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An Introduction to Bayesian inference in econometrics

Author: Zellner, Arnold Series: Wiley series in probability and mathematical statistics Publisher: Wiley, 1971.Language: EnglishDescription: 431 p ; 24 cm.ISBN: 0471981656Type of document: BookNote: Doriot and Tanoto: for 2016-2017 coursesBibliography/Index: Includes bibliographical references List(s) this item appears in: Textbooks for Bayesian Analysis / Anil Gaba / PhD 2016-2017
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Item type Current location Collection Call number Status Date due
Book Doriot Library
Main Collection
Print HB74 .M3 Z44
(Browse shelf)
000075758
Available
Book Tanoto Library
Textbook Collection (PhD)
Print HB74 .M3 Z44 1971
(Browse shelf)
900209411
Available

Doriot and Tanoto: for 2016-2017 courses

Includes bibliographical references

Digitized

An Introduction to Bayesian
Inference in Econometrics
Contents
I Remarks on Inference in Economics 1
1.1 The Unity of Science 1
1.2 Deductive Inference 2
1.3 Inductive Inference 4
1.4 Reductive Inference 5
1.5 Jeffreys' Rules for a Theory of Inductive Inference 7
1.6 Implications of the Rules 8
Questions and Problems 12
II Principles of Bayesian Analysis with Selected
Applications 13
2.1 Bayes' Theorem 13
2.2 Bayes' Theorem and Several Sets of Data 17
2.3 Prior Probability Density Functions 18
2.4 Marginal and Conditional Posterior Distributions
for Parameters 21
2.5 Point Estimates for Parameters 24
2.6 Bayesian Intervals and Regions for Parameters 27
2.7 Marginal Distribution of the Observations 28
2.8 Predictive Probability Density Functions 29
2.9 Point Prediction 30
2.10 Prediction Regions and Intervals 31
2.11 Some Large Sample Properties of Bayesian
Posterior Pdf's 31
2.12 Application of Principles to Analysis of the Pareto
Distribution 34
2.13 Application of Principles to Analysis of the
Binomial Distribution 38
2.14 Reporting the Results of Bayesian Analyses 40
Appendix 41
Questions and Problems 54
III The Univariate Normal Linear Regression
Model 58
3.1 The Simple Univariate Normal Linear Regression
Model 58
3.1.1 Model and Likelihood Function 58
3.1.2 Posterior Pdf's for Parameters with a Diffuse
Prior Pdf 60
3.1.3 Application to Analysis of the Investment
Multiplier 63
3.2 The Normal Multiple Regression Model 65
3.2.1 Model and Likelihood Function 65
3.2.2 Posterior Pdf's for Parameters with a Diffuse
Prior Pdf 66
3.2.3 Posterior Pdf Based on an Informative
Prior Pdf 70
3.2.4 Predictive Pdf 72
3.2.5 Analysis of Model when X'X is Singular 75
Questions and Problems 82
IV Special Problems in Regression Analysis 86
4.1 The Regression Model with Autocorrelated Errors 86
4.2 Regressions with Unequal Variances 98
4.3 Two Regressions with Some Common Coefficients 108
Appendix 1 110
Appendix 2 110
Questions and Problems 112
V On Errors in the Variables 114
5.1 The Classical EVM: Preliminary Problems 114
5.2 Classical EVM: ML Analysis of the Functional Form 123
5.3 ML Analysis of Structural Form of the EVM 127
5.4 Bayesian Analysis of the Functional Form of the EVM 132
5.5 Bayesian Analysis of the Structural Form of the EVM 145
5.6 Alternative Assumption about the Incidental
Parameters 145
Appendix 154
Questions and Problems 157
VI Analysis of Single Equation Nonlinear
Models 162
6.1 The Box-Cox Analysis of Transformations 162
6.2 Constant Elasticity of Substitution (CES)
Production Function 169
6.3 Generalized Production Functions 176
Questions and Problems 183
VII Time Series Models : Some Selected
Examples 186
7.1 First Order Normal Autoregressive Process 186
7.2 First Order Autoregressive Model with Incomplete
Data 191
7.3 Analysis of a Second Order Autoregressive Process 194
7.4 " Distributed Lag" Models 200
7.5 Applications to Consumption Function Estimation 207
7.6 Some Generalizations of the Distributed Lag Model 213
Appendix 216
Questions and Problems 220
VIII Multivariate Regression Models 224
8.1 The Traditional Multivariate Regression Model 224
8.2 Predictive Pdf for the Traditional
Multivariate Regression Model 233
8.3 The Traditional Multivariate Model with Exact
Restrictions 236
8.4 Traditional Model with an Informative Prior Pdf 238
8.5 The "Seemingly Unrelated" Regression Model 240
Questions and Problems 246
IX Simultaneous Equation Econometric
Models 248
9.1 Fully Recursive Models 250
9.2 General Triangular Systems 252
9.3 The Concept of Identification in Bayesian Analysis 253
9.4 Analysis of Particular Simultaneous Equation Models 258
9.5 "Limited Information" Bayesian Analysis 265
9.6 Full System Analysis 270
9.7 Results of Some Monte Carlo Experiments 276
9.7.1 The Model and Its Specifications 277
9.7.2 Sampling-Theory Analysis of the Model 278
9.7.3 Bayesian Analysis of the Model 278
9.7.4 Experimental Results: Point Estimates 280
9.7.5 Experimental Results: Confidence Intervals 286
9.7.6 Concluding Remarks on the Monte Carlo
Experiments 286
Questions and Problems 287
X On Comparing and Testing Hypotheses 291
10.1 Posterior Probabilities Associated with Hypotheses 292
10.2 Analyzing Hypotheses with Diffuse Prior Pdf's for
Parameters 298
10.3 Comparing and Testing Hypotheses with Nondiffuse
Prior Information 302
10.4 Comparing Regression Models 306
10.5 Comparing Distributed Lag Models 312
Questions and Problems 317
XI Analysis of Some Control Problems 319
11.1 Some Simple One Period Control Problems 320
11.2 Single-Period Control of Multiple Regression Processes 327
11.3 Control of Multivariate Normal Regression Processes 331
11.4 Sensitivity of Control to Form of Loss Function 333
11.5 Two-Period Control of the Multiple Regression Model 336
11.6 Some Multiperiod Control Problems 344
Appendix 1 354
Appendix 2 356
Questions and Problems 358
XII Conclusion 360
Appendix A 363
Appendix B 379
Appendix C 400
Bibliography 415
Author Index 423
Subject Index 427

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