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2012 | OriginalPaper | Chapter

10. Semiparametric Dynamic Proportional Hazard Models

Author : Professor Dr. Nikolaus Hautsch

Published in: Econometrics of Financial High-Frequency Data

Publisher: Springer Berlin Heidelberg

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Abstract

Proportional hazard (PH) models as introduced in Chap. 4 have a long history in labor economics and serve as a workhorse for the modelling of unemployment spells. Here, we discuss dynamic extensions which can be seen as the direct counterpart to the class of dynamic accelerated failure time (AFT) models to which the ACD model belongs to. As discussed in Chap. 4, a PH model can be estimated in different ways. One possibility is to adopt a fully parametric approach leading to a complete parameterization of the hazard function. Such a model is consistently estimated by maximum likelihood given that the chosen parameterization is correct.

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previous chapter Estimating Market Liquidity

next chapter Univariate Dynamic Intensity Models

See, for example, Han and Hausman (1990), Meyer (1990), Horowitz and Neumann (1987) or Horowitz (1996, 1999).

For an extended discussion of generalized errors in the context of non-dynamic models, see Gouriéroux et al. (1987).

See, e.g., Lancaster (1979) or Heckmann and Singer (1984) among others.

Recall that the BurrII(1) distribution coincides with the logistic distribution.

Albert J, Chib S (1995) Bayesian residual analysis for binary response regression models. Biometrika 82:747–759CrossRef

Cox DR (1981) Statistical analysis of time series: some recent developments. Scand J Stat 8:93–115

Cox DR (1975) Partial likelihood. Biometrika 62:269CrossRef

Gerhard F, Hautsch N (2007) A dynamic semiparametric proportional hazard model. Stud Nonlinear Dynamics Econometrics 11, http://www.bepress.com/snde/vol11/iss2/art1

Gouriéroux C, Monfort A, Renault E, Trognon A (1987) Generalised residuals. J Econom 34: 5–32CrossRef

Gouriéroux C, Monfort A, Trognon A (1985) A general approach to serial correlation. Econom Theory pp. 315–340

Han A, Hausman JA (1990) Flexible parametric estimation of duration and competing risk models. J Appl Econom 5:1–28CrossRef

Hautsch N (1999) Analyzing the time between trades with a gamma compounded hazard model. An Application to LIFFE Bund Future Transctions Discussion Paper 99/03, Center of Finance and Econometrics, University of Konstanz, Konstanz

Hautsch N, Pohlmeier W (2002) Econometric analysis of financial transaction data: pitfalls and opportunities. Allgemeines Statistisches Archiv 86:5–30CrossRef

Heckmann JJ, Singer B (1984) Econometrics duration analysis. J Econom 24:63–132CrossRef

Horowitz JL (1996) Semiparametric estimation of a regression model with an unknown transformation of the dependent variable. Econometrica 64:103–137CrossRef

Horowitz JL, Neumann GR (1987) Semiparametric estimation of employment duration models. Econometric Review 6(1):5–40CrossRef

Kiefer NM (1988) Economic duration data and hazard functions. J Econ Lit 26:646–679

Lancaster T (1979) Econometric methods for the duration of unemployment. Econometrica 47(4):939–956CrossRef

Lancaster T (1997) The econometric analysis of transition data. Cambridge University Press

Meyer BD (1990) Unemployment insurance and unemployment spells. Econometrica 58(4): 757–782CrossRef

Oakes D, Cui L (1994) On semiparametric inference for modulated renewal processes. Biometrika 81:83–90CrossRef

Prentice RL, Gloeckler LA (1978) Regression analysis of grouped survival data with application to breast cancer data. Biometrics 34:57–67CrossRef

Romeo CJ (1999) Conducting inference in semiparametric duration models and inequality restrictions on the shape of the hazard implied by job search theory. J Appl Econom 14: 587–605CrossRef

Sueyoshi GT (1995) A class of binary response models for grouped duration data. J Appl Econom 10:411–431CrossRef

Thompson WA (1977) On the treatment of grouped durations in life studies. Biometrics 33: 463–470CrossRef

Title: Semiparametric Dynamic Proportional Hazard Models
Author: Professor Dr. Nikolaus Hautsch
Publisher: Springer Berlin Heidelberg
Book: Econometrics of Financial High-Frequency Data
Print ISBN: 978-3-642-21924-5

Electronic ISBN: 978-3-642-21925-2

Copyright Year: 2012
DOI: https://doi.org/10.1007/978-3-642-21925-2_10