A guide to modern econometrics:
This_highly successful text focuses on exploring alternative techniques, combined with a practical emphasis, A guide to alternative techniques with the emphasis on the intuition behind the approaches and their practical reference, this new edition builds on the strengths of the second edition and br...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester [u.a.]
Wiley
2011
|
| Ausgabe: | 3. ed., reprinted |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | This_highly successful text focuses on exploring alternative techniques, combined with a practical emphasis, A guide to alternative techniques with the emphasis on the intuition behind the approaches and their practical reference, this new edition builds on the strengths of the second edition and brings the text completely up-to-date. |
| Beschreibung: | Literaturverz. S. [437] - 449 |
| Beschreibung: | XV, 472 S. graph. Darst. |
| ISBN: | 9780470517697 |
Internformat
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| 245 | 1 | 0 | |a A guide to modern econometrics |c Marno Verbeek |
| 250 | |a 3. ed., reprinted | ||
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2011 | |
| 300 | |a XV, 472 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 500 | |a Literaturverz. S. [437] - 449 | ||
| 520 | 3 | |a This_highly successful text focuses on exploring alternative techniques, combined with a practical emphasis, A guide to alternative techniques with the emphasis on the intuition behind the approaches and their practical reference, this new edition builds on the strengths of the second edition and brings the text completely up-to-date. | |
| 650 | 7 | |a Regression |2 stw | |
| 650 | 4 | |a Ökonometrie | |
| 650 | 7 | |a Ökonometrie |2 stw | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Regression analysis | |
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Datensatz im Suchindex
| _version_ | 1804148560665509888 |
|---|---|
| adam_text | Contents
Preface
xiii
1
Introduction
1
1.1
About Econometrics
1
1.2
The Structure of this Book
3
1.3
Illustrations and Exercises
4
2
An Introduction to Linear Regression
7
2.1
Ordinary Least Squares as an Algebraic Tool
8
2.1.1
Ordinary Least Squares
8
2.1.2
Simple Linear Regression
10
2.1.3
Example: Individual Wages
11
2.1.4
Matrix Notation
12
2.2
The Linear Regression Model
13
2.3
Small Sample Properties of the OLS Estimator
16
2.3.1
The Gauss-Markov Assumptions
16
2.3.2
Properties of the OLS Estimator
17
2.3.3
Example: Individual Wages (Continued)
20
2.4
Goodness-of-fit
21
2.5
Hypothesis Testing
23
2.5.1
A Simple r-Test
24
2.5.2
Example: Individual Wages (Continued)
26
2.5.3
Testing One Linear Restriction
26
2.5.4
A Joint Test of Significance of Regression Coefficients
27
2.5.5
Example: Individual Wages (Continued)
29
2.5.6
The General Case
30
2.5.7
Size, Power and p-Values
31
2.6
Asymptotic Properties of the OLS Estimator
32
vi
CONTENTS
2.6.1 Consistency 33
2.6.2
Asymptotic Normality
35
2.6.3
Small Samples and Asymptotic Theory
36
2.7
Illustration: The Capital Asset Pricing Model
38
2.7.1
The CAPM as a Regression Model
39
2.7.2
Estimating and Testing the CAPM
40
2.8
Multicollinearity
43
2.8.1
Example: Individual Wages (Continued)
45
2.9
Prediction
46
Exercises
47
3
Interpreting and Comparing Regression Models
53
3.1
Interpreting the Linear Model
53
3.2
Selecting the Set of Regressors
57
3.2.1
Misspecifying the Set of Regressors
57
3.2.2
Selecting Regressors
58
3.2.3
Comparing Non-nested Models
62
3.3
Misspecifying the Functional Form
64
3.3.1
Nonlinear Models
65
3.3.2
Testing the Functional Form
66
3.3.3
Testing for a Structural Break
66
3.4
Illustration: Explaining House Prices
67
3.5
Illustration: Predicting Stock Index Returns
71
3.6
Illustration: Explaining Individual Wages
75
3.6.1
Linear Models
75
3.6.2 Loglinear
Models
78
3.6.3
The Effects of Gender
81
3.6.4
Some Words of Warning
83
Exercises
83
4
Heteroskedasticity and Autocorrelation
85
4.1
Consequences for the OLS Estimator
85
4.2
Deriving an Alternative Estimator
87
4.3
Heteroskedasticity
88
4.3.1
Introduction
88
4.3.2
Estimator Properties and Hypothesis Testing
91
4.3.3
When the Variances are Unknown
92
4.3.4
Heteroskedasticity-consistent Standard Errors for OLS
93
4.3.5
A Model with Two Unknown Variances
95
4.3.6
Multiplicative Heteroskedasticity
96
4.3.7
Weighted Least Squares with Arbitrary Weights
97
4.4
Testing for Heteroskedasticity
97
4.4.1
Testing Equality of Two Unknown Variances
98
4.4.2
Testing for Multiplicative Heteroskedasticity
98
4.4.3
The Breusch-Pagan Test
99
4.4.4
The White Test
99
CONTENTS
vii
4.4.5
Which Test?
100
4.5
Illustration: Explaining Labour Demand
100
4.6
Autocorrelation
104
4.6.1
First-order Autocorrelation
105
4.6.2
Unknown
ρ
108
4.7
Testing for First-order Autocorrelation
109
4.7.1
Asymptotic Tests
109
4.7.2
The Durbin-Watson Test
110
4.8
Illustration: The Demand for Ice Cream 111
4.9
Alternative Autocorrelation Patterns
114
4.9.1
Higher-order Autocorrelation
114
4.9.2
Moving Average Errors
115
4.10
What to do When you Find Autocorrelation?
116
4.10.1
Misspecification
116
4.10.2
Heteroskedasticity-and-autocorrelation-consistent
Standard Errors for OLS
118
4.11
Illustration: Risk
Premia in
Foreign Exchange Markets
120
4.11.1
Notation
120
4.11.2
Tests for Risk
Premia in
the
1
Month Market
121
4.11.3
Tests for Risk
Premia
Using Overlapping Samples
124
Exercises
127
5
Endogeneity, Instrumental Variables and GMM
129
5.1
A Review of the Properties of the OLS Estimator
130
5.2
Cases Where the OLS Estimator Cannot be Saved
133
5.2.1
Autocorrelation with a Lagged Dependent Variable
134
5.2.2
An Example with Measurement Error
134
5.2.3
Endogeneity and Omitted Variable Bias
137
5.2.4
Simultaneity and Reverse Causality
138
5.3
The Instrumental Variables Estimator
140
5.3.1
Estimation with a Single Endogenous Regressor
and a Single Instrument
140
5.3.2
Back to the Keynesian model
144
5.3.3
Back to the Measurement Error Problem
145
5.3.4
Multiple Endogenous Regressors
145
5.4
Illustration: Estimating the Returns to Schooling
146
5.5
The Generalized Instrumental Variables Estimator
150
5.5.1
Multiple Endogenous Regressors with an Arbitrary
Number of Instruments
151
5.5.2
Two-stage Least Squares and the Keynesian Model
Again
154
5.5.3
Specification Tests
155
5.5.4
Weak Instruments
156
5.6
The Generalized Method of Moments
157
5.6.1
Example
158
5.6.2
The Generalized Method of Moments
159
5.6.3
Some Simple Examples
162
viii CONTENTS
5.6.4
Weak Identification
163
5.7
Illustration: Estimating
Intertemporal
Asset
Pricing Models
164
5.8
Concluding Remarks
167
Exercises
167
6
Maximum Likelihood Estimation and Specification Tests
171
6.1
An Introduction to Maximum Likelihood
172
6.1.1
Some Examples
172
6.1.2
General Properties
176
6.1.3
An Example (Continued)
179
6.1.4
The Normal Linear Regression Model
180
6.2
Specification Tests
181
6.2.1
Three Test Principles
181
6.2.2 Lagrange
Multiplier Tests
183
6.2.3
An Example (Continued)
187
6.3
Tests in the Normal Linear Regression Model
188
6.3.1
Testing for Omitted Variables
188
6.3.2
Testing for Heteroskedasticity
189
6.3.3
Testing for Autocorrelation
191
6.4
Quasi-maximum Likelihood and Moment Conditions Tests
192
6.4.1
Quasi-maximum Likelihood
192
6.4.2
Conditional Moment Tests
194
6.4.3
Testing for Normality
195
Exercises
195
7
Models with Limited Dependent Variables
199
7.1
Binary Choice Models
200
7.1.1
Using Linear Regression?
200
7.1.2
Introducing Binary Choice Models
200
7.1.3
An Underlying Latent Model
202
7.1.4
Estimation
203
7.1.5
Goodness-of-fit
205
7.1.6
Illustration: the Impact of Unemployment Benefits on
Recipiency
207
7.1.7
Specification Tests in Binary Choice Models
210
7.1.8
Relaxing Some Assumptions in Binary Choice Models
212
7.2
Multiresponse Models
213
7.2.1
Ordered Response Models
213
7.2.2
About Normalization
214
7.2.3
Illustration: Explaining Firms Credit Ratings
215
7.2.4
Illustration: Willingness to Pay for Natural Areas
217
7.2.5
Multinomial Models
220
7.3
Models for Count Data
223
7.3.1
The
Poisson
and Negative Binomial Models
224
7.3.2
Illustration: Patents and R&D Expenditures
228
CONTENTS ix
7.4 Tobit Models 230
7.4.1 The Standard Tobit Model 231
7.4.2
Estimation
233
7.4.3 Illustration:
Expenditures on Alcohol and Tobacco
(Part
1) 235
7.4.4
Specification Tests in the Tobit Model
238
7.5
Extensions of Tobit Models
240
7.5.1
The Tobit II Model
240
7.5.2
Estimation
243
7.5.3
Further Extensions
245
7.5.4
Illustration: Expenditures on Alcohol and Tobacco
(Part
2) 245
7.6
Sample Selection Bias
249
7.6.1
The Nature of the Selection Problem
250
7.6.2
Semi-parametric Estimation of the Sample-Selection
Model
252
7.7
Estimating Treatment Effects
253
7.8
Duration Models
257
7.8.1
Hazard Rates and Survival Functions
257
7.8.2
Samples and Model Estimation
260
7.8.3
Illustration: Duration of Bank Relationships
262
Exercises
264
8
Univariate Time Series Models
269
8.1
Introduction
270
8.1.1
Some Examples
270
8.1.2
Stationarity and the Autocorrelation Function
272
8.2
General
ARMA
Processes
275
8.2.1
Formulating
ARMA
Processes
275
8.2.2
Invertibility of Lag Polynomials
278
8.2.3
Common Roots
279
8.3
Stationarity and Unit Roots
280
8.4
Testing for Unit Roots
283
8.4.1
Testing for Unit Roots in a First-order
Autoregressive
Model
283
8.4.2
Testing for Unit Roots in Higher-order
Autoregressive
Models
286
8.4.3
Extensions
287
8.4.4
Illustration: Annual Price/Earnings Ratio
288
8.5
Illustration: Long-run Purchasing Power Parity (Part
1) 290
8.6
Estimation of
ARMA
Models
293
8.6.1
Least Squares
293
8.6.2
Maximum Likelihood
294
8.7
Choosing a Model
295
8.7.1
The Autocorrelation Function
296
8.7.2
The Partial Autocorrelation Function
298
8.7.3
Diagnostic Checking
299
χ
CONTENTS
8.7.4
Criteria for Model Selection
299
8.7.5
Illustration: Modelling the Price/Earnings Ratio
300
8.8
Predicting with
ARMA
Models
302
8.8.1
The Optimal Predictor
303
8.8.2
Prediction Accuracy
305
8.9
Illustration: The Expectations Theory of the Term Structure
307
8.10
Autoregressive
Conditional Heteroskedasticity
311
8.10.1
ARCH and GARCH Models
312
8.10.2
Estimation and Prediction
315
8.10.3
Illustration: Volatility in Daily Exchange Rates
317
8.11
What about Multivariate Models?
319
Exercises
320
9
Multivariate Time Series Models
323
9.1
Dynamic Models with Stationary Variables
324
9.2
Models with Nonstationary Variables
327
9.2.1
Spurious Regressions
327
9.2.2
Cointegration
328
9.2.3
Cointegration
and Error-correction Mechanisms
332
9.3
Illustration: Long-run Purchasing Power Parity (Part
2) 333
9.4
Vector
Autoregressive
Models
335
9.5
Cointegration:
the Multivariate Case
338
9.5.1
Cointegration
in
a VAR
338
9.5.2
Example:
Cointegration
in a Bivariate
VAR
341
9.5.3
Testing for
Cointegration
342
9.5.4
Illustration: Long-run Purchasing Power Parity (Part
3) 345
9.6
Illustration: Money Demand and Inflation
347
9.7
Concluding Remarks
353
Exercises
353
10
Models Based on Panel Data
355
10.1
Introduction to Panel Data Modeling
356
10.1.1
Efficiency of Parameter Estimators
357
10.1.2
Identification of Parameters
358
10.2
The Static Linear Model
359
10.2.1
The Fixed Effects Model
359
10.2.2
The First-difference Estimator
362
10.2.3
The Random Effects Model
364
10.2.4
Fixed Effects or Random Effects?
367
10.2.5
Goodness-of-fit
369
10.2.6
Alternative Instrumental Variables Estimators
370
10.2.6
Robust Inference
372
10.2.7
Testing for Heteroskedasticity and Autocorrelation
373
10.3
Illustration: Explaining Individual Wages
375
10.4
Dynamic Linear Models
377
10.4.1
An
Autoregressive
Panel Data Model
377
CONTENTS Xl
10.4.2 Dynamic Models
with Exogenous
Variables 382
10.5 Illustration:
Explaining
Capital
Structure
383
10.6 Nonstationarity,
Unit Roots and
Cointegration
389
10.6.1
Panel Data Unit Root Tests
390
10.6.2
Panel Data
Cointegration
Tests
392
10.7
Models with Limited Dependent Variables
393
10.7.1
Binary Choice Models
394
10.7.2
The Fixed Effects Logit Model
395
10.7.3
The Random Effects
Probit
Model
396
10.7.4
Tobit Models
398
10.7.5
Dynamics and the Problem of Initial Conditions
398
10.7.6
Semi-parametric Alternatives
400
10.8
Incomplete Panels and Selection Bias
401
10.8.1
Estimation with Randomly Missing Data
402
10.8.2
Selection Bias and Some Simple Tests
403
10.8.3
Estimation with Nonrandomly Missing Data
405
10.9
Pseudo
Panels and Repeated Cross-sections
406
10.9.1
The Fixed Effects Model
407
10.9.2
An Instrumental Variables Interpretation
409
10.9.3
Dynamic Models
410
Exercises
411
A Vectors and Matrices
417
A.I Terminology
417
A.2 Matrix Manipulations
418
A.3 Properties of Matrices and Vectors
419
A.4 Inverse Matrices
420
A.5 Idempotent Matrices
421
A.
6
Eigenvalues and Eigenvectors
421
A.7 Differentiation
422
A.8 Some Least Squares Manipulations
423
В
Statistical and Distribution Theory
425
B.I Discrete Random Variables
425
B.2 Continuous Random Variables
426
B.3 Expectations and Moments
427
B.4 Multivariate Distributions
428
B.5 Conditional Distributions
429
B.6 The Normal Distribution
431
B.7 Related Distributions
433
Bibliography
437
Index
451
|
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| author | Verbeek, Marno 1965- |
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| dewey-search | 330.01/5195 |
| dewey-sort | 3330.01 45195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| edition | 3. ed., reprinted |
| format | Book |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV039691464 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:09:05Z |
| institution | BVB |
| isbn | 9780470517697 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024540230 |
| oclc_num | 723657751 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG |
| owner_facet | DE-473 DE-BY-UBG |
| physical | XV, 472 S. graph. Darst. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Wiley |
| record_format | marc |
| spelling | Verbeek, Marno 1965- Verfasser (DE-588)170802655 aut A guide to modern econometrics Marno Verbeek 3. ed., reprinted Chichester [u.a.] Wiley 2011 XV, 472 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. [437] - 449 This_highly successful text focuses on exploring alternative techniques, combined with a practical emphasis, A guide to alternative techniques with the emphasis on the intuition behind the approaches and their practical reference, this new edition builds on the strengths of the second edition and brings the text completely up-to-date. Regression stw Ökonometrie Ökonometrie stw Econometrics Regression analysis Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s 1\p DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024540230&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Verbeek, Marno 1965- A guide to modern econometrics Regression stw Ökonometrie Ökonometrie stw Econometrics Regression analysis Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4123623-3 |
| title | A guide to modern econometrics |
| title_auth | A guide to modern econometrics |
| title_exact_search | A guide to modern econometrics |
| title_full | A guide to modern econometrics Marno Verbeek |
| title_fullStr | A guide to modern econometrics Marno Verbeek |
| title_full_unstemmed | A guide to modern econometrics Marno Verbeek |
| title_short | A guide to modern econometrics |
| title_sort | a guide to modern econometrics |
| topic | Regression stw Ökonometrie Ökonometrie stw Econometrics Regression analysis Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Regression Ökonometrie Econometrics Regression analysis Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024540230&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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