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Dealing with endogeneity in regression models with dynamic coefficients / Chang-Jin Kim.

By: Series: Foundations and trends in econometrics ; v. 3, no. 3. Publication details: Hanover : Now Publishers, c2010.Description: ix, 104 p. ; illISBN:
  • 9781601983121
Subject(s): DDC classification:
  • 338.9 23 K49
Contents:
1. Introduction-- 2. The control function approach to dealing with Endogeneity for models with constant coefficients: Basic framework-- 3. Markov-Switching models with endogenous regressors-- 4. Markov-Swithching models with endogenous switching: when the state variable and regression disturbance are correlated-- 5. Time-varying parameter models with endogenous regressors-- 6. Concluding remarks-- References.
Summary: The purpose of this monograph is to present a unified econometric framework for dealing with the issues of endogeneity in Markov-switching models and time-varying parameter models, as developed by Kim (2004, 2006, 2009), Kim and Nelson (2006), Kim et al. (2008), and Kim and Kim (2009). While Cogley and Sargent (2002), Primiceri (2005), Sims and Zha (2006), and Sims et al. (2008) consider estimation of simultaneous equations models with stochastic coefficients as a system, we deal with the LIML (limited information maximum likelihood) estimation of a single equation of interest out of a simultaneous equations model. Our main focus is on the two-step estimation procedures based on the control function approach, and we show how the problem of generated regressors can be addressed in second-step regressions.
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1. Introduction--
2. The control function approach to dealing with Endogeneity for models with constant coefficients: Basic framework--
3. Markov-Switching models with endogenous regressors--
4. Markov-Swithching models with endogenous switching: when the state variable and regression disturbance are correlated--
5. Time-varying parameter models with endogenous regressors--
6. Concluding remarks--
References.

The purpose of this monograph is to present a unified econometric framework for dealing with the issues of endogeneity in Markov-switching models and time-varying parameter models, as developed by Kim (2004, 2006, 2009), Kim and Nelson (2006), Kim et al. (2008), and Kim and Kim (2009). While Cogley and Sargent (2002), Primiceri (2005), Sims and Zha (2006), and Sims et al. (2008) consider estimation of simultaneous equations models with stochastic coefficients as a system, we deal with the LIML (limited information maximum likelihood) estimation of a single equation of interest out of a simultaneous equations model. Our main focus is on the two-step estimation procedures based on the control function approach, and we show how the problem of generated regressors can be addressed in second-step regressions.

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