Principles of econometrics:
Gespeichert in:
| Vorheriger Titel: | Hill, Rufus Carter Undergraduate econometrics |
|---|---|
| Beteiligte Personen: | , , |
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2008
|
| Ausgabe: | 3. ed |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016284757&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Bis 2. Aufl. u.d.T.: Hill, Rufus Carter: Undergraduate econometrics. - Includes index |
| Umfang: | XXVII, 579 S. graph. Darst. |
| ISBN: | 9780471723608 |
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| 100 | 1 | |a Hill, Rufus Carter |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Principles of econometrics |c R. Carter Hill ; William E. Griffiths ; Guay C. Lim |
| 250 | |a 3. ed | ||
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2008 | |
| 300 | |a XXVII, 579 S. |b graph. Darst. | ||
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| 500 | |a Bis 2. Aufl. u.d.T.: Hill, Rufus Carter: Undergraduate econometrics. - Includes index | ||
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| 650 | 4 | |a Econometrics | |
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| 780 | 0 | 0 | |i Früher u.d.T. |a Hill, Rufus Carter |t Undergraduate econometrics |
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Datensatz im Suchindex
| _version_ | 1819348881635278848 |
|---|---|
| adam_text | Contents
Preface ¡x
Chapter
1 An
Introduction to Econometrics
1
1.1
Why Study Econometrics?
1
1.2
What is Econometrics About?
2
1.2.1
Some Examples
3
1.3
The Econometric Model
4
1.4
How Do We Obtain Data?
5
1.4.1
Experimental Data
5
1.4.2
Nonexperimental Data
5
1.5
Statistical Inference
6
1.6
A Research Format
η
Chapter
2
The Simple Linear Regression Model
8
Learning Objectives
8
Keywords
9
2.1
An Economic Model
9
2.2
An Econometric Model
12
2.2.1
Introducing the Error Term
15
2.3
Estimating the Regression Parameters
18
2.3.1
The Least Squares Principle
20
2.3.2
Estimates for the Food Expenditure Function
22
2.3.3
Interpreting the Estimates
22
2.3.3a Elasticities
23
2.3.3b Prediction
24
2.3.3c Computer Output
24
2.3.4
Other Economic Models
24
2.4
Assessing the Least Squares Estimators
26
2.4.1
The Estimator b2
27
2.4.2
The Expected Values of b and b2
27
2.4.3
Repeated Sampling
28
2.4.4
The Variances and Covariance of b and fe2
29
2.5
The Gauss-Markov Theorem
31
2.6
The Probability Distributions of the Least Squares Estimators
32
2.7
Estimating the Variance of the Error Term
33
2.7.1
Estimating the Variances and Covariances of the Least Squares
Estimators
34
m CONTENTS
2.7.2
Calculations for the Food Expenditure Data
35
2.8
Exercises
36
2.8.1
Problems
36
2.8.2
Computer Exercises
39
Appendix 2A Derivation of the Least Squares Estimates
42
Appendix 2B Deviation from the Mean Form of
Ъг
43
Appendix 2C b2 is a Linear Estimator
44
Appendix 2D Derivation of Theoretical Expression for b2
44
Appendix 2E Deriving the Variance of
Ъг
45
Appendix 2F Proof of the Gauss-Markov Theorem
46
Chapter
3
Interval Estimation and Hypothesis Testing
48
Learning Objectives
48
Keywords
48
3.1
Interval Estimation
49
3.1.1
The
ŕ-distribution
49
3.1.2
Obtaining Interval Estimates
51
3.1.3
An Illustration
52
3.1.4
The Repeated Sampling Context
53
3.2
Hypothesis Tests
54
3.2.1
The Null Hypothesis
55
3.2.2
The Alternative Hypothesis
55
3.2.3
The Test Statistic
55
3.2.4
The Rejection Region
55
3.2.5
A Conclusion
56
3.3
Rejection Regions for Specific Alternatives
56
3.3.1
One-Tail Tests with Alternative Greater Than
(>) 56
3.3.2
One-Tail Tests with Alternative Less Than
(<) 57
3.3.3
Two-Tail Tests with Alternative Not Equal To
(ψ)
58
3.4
Examples of Hypothesis Tests
59
3.4.1
Right-Tail Tests
59
3.4.1a One-Tail Test of Significance
59
3.4.1b One-Tail Test of an Economic Hypothesis
60
3.4.2
Left-Tail Tests
61
3.4.3
Two-Tail Tests
62
3.4.3a Two-Tail Test of an Economic Hypothesis
62
3.4.3b Two-Tail Test of Significance
63
3.5
The p-value
64
3.5.1
p-value for a Right-Tail Test
65
3.5.2
p-value for a Left-Tail Test
66
3.5.3
p-value for a Two-Tail Test
66
3.5.4
p-value for a Two-Tail Test of Significance
67
3.6
Exercises
68
3.6.1
Problems
68
3.6.2
Computer Exercises
69
Appendix
ЗА
Derivation of the
ŕ-Distribution
72
Appendix 3B Distribution of the r-Statistic Under H
73
CONTENTS xvii
Chapter
4
Prediction, Goodness-of-Fit, and Modeling Issues
75
Learning Objectives
75
Keywords
76
4.1
Least Squares Prediction
76
4.1.1
Prediction in the Food Expenditure Model
79
4.2
Measuring Goodness-of-Fit
80
4.2.1
Correlation Analysis
81
4.2.2
Correlation Analysis and R2
82
4.2.3
The Food Expenditure Example
82
4.2.4
Reporting the Results
83
4.3
Modeling Issues
84
4.3.1
The Effects of Scaling the Data
84
4.3.2
Choosing a Functional Form
86
4.3.3
The Food Expenditure Model
87
4.3.4
Are the Regression Errors Normally Distributed?
89
4.3.5
Another Empirical Example
90
4.4
Log-Linear Models
93
4.4.1
A Growth Model
94
4.4.2
A Wage Equation
94
4.4.3
Prediction in the Log-Linear Model
95
4.4.4
A Generalized R2 Measure
96
4.4.5
Prediction Intervals in the Log-Linear Model
96
4.5
Exercises
97
4.5.1
Problems
97
4.5.2
Computer Exercises
98
Appendix 4A Development of a Prediction Interval
101
Appendix 4B The Sum of Squares Decomposition
103
Appendix 4C The Log-Normal Distribution
103
Chapter
5
The Multiple Regression Model
105
Learning Objectives
105
Keywords
105
5.1
Introduction
106
5.1.1
The Economic Model
106
5.1.2
The Econometric Model
108
5.1.2a The General Model
109
5.1.2b The Assumptions of the Model
110
5.2
Estimating the Parameters of the Multiple Regression Model 111
5.2.1
Least Squares Estimation Procedure 111
5.2.2
Least Squares Estimates Using Hamburger Chain Data
112
5.2.3
Estimation of the Error Variance
σ2
114
5.3
Sampling Properties of the Least Squares Estimator
115
5.3.1
The Variances and Covariances of the Least Squares Estimators
115
5.3.2
The Properties of the Least Squares Estimators Assuming
Normally Distributed Errors
117
5.4
Interval Estimation
118
5.5
Hypothesis Testing for a Single Coefficient
120
5.5.1
Testing the Significance of a Single Coefficient
120
xviii CONTENTS
5.5.2
One-Tail Hypothesis Testing for a Single Coefficient
122
5.5.2a Testing For Elastic Demand
122
5.5.2b Testing Advertising Effectiveness
123
5.6
Measuring Goodness-of-Fit
124
5.6.1
Reporting the Regression Results
126
5.7
Exercises
127
5.7.1
Problems
127
5.7.2
Computer Exercises
129
Appendix 5A Derivation of Least Squares Estimators
133
Chapter
6
Further Inference in the Multiple Regression Model
134
Learning Objectives
134
Keywords
135
6.1
The F-Test
135
6.1.1
The Relationship Between t- and F-Tests
138
6.2
Testing the Significance of a Model
138
6.3
An Extended Model
140
6.4
Testing Some Economic Hypotheses
142
6.4.1
The Significance of Advertising
142
6.4.2
The Optimal Level of Advertising
142
6.4.2a A One-Tail Test with More than One Parameter
144
6.4.3
Using Computer Software
145
6.5
The Use of
Nonsample
Information
146
6.6
Model Specification
148
6.6.1
Omitted Variables
149
6.6.2
Irrelevant Variables
150
6.6.3
Choosing the Model
151
6.6.3a The RESET Test
151
6.7
Poor Data, Collinearity, and Insignificance
153
6.7.1
The Consequences of Collinearity
153
6.7.2
An Example
154
6.7.3
Identifying and Mitigating Collinearity
155
6.8
Prediction
156
6.9
Exercises
157
6.9.1
Problems
157
6.9.2
Computer Exercises
160
Appendix 6A Chi-Square and F-Tests: More Details
163
Appendix 6B Omitted-Variable Bias: A Proof
165
Chapter
7
Nonlinear Relationships
166
Learning Objectives
166
Keywords
166
7.1
Polynomials
167
7.1.1
Cost and Product Curves
167
7.1.2
A Wage Equation
169
7.2
Dummy Variables
170
7.2.1
Intercept Dummy Variables
171
7.2.1a Choosing the Reference Group
172
CONTENTS xix
7.2.2
Slope Dummy Variables
172
7.2.3
An Example: The University Effect on House Prices
174
7.3
Applying Dummy Variables
175
7.3.1
Interactions between Qualitative Factors
175
7.3.2
Qualitative Factors with Several Categories
177
7.3.3
Testing the Equivalence of Two Regressions
179
7.3.4
Controlling for Time
181
7.3.4a Seasonal Dummies
181
7.3.4b Annual Dummies
182
7.3.4c Regime Effects
182
7.4
Interactions Between Continuous Variables
182
7.5
Log-Linear Models
184
7.5.1
Dummy Variables
185
7.5.1a A Rough Calculation
185
7.5.1b An Exact Calculation
185
7.5.2
Interaction and Quadratic Terms
186
7.6
Exercises
186
7.6.1
Problems
186
7.6.2
Computer Exercises
190
Appendix
7
A Details of Log-Linear Model Interpretation
195
Chapter
8
Heteroskedasticity
197
Learning Objectives
197
Keywords
197
8.1
The Nature of Heteroskedasticity
197
8.2
Using the Least Squares Estimator
201
8.3
The Generalized Least Squares Estimator
202
8.3.1
Transforming The Model
203
8.3.2
Estimating the Variance Function
205
8.3.3
A Heteroskedastic Partition
208
8.4
Detecting Heteroskedasticity
211
8.4.1
Residual Plots
211
8.4.2
The Goldfeld-Quandt Test
211
8.4.3
Testing the Variance Function
212
8.4.3a The White Test
215
8.4.3b Testing the Food Expenditure Example
215
8.5
Exercises
216
8.5.1
Problems
216
8.5.2
Computer Exercises
219
Appendix 8A Properties of the Least Squares Estimator
222
Appendix 8B Variance Function Tests for Heteroskedasticity
224
Chapter
9
Dynamic Models, Autocorrelation and Forecasting
226
Learning Objectives
226
Keywords
226
9.1
Introduction
227
9.2
Lags in the Error Term: Autocorrelation
230
9.2.1
Area Response Model for Sugar Cane
230
xx CONTENTS
9.2.2 First Order Autoregressive
Errors
231
9.3
Estimating
an
AR(1)
Error
Model 235
9.3.1
Least
Squares
Estimation
235
9.3.2
Nonlinear Least Squares Estimation
236
9.3.2a Generalized Least Squares Estimation
237
9.3.3
Estimating a More General Model
237
9.4
Testing for Autocorrelation
239
9.4.1
Residual Correlogram
239
9.4.2
A
Lagrange
Multiplier Test
242
9.4.3
Recapping and Looking Forward
243
9.5
An Introduction to Forecasting:
Autoregressive
Models
244
9.6
Finite Distributed Lags
248
9.7
Autoregressive
Distributed Lag Models
250
9.8
Exercises
253
9.8.1
Problems
253
9.8.2
Computer Exercises
255
Appendix 9A Generalized Least Squares Estimation
259
Appendix 9B The
Durbin-
Watson Test
261
9B.1 The
Durbin-
Watson Bounds Test
263
Appendix 9C Deriving ARDL Lag Weights
264
9C.1 The Geometric Lag
264
9C.2 Lag Weights for More General ARDL Models
265
Appendix 9D ForecastingiExponential Smoothing
266
Chapter
10
Random Regressors and Moment Based Estimation
268
Learning Objectives
268
Keywords
269
10.1
Linear Regression with Random x s
270
10.1.1
The Small Sample Properties of the Least Squares Estimator
270
10.1.2
Asymptotic Properties of the Least Squares Estimator:
χ
Not
Random
271
10.1.3
Asymptotic Properties of the Least Squares Estimator:
χ
Random
272
10.1.4
Why Least Squares Fails
273
10.2
Cases in Which
χ
and
e are
Correlated
274
10.2.1
Measurement Error
274
10.2.2
Omitted Variables
275
10.2.3
Simultaneous Equations Bias
276
10.2.4
Lagged Dependent Variable Models with Serial Correlation
276
10.3
Estimators Based on the Method of Moments
276
10.3.1
Method of Moments Estimation of a Population Mean and
Variance
277
10.3.2
Method of Moments Estimation in the Simple Linear
Regression Model
278
10.3.3
Instrumental Variables Estimation in the Simple Linear
Regression Model
278
10.3.3a The Importance of Using Strong Instruments
279
10.3.3b An Illustration Using Simulated Data
280
10.3.3c An Illustration Using a Wage Equation
281
CONTENTS xxi
10.3.4 Instrumental Variables
Estimation
with Surplus
Instruments 282
10.3.4a An Illustration
Using Simulated Data
284
10.3.4b An Illustration
Using a Wage Equation
284
10.3.5
Instrumental Variables Estimation in a General Model
285
10.3.5a Hypothesis Testing with Instrumental Variables
Estimates
286
10.3.5b Goodness-of-Fit with Instrumental Variables
Estimates
286
10.4
Specification Tests
286
10.4.1
The Hausman Test for Endogeneity
287
10.4.2
Testing for Weak Instruments
288
10.4.3
Testing Instrument Validity
289
10.4.4
Numerical Examples Using Simulated Data
290
10.4.4a The Hausman Test
290
10.4.4b Test for Weak Instraments
290
10.4.4c Testing Surplus Moment Conditions
291
10.4.5
Specification Tests for the Wage Equation
291
10.5
Exercises
292
10.5.1
Problems
292
10.5.2
Computer Exercises
293
Appendix 10A Conditional and Iterated Expectations
297
10A.1 Conditional Expectations
297
10A.2 Iterated Expectations
298
10A.3 Regression Model Applications
298
Appendix 10B The Inconsistency of Least Squares
299
Appendix 10C The Consistency of the
W
Estimator
300
Appendix 10D The Logic of the Hausman Test
301
Chapter
11
Simultaneous Equations Models
303
Learning Objectives
303
Keywords
303
11.1
A Supply and Demand Model
304
11.2
The Reduced Form Equations
306
11.3
The Failure of Least Squares
307
11.4
The Identification Problem
307
11.5
Two-Stage Least Squares Estimation
309
11.5.1
The General Two-Stage Least Squares Estimation
Procedure
310
11.5.2
The Properties of the Two-Stage Least Squares Estimator
311
11.6
An Example of Two-Stage Least Squares Estimation
311
11.6.1
Identification
312
11.6.2
The Reduced Form Equations
312
11.6.3
The Structural Equations
313
11.7
Supply and Demand at the Fulton Fish Market
314
11.7.1
Identification
315
11.7.2
The Reduced Form Equations
315
11.7.3
Two-Stage Least Squares Estimation of Fish Demand
317
11.8
Exercises
318
11.8.1
Problems
318
CONTENTS
11.8.2 Computer
Exercises
319
Appendix HA An
Algebraic Explanation of the Failure of
Least Squares
323
Chapter
12
Nonstationary Time-Series Data and Cointegration
325
Learning Objectives
325
Keywords
325
12.1
Stationary and Nonstationary Variables
326
12.1.1
The First-Order
Autoregressive
Model
328
12.1.2
Random Walk Models
331
12.2
Spurious Regressions
333
12.3
Unit Root Tests for Stationarity
335
12.3.1
Dickey-Fuller Test
1
(No Constant and No Trend)
335
12.3.2
Dickey-Fuller Test
2
(With Constant But No Trend)
335
12.3.3
Dickey-Fuller Test
3
(With Constant and With Trend)
336
12.3.4
The Dickey-Fuller Testing Procedure
336
12.3.5
The Dickey-Fuller Tests: An Example
337
12.3.6
Order of Integration
338
12.4
Cointegration
339
12.4.1
An Example of a Cointegration Test
340
12.5
Regression When There is No Cointegration
340
12.5.1
First Difference Stationary
341
12.5.2
Trend Stationary
342
12.6
Exercises
342
12.6.1
Problems
342
12.6.2
Computer Exercises
344
Chapter
13
VEC
and
VAR
Models:
An Introduction to Macroeconometrics
346
Learning Objectives
346
Keywords
346
13.1
VEC
and
VAR
Models
347
13.2
Estimating a Vector Error Correction Model
349
13.2.1
Example
349
13.3
Estimating
a VAR
Model
351
13.4
Impulse Responses and Variance Decompositions
352
13.4.1
Impulse Response Functions
352
13.4.1a The Univariate Case
352
13.4.1b The Bivariate Case
353
13.4.2
Forecast Error Variance Decompositions
355
13.4.2a Univariate Analysis
355
13.4.2b Bivariate Analysis
356
13.4.2c The General Case
357
13.5
Exercises
357
13.5.1
Problems
357
13.5.2
Computer Exercises
358
Appendix 13AThe Identification Problem
361
CONTENTS xxiii
Chapter
14
Time-Varying Volatility and ARCH Models:
An Introduction to Financial Econometrics
363
Learning Objectives
З6З
Keywords
363
14.1
The ARCH Model
364
14.1.1
Conditional and Unconditional Forecasts
365
14.2
Time-Varying Volatility
365
14.3
Testing, Estimating and Forecasting
369
14.3.1
Testing for ARCH Effects
369
14.3.2
Estimating ARCH Models
369
14.3.3
Forecasting Volatility
370
14.4
Extensions
371
14.4.1
The GARCH Model—Generalized ARCH
371
14.4.2
Allowing for an Asymmetric Effect
372
14.4.3
GARCH-in-Mean and Time-Varying Risk Premium
374
14.5
Exercises
375
14.5.1
Problems
375
14.5.2
Computer Exercises
376
Chapter
15
Panel Data Models
382
Learning Objectives
382
Keywords
382
15.1
Granfeld s Investment Data
384
15.2
Sets of Regression Equations
385
15.3
Seemingly Unrelated Regressions
387
15.3.1
Separate or Joint Estimation?
389
15.3.2
Testing Cross-Equation Hypotheses
390
15.4
The Fixed Effects Model
391
15.4.1
A Dummy Variable Model
391
15.4.2
The Fixed Effects Estimator
393
15.4.3
Fixed Effects Estimation Using
a Microeconomic
Panel
396
15.5
The Random Effects Model
398
15.5.1
Error Term Assumptions
399
15.5.2
Testing for Random Effects
400
15.5.3
Estimation of the Random Effects Model
401
15.5.4
An Example Using the NLS Data
402
15.5.5
Comparing Fixed and Random Effects Estimators
403
15.5.5a Endogeneity in the Random Effects Model
403
15.5.5b The Fixed Effects Estimator in a Random
Effects Model
404
15.5.5c A Hausman Test
404
15.6
Exercises
406
15.6.1
Problems
406
15.6.2
Computer Exercises
408
Appendix 15A Estimation of Error Components
415
xxiv CONTENTS
Chapter
16 Qualitative and Limited
Dependent
Variable Models 417
Learning Objectives
417
Keywords
417
16.1
Models with Binary Dependent Variables
418
16.1.1
The Linear Probability Model
419
16.1.2
The
Probit
Model
421
16.1.3
Interpretation of the
Probit
Model
422
16.1.4
Maximum Likelihood Estimation of the
Probit
Model
423
16.1.5
An Example
424
16.2
The Logit Model for Binary Choice
425
16.3
Multinomial Logit
426
16.3.1
Multinomial Logit Choice Probabilities
427
16.3.2
Maximum Likelihood Estimation
427
16.3.3
Post-Estimation Analysis
428
16.3.4
An Example
429
16.4
Conditional Logit
431
16.4.1
Conditional Logit Choice Probabilities
431
16.4.2
Post-Estimation Analysis
432
16.4.3
An Example
433
16.5
Ordered Choice Models
433
16.5.1
Ordinal
Probit
Choice Probabilities
434
16.5.2
Estimation and Interpretation
435
16.5.3
An Example
437
16.6
Models for Count Data
437
16.6.1
Maximum Likelihood Estimation
438
16.6.2
Interpretation in the
Poisson
Regression Model
439
16.6.3
An Example
440
16.7
Limited Dependent Variables
441
16.7.1
Censored Data
441
16.7.2
A Monte Carlo Experiment
442
16.7.3
Maximum Likelihood Estimation
444
16.7.4
Tobit Model Interpretation
445
16.7.5
An Example
446
16.7.6
Sample Selection
447
16.7.6a The Econometric Model
448
16.7.6b Heckit Example: Wages of Married Women
449
16.8
Exercises
450
Chapter
17
Writing an Empirical Research Report,
and Sources of Economic Data
457
17.1
Selecting a Topic for an Economics Project
457
17.1.1
Choosing a Topic
457
17.1.2
Writing an Abstract
458
17.2
A Format for Writing a Research Report
458
17.3
Sources of Economic Data
460
17.3.1
Links to Economic Data on the Internet
460
17.3.2
Traditional Sources of Economic Data
461
17.3.3
Interpreting Economic Data
461
17.4
Exercises
462
CONTENTS xxv
Appendix A Review of Math Essentials
463
Learning Objectives
463
Keywords
463
A.I Summation
464
A.2 Some Basics
465
A.2.1 Numbers
465
A.2.2 Exponents
466
A.2.3 Scientific Notation
466
A.2.4 Logarithms and the Number
e
466
A.3 Linear Relationships
468
A.3.1 Elasticity
469
A.4 Nonlinear Relationships
470
A.4.1 Quadratic Function
471
A.4.2 Cubic Function
471
A.4.3 Reciprocal Function
472
A.4.4 Log-Log Function
473
A.4.5 Log-Linear Function
473
A.4.6 Approximating Logarithms
473
A.4.7 Approximating Logarithms in the Log-Linear Model
474
A.4.8 Linear-Log Function
475
A.5 Exercises
476
Appendix
В
Review of Probability Concepts
478
Learning Objectives
478
Keywords
479
B.I Random Variables
479
B.2 Probability Distributions
480
B.3 Joint, Marginal and Conditional Probability Distributions
483
B.3.1 Marginal Distributions
484
B.3.2 Conditional Probability
484
B.3.3 A Simple Experiment
486
B.4 Properties of Probability Distributions
487
B.4.1 Mean, Median and Mode
487
B.4.2 Expected Values of Functions of a Random Variable
488
B.4.3 Expected Values of Several Random Variables
490
B.4.4 The Simple Experiment Again
492
B.5 Some Important Probability Distributions
493
B.5.1 The Normal Distribution
493
B.5.2 The Chi-Square Distribution
495
B.5.3 The i-Distribution
495
B.5.4 The F-Distribution
496
B.6 Exercises
497
Appendix
С
Review of Statistical Inference
501
Learning Objectives
501
Keywords
502
xxvi CONTENTS
Cl A
Sample
of Data
502
C.2 An Econometric Model
504
C.3 Estimating the Mean of a Population
504
C.3.1 The Expected Value of
Τ
506
C.3.2 The Variance of
Τ
_ 506
C.3.3 The Sampling Distribution of
Y
507
C.3.
4
The Central Limit Theorem
508
C.3.5 Best Linear Unbiased Estimation
509
C.4 Estimating the Population Variance and Other Moments
509
C.4.1 Estimating the Population Variance
510
C.4.2 Estimating Higher Moments
511
C.4.3 The Hip Data
511
C.4.4 Using the Estimates
512
C.5 Interval Estimation
512
C.5.1 Interval Estimation:
σ2
Known
513
C.5.2 A Simulation
514
C.5.3 Interval Estimation:
σ2
Unknown
515
C.5.4 A Simulation (Continued)
517
C.5.
5
Interval Estimation Using the Hip Data
517
C.6 Hypothesis Tests About a Population Mean
517
C.6.1 Components of Hypothesis Tests
517
С.б.іа
The Null Hypothesis
518
C.6.1b The Alternative Hypothesis
518
С.б.іс
The Test Statistic
518
C.6.1d The Rejection Region
519
С.б.іе
A Conclusion
519
C.6.2 One-Tail Tests with Alternative Greater Than
(>) 519
C.6.3 One-Tail Tests with Alternative Less Than
«) 519
C.6.4 Two-Tail Tests with Alternative Not Equal To
(φ)
520
С.б.о
Example of a One-Tail Test Using the Hip Data
520
C.6.6 Example of a Two-Tail Test Using Hip Data
521
C.6.7 The /7-Value
522
C.6.8 A Comment on Stating Null and Alternative Hypotheses
523
C.6.9 Type I and Type
Π
Errors
524
C.6.
10
A Relationship Between Hypothesis Testing and Confidence
Intervals
525
C.7 Some Other Useful Tests
525
C.7.1 Testing the Population Variance
525
C.7.2 Testing the Equality of Two Population Means
526
C.7.3 Testing the Ratio of Two Population Variances
527
C.7
.4
Testing the Normality of a Population
527
C.8 Introduction to Maximum Likelihood Estimation
528
C.8.1 Inference with Maximum Likelihood Estimators
532
C.8.2 The Variance of the Maximum Likelihood Estimator
533
C.8.3 The Distribution of the Sample Proportion
534
C.8.4 Asymptotic Test Procedures
536
C.8.4a
The Likelihood Ratio (LR) Test
536
C.8.4b The
Wald Test 538
C.8.4C The
Lagrange
Multiplier (LM) Test
539
CONTENTS xxvii
C.9
Algebraic
Supplements (Optional) 541
C.9.1
Derivation of Least
Squares
Estimator
541
C.9.2 Best Linear
Unbiased Estimation
543
CIO
Exercises
544
Appendix
D
Answers to Selected Exercises
548
Appendix
E
Tables
572
Table
1
Cumulative Probabilities for the Standard Normal Distribution
572
Table
2
Percentiles for the f-Distribution
573
Table
3
Percentiles for the Chi-square Distribution
574
Table
4
95th
Percentik
for the F-Distribution
575
Table
5
99th Percentile for the ^-Distribution
576
Index
577
|
| any_adam_object | 1 |
| author | Hill, Rufus Carter Griffiths, William E. 1946- Lim, Guay C. 1951- |
| author_GND | (DE-588)135543134 (DE-588)129060518 |
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| callnumber-first | H - Social Science |
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| dewey-sort | 3330.015195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| edition | 3. ed |
| format | Book |
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| genre_facet | Lehrbuch |
| id | DE-604.BV023081745 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:08:12Z |
| institution | BVB |
| isbn | 9780471723608 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016284757 |
| oclc_num | 263419458 |
| open_access_boolean | |
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| physical | XXVII, 579 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Wiley |
| record_format | marc |
| spellingShingle | Hill, Rufus Carter Griffiths, William E. 1946- Lim, Guay C. 1951- Principles of econometrics Econometrics Anwendungssoftware (DE-588)4120906-0 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4120906-0 (DE-588)4132280-0 (DE-588)4123623-3 |
| title | Principles of econometrics |
| title_auth | Principles of econometrics |
| title_exact_search | Principles of econometrics |
| title_full | Principles of econometrics R. Carter Hill ; William E. Griffiths ; Guay C. Lim |
| title_fullStr | Principles of econometrics R. Carter Hill ; William E. Griffiths ; Guay C. Lim |
| title_full_unstemmed | Principles of econometrics R. Carter Hill ; William E. Griffiths ; Guay C. Lim |
| title_old | Hill, Rufus Carter Undergraduate econometrics |
| title_short | Principles of econometrics |
| title_sort | principles of econometrics |
| topic | Econometrics Anwendungssoftware (DE-588)4120906-0 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Econometrics Anwendungssoftware Ökonometrie Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016284757&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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