Frank Westhoff |
| title_full_unstemmed | An introduction to econometrics a self-contained approach Frank Westhoff |
| title_short | An introduction to econometrics |
| title_sort | an introduction to econometrics a self contained approach |
| title_sub | a self-contained approach |
| topic | Econometrics Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Econometrics Ökonometrie Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026862007&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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