Synopses & Reviews
"Christensen and Kiefer's excellent book shows how careful dynamic theory and econometrics go hand in hand, opening up new vistas in the areas of search theory, finance, and macroeconomics."
--Tom Sargent, New York University and the Hoover Institution"There is no other book that mixes dynamic economic theory, statistical inference, and real quantitative applications like this one. Christensen and Kiefer will challenge the top tier of students and take them to the research frontier."--Robert Lucas, University of Chicago
"Dynamic programming is an organizing framework that has enabled economists to integrate economic theory with empirical analysis. Few textbooks reflect the integrated nature of contemporary research, but Christensen and Kiefer reveal the power of the dynamic programming approach in a wide variety of applications from job search to portfolio choice. Their new book will be invaluable to students who wish to participate in this exciting enterprise."--John Y. Campbell, Harvard University
"The authors do a splendid job of showing how to use stochastic dynamic optimization techniques to generate the implied distributions of observables needed for estimation. There are many interesting and useful examples included in the book, ranging from applications of the theory of job search to those of asset pricing theory. This book should be a reference for anyone interested in using dynamic economic models to make inferences about the world we observe."--Dale Mortensen, Aarhus University, Denmark, and Northwestern University
"An extremely ambitious and thought-provoking book, one that combines state-of-the-art economic theory with sophisticated econometric techniques. The dynamic programming framework brings together important results and recent developments in a unique, unified way. The book is sure to inspire many PhD students and empirically oriented researchers for years to come."--Tim Bollerslev, Duke University
"I have been looking for a book like this for quite a while. Economic Modeling and Inference is written for those who want to do applied work and actually apply this to real-life data or run simulations. This much-needed book fills a void. It is certainly a significant contribution to the field."--Yaw Nyarko, New York University
"Economic Modeling and Inference blends economic theory and statistical inference in a seamless fashion. Every dynamic decision model is discussed with an eye for it to be fit with economic data. Every econometric inference tool is developed for the purpose of testing economic decision models. This book is long overdue. It will influence and benefit young economists for generations to come."--Mark Y. An, Fannie Mae
Review
The authors do a splendid job of showing how to use stochastic dynamic optimization techniques to generate the implied distributions of observables needed for estimation. There are many interesting and useful examples included in the book, ranging from applications of the theory of job search to those of asset pricing theory. This book should be a reference for anyone interested in using dynamic economic models to make inferences about the world we observe.
Review
The authors do a splendid job of showing how to use stochastic dynamic optimization techniques to generate the implied distributions of observables needed for estimation. There are many interesting and useful examples included in the book, ranging from applications of the theory of job search to those of asset pricing theory. This book should be a reference for anyone interested in using dynamic economic models to make inferences about the world we observe.
Review
An extremely ambitious and thought-provoking book, one that combines state-of-the-art economic theory with sophisticated econometric techniques. The dynamic programming framework brings together important results and recent developments in a unique, unified way. The book is sure to inspire many PhD students and empirically oriented researchers for years to come.
Review
I have been looking for a book like this for quite a while. is written for those who want to do applied work and actually apply this to real-life data or run simulations. This much-needed book fills a void. It is certainly a significant contribution to the field.
Review
blends economic theory and statistical inference in a seamless fashion. Every dynamic decision model is discussed with an eye for it to be fit with economic data. Every econometric inference tool is developed for the purpose of testing economic decision models. This book is long overdue. It will influence and benefit young economists for generations to come.
Review
"Economic Modeling and Inference gives an excellent overview of dynamic modeling techniques in economics and fills an important gap among current textbooks. [It] is an excellent book, especially for graduate students in economics. . . . [I]t is also a must for economists who need a refresher course in dynamic modeling . . . [and] should also be on the bookshelf of practicing researchers interested in expanding the number of models used in their work."--Journal of the American Statistical Association
Review
"Economic Modeling and Inference offers a technically sophisticated grounding in the structural approach to analyzing data. The book does an excellent job of illustrating the wide range of questions that the empirical dynamic programming approach can tackle by explicitly bridging economic theory and econometrics. . . . Books such as these will undoubtedly help the structural paradigm more successfully compete in the market for applied econometric methodologies."--Robert M. Sauer, Journal of Economic Literature
Review
Economic Modeling and Inference gives an excellent overview of dynamic modeling techniques in economics and fills an important gap among current textbooks. [It] is an excellent book, especially for graduate students in economics. . . . [I]t is also a must for economists who need a refresher course in dynamic modeling . . . [and] should also be on the bookshelf of practicing researchers interested in expanding the number of models used in their work. Journal of the American Statistical Association
Review
Economic Modeling and Inference offers a technically sophisticated grounding in the structural approach to analyzing data. The book does an excellent job of illustrating the wide range of questions that the empirical dynamic programming approach can tackle by explicitly bridging economic theory and econometrics. . . . Books such as these will undoubtedly help the structural paradigm more successfully compete in the market for applied econometric methodologies. Robert M. Sauer
Review
Economic Modeling and Inference gives an excellent overview of dynamic modeling techniques in economics and fills an important gap among current textbooks. [It] is an excellent book, especially for graduate students in economics. . . . [I]t is also a must for economists who need a refresher course in dynamic modeling . . . [and] should also be on the bookshelf of practicing researchers interested in expanding the number of models used in their work. Journal of the American Statistical Association
Review
Economic Modeling and Inference offers a technically sophisticated grounding in the structural approach to analyzing data. The book does an excellent job of illustrating the wide range of questions that the empirical dynamic programming approach can tackle by explicitly bridging economic theory and econometrics. . . . Books such as these will undoubtedly help the structural paradigm more successfully compete in the market for applied econometric methodologies. Robert M. Sauer
Review
"Overall, the book is well structured and will be useful for many different purposes, ranging from a main text for a PhD-level course to practical guidance for researchers who do empirical work based on dynamic programming models."--Dong-Hyuk Kim, Economic Record
Synopsis
Economic Modeling and Inference takes econometrics to a new level by demonstrating how to combine modern economic theory with the latest statistical inference methods to get the most out of economic data. This graduate-level textbook draws applications from both microeconomics and macroeconomics, paying special attention to financial and labor economics, with an emphasis throughout on what observations can tell us about stochastic dynamic models of rational optimizing behavior and equilibrium. Bent Jesper Christensen and Nicholas Kiefer show how parameters often thought estimable in applications are not identified even in simple dynamic programming models, and they investigate the roles of extensions, including measurement error, imperfect control, and random utility shocks for inference. When all implications of optimization and equilibrium are imposed in the empirical procedures, the resulting estimation problems are often nonstandard, with the estimators exhibiting nonregular asymptotic behavior such as short-ranked covariance, superconsistency, and non-Gaussianity. Christensen and Kiefer explore these properties in detail, covering areas including job search models of the labor market, asset pricing, option pricing, marketing, and retirement planning. Ideal for researchers and practitioners as well as students, Economic Modeling and Inference uses real-world data to illustrate how to derive the best results using a combination of theory and cutting-edge econometric techniques.
- Covers identification and estimation of dynamic programming models
- Treats sources of error--measurement error, random utility, and imperfect control
- Features financial applications including asset pricing, option pricing, and optimal hedging
- Describes labor applications including job search, equilibrium search, and retirement
- Illustrates the wide applicability of the approach using micro, macro, and marketing examples
Synopsis
Economic Modeling and Inference takes econometrics to a new level by demonstrating how to combine modern economic theory with the latest statistical inference methods to get the most out of economic data. This graduate-level textbook draws applications from both microeconomics and macroeconomics, paying special attention to financial and labor economics, with an emphasis throughout on what observations can tell us about stochastic dynamic models of rational optimizing behavior and equilibrium. Bent Jesper Christensen and Nicholas Kiefer show how parameters often thought estimable in applications are not identified even in simple dynamic programming models, and they investigate the roles of extensions, including measurement error, imperfect control, and random utility shocks for inference. When all implications of optimization and equilibrium are imposed in the empirical procedures, the resulting estimation problems are often nonstandard, with the estimators exhibiting nonregular asymptotic behavior such as short-ranked covariance, superconsistency, and non-Gaussianity. Christensen and Kiefer explore these properties in detail, covering areas including job search models of the labor market, asset pricing, option pricing, marketing, and retirement planning. Ideal for researchers and practitioners as well as students, Economic Modeling and Inference uses real-world data to illustrate how to derive the best results using a combination of theory and cutting-edge econometric techniques.
- Covers identification and estimation of dynamic programming models
- Treats sources of error--measurement error, random utility, and imperfect control
- Features financial applications including asset pricing, option pricing, and optimal hedging
- Describes labor applications including job search, equilibrium search, and retirement
- Illustrates the wide applicability of the approach using micro, macro, and marketing examples
Synopsis
"Christensen and Kiefer's excellent book shows how careful dynamic theory and econometrics go hand in hand, opening up new vistas in the areas of search theory, finance, and macroeconomics."--Tom Sargent, New York University and the Hoover Institution
"There is no other book that mixes dynamic economic theory, statistical inference, and real quantitative applications like this one. Christensen and Kiefer will challenge the top tier of students and take them to the research frontier."--Robert Lucas, University of Chicago
"Dynamic programming is an organizing framework that has enabled economists to integrate economic theory with empirical analysis. Few textbooks reflect the integrated nature of contemporary research, but Christensen and Kiefer reveal the power of the dynamic programming approach in a wide variety of applications from job search to portfolio choice. Their new book will be invaluable to students who wish to participate in this exciting enterprise."--John Y. Campbell, Harvard University
"The authors do a splendid job of showing how to use stochastic dynamic optimization techniques to generate the implied distributions of observables needed for estimation. There are many interesting and useful examples included in the book, ranging from applications of the theory of job search to those of asset pricing theory. This book should be a reference for anyone interested in using dynamic economic models to make inferences about the world we observe."--Dale Mortensen, Aarhus University, Denmark, and Northwestern University
"An extremely ambitious and thought-provoking book, one that combines state-of-the-art economic theory with sophisticated econometric techniques. The dynamic programming framework brings together important results and recent developments in a unique, unified way. The book is sure to inspire many PhD students and empirically oriented researchers for years to come."--Tim Bollerslev, Duke University
"I have been looking for a book like this for quite a while. Economic Modeling and Inference is written for those who want to do applied work and actually apply this to real-life data or run simulations. This much-needed book fills a void. It is certainly a significant contribution to the field."--Yaw Nyarko, New York University
"Economic Modeling and Inference blends economic theory and statistical inference in a seamless fashion. Every dynamic decision model is discussed with an eye for it to be fit with economic data. Every econometric inference tool is developed for the purpose of testing economic decision models. This book is long overdue. It will influence and benefit young economists for generations to come."--Mark Y. An, Fannie Mae
Synopsis
Economic Modeling and Inference takes econometrics to a new level by demonstrating how to combine modern economic theory with the latest statistical inference methods to get the most out of economic data. This graduate-level textbook draws applications from both microeconomics and macroeconomics, paying special attention to financial and labor economics, with an emphasis throughout on what observations can tell us about stochastic dynamic models of rational optimizing behavior and equilibrium. Bent Jesper Christensen and Nicholas Kiefer show how parameters often thought estimable in applications are not identified even in simple dynamic programming models, and they investigate the roles of extensions, including measurement error, imperfect control, and random utility shocks for inference. When all implications of optimization and equilibrium are imposed in the empirical procedures, the resulting estimation problems are often nonstandard, with the estimators exhibiting nonregular asymptotic behavior such as short-ranked covariance, superconsistency, and non-Gaussianity. Christensen and Kiefer explore these properties in detail, covering areas including job search models of the labor market, asset pricing, option pricing, marketing, and retirement planning. Ideal for researchers and practitioners as well as students,
Economic Modeling and Inference uses real-world data to illustrate how to derive the best results using a combination of theory and cutting-edge econometric techniques.
- Covers identification and estimation of dynamic programming models
- Treats sources of error--measurement error, random utility, and imperfect control
- Features financial applications including asset pricing, option pricing, and optimal hedging
- Describes labor applications including job search, equilibrium search, and retirement
- Illustrates the wide applicability of the approach using micro, macro, and marketing examples
Synopsis
"Christensen and Kiefer's excellent book shows how careful dynamic theory and econometrics go hand in hand, opening up new vistas in the areas of search theory, finance, and macroeconomics."--Tom Sargent, New York University and the Hoover Institution
"There is no other book that mixes dynamic economic theory, statistical inference, and real quantitative applications like this one. Christensen and Kiefer will challenge the top tier of students and take them to the research frontier."--Robert Lucas, University of Chicago
"Dynamic programming is an organizing framework that has enabled economists to integrate economic theory with empirical analysis. Few textbooks reflect the integrated nature of contemporary research, but Christensen and Kiefer reveal the power of the dynamic programming approach in a wide variety of applications from job search to portfolio choice. Their new book will be invaluable to students who wish to participate in this exciting enterprise."--John Y. Campbell, Harvard University
"The authors do a splendid job of showing how to use stochastic dynamic optimization techniques to generate the implied distributions of observables needed for estimation. There are many interesting and useful examples included in the book, ranging from applications of the theory of job search to those of asset pricing theory. This book should be a reference for anyone interested in using dynamic economic models to make inferences about the world we observe."--Dale Mortensen, Aarhus University, Denmark, and Northwestern University
"An extremely ambitious and thought-provoking book, one that combines state-of-the-art economic theory with sophisticated econometric techniques. The dynamic programming framework brings together important results and recent developments in a unique, unified way. The book is sure to inspire many PhD students and empirically oriented researchers for years to come."--Tim Bollerslev, Duke University
"I have been looking for a book like this for quite a while. Economic Modeling and Inference is written for those who want to do applied work and actually apply this to real-life data or run simulations. This much-needed book fills a void. It is certainly a significant contribution to the field."--Yaw Nyarko, New York University
"Economic Modeling and Inference blends economic theory and statistical inference in a seamless fashion. Every dynamic decision model is discussed with an eye for it to be fit with economic data. Every econometric inference tool is developed for the purpose of testing economic decision models. This book is long overdue. It will influence and benefit young economists for generations to come."--Mark Y. An, Fannie Mae
About the Author
Bent Jesper Christensen is professor of economics and management at the University of Aarhus in Denmark. Nicholas M. Kiefer is the Ta-Chung Liu Professor in Economics and Statistical Science at Cornell University.
Table of Contents
Preface xiii
Chapter 1: Introduction 1
1.1 Expected Utility Theory 1
1.2 Uncertainty Aversion, Ellsberg and Allais 4
1.3 Structural Versus Reduced-Form Methods 6
1.4 Exercises 7
1.5 References 8
Chapter 2: Components of a Dynamic Programming Model 9
2.1 Examples 9
2.2 Data Configurations 13
2.3 The Objective Function 16
2.4 The State Variables 17
2.5 The Control Variables 18
2.6 The Transition Distribution 19
2.7 The Curse of Dimensionality 21
2.8 The Curse of Degeneracy 22
2.9 Exercises 24
2.10 References 25
Chapter 3: Discrete States and Controls 26
3.1 Solving DP Problems: Finite Horizon 26
3.2 Solving DP Problems: Infinite Horizon 30
3.2.1 The Method of Successive Approximation 32
3.2.2 The Method of Policy Iteration 34
3.3 Identification: A Preview 35
3.4 Exercises 37
3.5 References 37
Chapter 4: Likelihood Functions for Discrete State/Control Models 38
4.1 Likelihood with Complete Observability 38
4.2 Measurement Error 45
4.3 Imperfect Control 51
4.4 Conclusions 54
4.5 Exercises 55
4.6 References 55
Chapter 5: Random Utility Models 57
5.1 Introduction 57
5.2 The Value Function 59
5.3 A Binary Utility Shock 60
5.4 A Continuously Distributed Utility Shock 62
5.5 Choice Probabilities 65
5.6 Dynamic Continuous Random Utility 66
5.7 Exercises 69
5.8 References 70
Chapter 6: Continuous States, Discrete Controls 71
6.1 Introduction 71
6.2 Transition Distributions and Utility 73
6.3 The Value Function and Backward Recursion 74
6.4 Example: Exercising an American Option 76
6.5 Infinite Horizon: Contraction and Forward Recursion 79
6.6 Example: Optimal Stopping in Discrete Time 83
6.7 Exercises 85
6.8 References 85
Chapter 7: Econometric Framework for the Search Model 87
7.1 The Search Model 87
7.2 Likelihood: General Considerations 89
7.3 Likelihood: Specifics for Wage Data 94
7.3.1 Wage Data Alone--One Parameter 96
7.3.2 Wage Data--Two Parameters 97
7.3.3 Wage Data Alone--Offer Arrival Probability 99
7.4 Likelihood: Wage and Duration Data 100
7.4.1 Wage and Duration Data--Two Parameters 100
7.4.2 Wage and Duration Data--Three Parameters 102
7.4.3 Wage and Duration Data--Gamma Distribution 104
7.5 Exercises 107
7.6 References 108
Chapter 8: Exact Distribution Theory for the Job Search Model 109
8.1 Introduction 109
8.2 The Prototypal Search Model 110
8.3 Alternative Economic Parametrizations 115
8.4 Models for Joint Wage and Duration Data 122
8.5 Conclusion 127
8.6 Exercises 128
8.7 References 128
Chapter 9: Measurement Error in the Prototypal Job Search Model 129
9.1 Introduction 129
9.2 The Prototypal Search Model 130
9.3 The Prototypal Model with Measurement Errors 132
9.4 Characterizing the Distribution of Measurement Errors 134
9.5 Estimation in the Prototypal Model with Measurement Errors 136
9.6 Application to the SIPP Data Set 139
9.7 Conclusions 146
9.8 Exercises 146
9.9 References 147
Chapter 10: Asset Markets 148
10.1 Introduction 148
10.2 General Asset Pricing 148
10.3 The Term Structure of Interest Rates 150
10.4 Forward Contracts 154
10.5 Futures Contracts 156
10.6 Introduction to Options 160
10.7 The Binomial Method 162
10.8 Empirical Applications 166
10.8.1 Time Series Properties 167
10.8.2 Portfolio Models 174
10.8.3 Time-Varying Volatility 181
10.8.4 Term Structure Analysis 184
10.9 Exercises 191
10.10 References 191
Chapter 11: Financial Options 192
11.1 Introduction 192
11.2 Financial Option Exercise and Job Search 192
11.3 Multiple Finite-Horizon Options 194
11.4 Markov Stock Prices 196
11.5 Value Functions for American Options 199
11.6 Option Price Data 205
11.7 Testing Option Market Efficiency 208
11.8 Exercises 212
11.9 References 212
Chapter 12: Retirement 213
12.1 Introduction 213
12.2 A Simple Retirement Model 213
12.3 The Likelihood Function 216
12.4 Longitudinal Data 221
12.5 Regularizing the Likelihood 224
12.6 Generalizations 232
12.7 Alternative Models 236
12.8 Application: The Joint Retirement of Married Couples 240
12.9 Exercises 242
12.10 References 243
Chapter 13: Continuous States and Controls 244
13.1 Introduction 244
13.2 The Linear-Quadratic Model: Finite Horizon 245
13.2.1 An Application: Macroeconomic Control 247
13.2.2 Rational Expectations 248
13.3 The Linear-Quadratic Model: Infinite Horizon 249
13.3.1 Application: Macro Policy with Rational Expectations 250
13.4 Estimation of Linear-Quadratic Models 251
13.4.1 The Curse of Degeneracy 251
13.4.2 Sources of Noise 251
13.4.3 Measurement Error 253
13.4.4 Imperfect Control 253
13.4.5 Random Utility 254
13.5 The General (Non-LQ) Case 256
13.6 Smoothness: Euler Equations 260
13.7 Discussion and Examples 261
13.8 Random Utility in the General Case 264
13.9 Exercises 264
13.10 References 265
Chapter 14: Continuous-Time Models 266
14.1 Introduction 266
14.2 Optimal Stopping in Continuous Time 269
14.3 A Jump Process Application: Allocation of Time over Time 270
14.4 Dynamic Consumption and Portfolio Choice 274
14.5 Application: Changing Investment Opportunities 278
14.6 Derivatives, Hedging, and Arbitrage Pricing 281
14.7 Stochastic Volatility and Jumps 289
14.8 The Term Structure of Interest Rates in Continuous Time 298
14.9 Exercises 310
14.10 References 310
Chapter 15: Microeconomic Applications 312
15.1 Introduction 312
15.2 Bus Engine Replacement 313
15.3 Aircraft Engine Maintenance 314
15.4 Medical Treatment and Absenteeism 316
15.5 Nuclear Power Plant Operation 317
15.6 Fertility and Child Mortality 319
15.7 Costs of Price Adjustment 320
15.8 Schooling, Work, and Occupational Choice 322
15.9 Renewal of Patents 323
15.10 Marketing--Direct Mailing of Catalogs 324
15.11 Scrapping Subsidies and Automobile Purchases 326
15.12 On-the-Job Search and the Wage Distribution 327
15.13 Exercises 329
15.14 References 330
Chapter 16: Macroeconomic Applications 331
16.1 Consumption as a Random Walk 331
16.2 Consumption and Asset Returns 333
16.3 Dynamic Labor Demand 334
16.4 Time Inconsistency of Optimal Plans 336
16.5 Time to Build 338
16.6 Nonseparable Utility 339
16.7 Preferences of Monetary Authorities 341
16.8 Dynamic Labor Supply 342
16.9 Effects of U.S. Farm Subsidies 345
16.10 Exercises 346
16.11 References 346
Chapter 17: Finance Application: Futures Hedging 347
17.1 Hedging Strategies 347
17.2 Self-Financing Trading Strategies 350
17.3 Estimation 353
17.4 Exercises 359
17.5 References 359
Chapter 18: Intertemporal Asset Pricing 360
18.1 Introduction 360
18.2 Prices and Returns 361
18.3 Capital Asset Pricing Model 362
18.4 Estimation 363
18.5 A Structural Model 365
18.6 Asset Pricing Puzzles 369
18.7 Exercises 376
18.8 References 376
Chapter 19: Dynamic Equilibrium: The Search Model 377
19.1 Introduction 377
19.2 Homogeneous Equilibrium Search 378
19.3 Data Distribution and Likelihood 383
19.4 Panels with Partially Missing Observations 389
19.4.1 The Contribution of Unemployment Duration 390
19.4.2 The Contribution of Wages 390
19.4.3 The Contribution of Employment Duration 392
19.4.4 A Numerical Example 394
19.5 Geometric Information Decomposition 395
19.5.1 Destination State Information 400
19.6 Data and Summary Statistics 403
19.7 Empirical Results 406
19.8 Conclusion 414
19.9 Exercises 415
19.10 References 415
Chapter 20: Dynamic Equilibrium: Search Equilibrium Extensions 416
20.1 Introduction 416
20.2 Measurement Error in Wages 416
20.3 Heterogeneity in Productivity: The Discrete Case 420
20.4 Heterogeneity in Productivity: The Continuous Case 424
20.5 Conclusion 429
20.6 Exercises 429
20.7 References 429
Appendix: Brief Review of Statistical Theory 431
A.1 Introduction 431
A.2 Exponential Families 432
A.3 Maximum Likelihood 434
A.4 Classical Theory of Testing 437
References 441
Index 469