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Format: 1 Online-Ressource

Series Statement: Handbooks in economics 2

Note: Bd. 1 (1983) bis Bd. 7,A (2020) im Rahmen einer Nationallizenz (ZDB-1-HBE) verfügbar.

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-444-88766-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-444-86187-0

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-444-86186-3

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-444-53200-8

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-444-50631-3

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-444-82340-3

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-444-86185-6

Language: English

Subjects: Economics , Mathematics

Keywords: Ökonometrie ; Aufsatzsammlung

URL: Volltext

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Format: Online Ressource

ISBN: 9780444823403 , 0444823409

Series Statement: Handbooks in economics 2

Content: The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics. Comprehensive surveys, written by experts, discuss recent developments at a level suitable for professional use by economists, econometricians, statisticians, and in advanced graduate econometrics courses

Content: The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics. Comprehensive surveys, written by experts, discuss recent developments at a level suitable for professional use by economists, econometricians, statisticians, and in advanced graduate econometrics courses

Note: Description based upon print version of record , Part 11: New Developments in Theoretical Econometrics. 52. The bootstrap (J. Horowitz). 53. Panel data models: some recent developments (M. Arellano, B. Honor & eacute;). 54. Interactions-based models (W.A. Brock, S.N. Durlauf). 55. Duration models: specification, identification, and multiple durations (G.J. van den Berg). Part 12: Computational Methods in Econometrics. 56. Computational intensive methods for integration in econometrics (J. Geweke, M. Keane). 57. Markov chain Monte Carlo methods: computation and inference (S. Chib). Part 13: Applied Econometrics. 58. Calibration (C. Dawkins, T.N. Srinivasan and J. Whalley). 59. Measurement error in survey data (J. Bound, C. Brown and N. Mathiowetz). , Cover; Copyright Page; CONTENTS; Introduction to the Series; Contents of the Handbook; Preface to the Handbook; References; Part 11: NEW DEVELOPMENTS IN THEORETICAL ECONOMETRICS; Chapter 52. The Bootstrap; Abstract; Keywords; 1. Introduction; 2. The bootstrap sampling procedure and its consistency; 3. Asymptotic refinements; 4. Extensions; 5. Monte Carlo experiments; 6. Conclusions; Acknowledgements; Appendix A. Informal derivation of Equation (3.27); References; Chapter 53. Panel Data Models: Some Recent Developments; Abstract; Keywords; 1. Introduction , 2. Linear models with predetermined variables: identification3. Linear models with predetermined variables: estimation; 4. Nonlinear panel data models; 5. Conditional maximum likelihood estimation; 6. Discrete choice models with ?fixedŽ effects; 7. Tobit-type models with ?fixedŽ effects; 8. Models with lagged dependent variables; 9. ?RandomŽ effects models; 10. Concluding remarks; References; Chapter 54. Interactions-Based Models; Abstract; Keywords; 1. Introduction; 2. Binary choice with social interactions; 3. Identification: basic issues; 4. Further topics in identification , 5. Sampling properties6. Statistical analysis with grouped data; 7. Evidence; 8. Summary and conclusions; Appendix A; References; Chapter 55. Duration Models: Specification, Identification and Multiple Durations; Abstract; Keywords; 1. Introduction; 2. Basic concepts and notation; 3. Some structural models of durations; 4. The Mixed Proportional Hazard model; 5. Identification of the MPH model with single-spell data; 6. The MPH model with multi-spell data; 7. An informal classification of reduced-form multiple-duration models; 8. The Multivariate Mixed Proportional Hazard model , 9. Causal duration effects and selectivity10. Conclusions and recommendations; References; Part 12: COMPUTATIONAL METHODS IN ECONOMETRICS; Chapter 56. Computationally Intensive Methods for Integration in Econometrics; Abstract; Keywords; 1. Introduction; 2. Monte Carlo methods of integral approximation; 3. Approximate solution of discrete dynamic optimization problems; 4. Classical simulation estimation of the multinomial probit model; 5. Univariate latent linear models; 6. Multivariate latent linear models; 7. Bayesian inference for a dynamic discrete choice model , Appendix A. The full univariate latent linear modelAppendix B. The full multivariate latent linear model; References; Chapter 57. Markov Chain Monte Carlo Methods: Computation and Inference; Abstract; Keywords; 1. Introduction; 2. Classical sampling methods; 3. Markov chains; 4. Metropolis-Hastings algorithm; 5. The Gibbs sampling algorithm; 6. Sampler performance and diagnostics; 7. Strategies for improving mixing; 8. MCMC algorithms in Bayesian estimation; 9. Sampling the predictive density; 10. MCMC methods in model choice problems; 11. MCMC methods in optimization problems , 12. Concluding remarks

Additional Edition: ISBN 9780080524795

Additional Edition: Erscheint auch als Druck-Ausgabe Handbook of econometrics. Vol. 5 Amsterdam ; London : North Holland, 2001 ISBN 0444823409

Language: English

URL: lizenzpflichtig

URL: Volltext  (Deutschlandweit zugänglich)

URL: Inhaltstext

Author information: Heckman, James J. 1944-

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Format: XXII S., S. 3160 - 3843, 27 S. , graph. Darst.

Edition: 1. ed.

ISBN: 0444823409 , 9780444823403

Series Statement: Handbooks in economics 2,5

In: 5

Language: English

Subjects: Economics

Keywords: Ökonometrie ; Aufsatzsammlung

Author information: Durlauf, Steven N.

Author information: Heckman, James J. 1944-

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ISBN: 9780444823403

In: Handbook of econometrics, Amsterdam : North Holland, 2001, (2001), Seite xiii-xiv, 9780444823403

In: 0444823409

In: year:2001

In: pages:xiii-xiv

Language: English

DOI: 10.1016/S1573-4412(01)05003-6

URL: Volltext  (Deutschlandweit zugänglich)

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ISBN: 9780444823403

Content: This chapter focuses on two of the developments in panel data econometrics since the Handbook chapter by Chamberlain (1984). The first objective of this chapter is to provide a review of linear panel data models with predetermined variables. We discuss the implications of assuming that explanatory variables are predetermined as opposed to strictly exogenous in dynamic structural equations with unobserved heterogeneity. We compare the identification from moment conditions in each case, and the implications of alternative feedback schemes for the time series properties of the errors. We next consider autoregressive error component models under various auxiliary assumptions. There is a trade-off between robustness and efficiency since assumptions of stationary initial conditions or time series homoskedasticity can be very informative, but estimators are not robust to their violation. We also discuss the identification problems that arise in models with predetermined variables and multiple effects. Concerning inference in linear models with predetermined variables, we discuss the form of optimal instruments, and the sampling properties of GMM and LIML-analogue estimators drawing on Monte Carlo results and asymptotic approximations. A number of identification results for limited dependent variable models with fixed effects and strictly exogenous variables are available in the literature, as well as some results on consistent and asymptotically normal estimation of such models. There are also some results available for models of this type including lags of the dependent variable, although even less is known for nonlinear dynamic models. Reviewing the recent work on discrete choice and selectivity models with fixed effects is the second objective of this chapter. A feature of parametric limited dependent variable models is their fragility to auxiliary distributional assumptions. This situation prompted the development of a large literature dealing with semiparametric alternatives (reviewed in Powell, 1994’s chapter). The work that we review in the second part of the chapter is thus at the intersection of the panel data literature and that on cross-sectional semiparametric limited dependent variable models.

In: Handbook of econometrics, Amsterdam : North Holland, 2001, (2001), Seite 3229-3296, 9780444823403

In: 0444823409

In: year:2001

In: pages:3229-3296

Language: English

DOI: 10.1016/S1573-4412(01)05006-1

URL: Volltext  (Deutschlandweit zugänglich)

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ISBN: 9780444823403

Content: Since the early 1980s, the econometric analysis of duration variables has become widespread. This chapter provides an overview of duration analysis, with an emphasis on the specification and identification of duration models, and with special attention to models for multiple durations. Most of the chapter deals with so-called reduced-form duration models, notably the popular Mixed Proportional Hazard (MPH) model and its multivariate extensions. The MPH model is often used to describe the relation between the empirical exit rate and “background variables” in a concise way. However, since the applications usually interpret the results in terms of some economic-theoretical model, we examine to what extent the deep structural parameters of some important theoretical models can be related to reduced-form parameters. We subsequently examine the specification and identification of the MPH model in great detail, we provide intuition on what drives identification, and we infer to what extent biases may occur because of misspecifications. This examination is carried out separately for the case of single-spell data and the case of multi-spell data. We also compare different functional forms for the unobserved heterogeneity distribution. Next, we examine models for multiple durations. In the applied econometric literature on the estimation of multiple-duration models, the range of different models is actually not very large. Typically, the models allow for dependence between the duration variables by way of their unobserved determinants, with each single duration following its own MPH model. In addition to this, the model may allow for an interesting “causal” effect of one duration on the other, as motivated by an underlying economic theory. For all these models we examine the conditions for identification. Some of these are intimately linked to particular estimation strategies. The multiple-duration model where the marginal duration distributions each satisfy an MPH specification, and the durations can only be dependent by way of their unobserved determinants, is called the Multivariate Mixed Proportional Hazard (MMPH) model. For this model, we address the issue of the dimensionality of the heterogeneity distribution and we compare the flexibility of different parametric heterogeneity distributions. On a number of occasions, we incorporate recent insights from the biostatistical literature on duration analysis, and we contrast points of view in this literature to those in the econometric literature. Finally, throughout the chapter, we discuss the importance of the possible collection of additional data.

In: Handbook of econometrics, Amsterdam : North Holland, 2001, (2001), Seite 3381-3460, 9780444823403

In: 0444823409

In: year:2001

In: pages:3381-3460

Language: English

DOI: 10.1016/S1573-4412(01)05008-5

URL: Volltext  (Deutschlandweit zugänglich)

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ISBN: 9780444823403

Content: Until recently, inference in many interesting models was precluded by the requirement of high dimensional integration. But dramatic increases in computer speed, and the recent development of new algorithms that permit accurate Monte Carlo evaluation of high dimensional integrals, have greatly expanded the range of models that can be considered. This chapter presents the methodology for several of the most important Monte Carlo methods, supplemented by a set of concrete examples that show how the methods are used. Some of the examples are new to the econometrics literature. They include inference in multinomial discrete choice models and selection models in which the standard normality assumption is relaxed in favor of a multivariate mixture of normals assumption. Several Monte Carlo experiments indicate that these methods are successful at identifying departures from normality when they are present. Throughout the chapter the focus is on inference in parametric models that permit rich variation in the distribution of disturbances. The chapter first discusses Monte Carlo methods for the evaluation of high dimensional integrals, including integral simulators like the GHK method, and Markov Chain Monte Carlo methods like Gibbs sampling and the Metropolis–Hastings algorithm. It then turns to methods for approximating solutions to discrete choice dynamic optimization problems, including the methods developed by Keane and Wolpin, and Rust, as well as methods for circumventing the integration problem entirely, such as the approach of Geweke and Keane. The rest of the chapter deals with specific examples: classical simulation estimation for multinomial probit models, both in the cross sectional and panel data contexts; univariate and multivariate latent linear models; and Bayesian inference in dynamic discrete choice models in which the future component of the value function is replaced by a flexible polynomial.

In: Handbook of econometrics, Amsterdam : North Holland, 2001, (2001), Seite 3463-3568, 9780444823403

In: 0444823409

In: year:2001

In: pages:3463-3568

Language: English

DOI: 10.1016/S1573-4412(01)05009-7

URL: Volltext  (Deutschlandweit zugänglich)

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ISBN: 9780444823403

In: Handbook of econometrics, Amsterdam : North Holland, 2001, (2001), Seite iv, 9780444823403

In: 0444823409

In: year:2001

In: pages:iv

Language: English

DOI: 10.1016/S1573-4412(01)05508-8

URL: Volltext  (Deutschlandweit zugänglich)

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ISBN: 9780444823403

In: Handbook of econometrics, Amsterdam : North Holland, 2001, (2001), Seite I1-I18, 9780444823403

In: 0444823409

In: year:2001

In: pages:I1-I18

Language: English

DOI: 10.1016/S1573-4412(01)05013-9

URL: Volltext  (Deutschlandweit zugänglich)

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ISBN: 9780444823403

In: Handbook of econometrics, Amsterdam : North Holland, 2001, (2001), Seite I19-I27, 9780444823403

In: 0444823409

In: year:2001

In: pages:I19-I27

Language: English

DOI: 10.1016/S1573-4412(01)05506-4

URL: Volltext  (Deutschlandweit zugänglich)