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- V. Joseph Hotz & Robert A. Miller (1993): Conditional Choice Probabilities and the Estimation of Dynamic Models
This paper develops a new method for estimating the structural parameters of (discrete choice) dynamic programming problems. ... We show the valuation functions characterizing the expected future utility associated with the choices often can be represented as an easily computed function of the state variables, structural parameters, and the probabilities of choosing alternative actions for states which are feasible in the future. Under certain conditions, nonparametric estimators of these probabilities can be formed from sample information on the relative frequencies of observed choices using observations with the same (or similar) state variables. Substituting the estimators for the true conditional choice probabilities in formulating optimal decision rules, we establish the consistency and asymptotic normality of the resulting structural parameter estimators. To illustrate our new method, we estimate a dynamic model of parental contraceptive choice and fertility using data from the National Fertility Survey.
RePEc:oup:restud:v:60:y:1993:i:3:p:497-529. Save to MyIDEAS - V. Joseph Hotz & Robert A. Miller (1992): Conditional Choice Probabilities and the Estimation of Dynamic Models
This paper develops a new method for estimating the structural parameters of dynamic programming problems in which choices are discrete. ... We show the valuation functions characterizing the expected future utility function associated with such choices often can be represented as an easily computed function of the state variables, structural parameters, and the probabilities of choosing alternative actions for states which are feasible in the future. Under certain conditions, nonparametric estimators of these probabilities can be formed from sample information on the relative frequencies of observed choices using observations with the same (or similar) state variables. Substituting the estimators for the true conditional choice probabilities in formulating optimal decision rules, we establish the consistency and asymptotic normality of the resulting structural parameter estimators. To illustrate our new method, we estimate a dynamic model of parental contraceptive choice and fertility using data from the National Fertility Survey.
RePEc:har:wpaper:9202 Save to MyIDEAS - Hotz, V.J. & Miller, R.A. (1991): Conditional Choice Probabilities and the Estimation of Dynamic Models
This paper develops a new method for estimating the structural parameters of (discrete choice) dynamic programming problems. ... We show the valuation functions characterizing the expected future utility associated with the choices often can be represented as an easily computed function of the state variables, structural parameters, and the probabilities of choosing alternative actions for states which are feasible in the future. Under certain conditions, nonparametric estimators of these probabilities can be formed from sample information on the relative frequencies of observed choices using observations with the same (or similar) state variables. Substituting the estimators for the true conditional choice probabilities in formulating optimal decision rules, we establish the consistency and asymptotic normality of the resulting structural parameter estimators. To illustrate our new method, we estimate a dynamic model of parental contraceptive choice and fertility using data from the National Fertility Survey.
RePEc:cmu:gsiawp:1992-12 Save to MyIDEAS - HOTZ, V.J. & MILLER, R.A. (1989): Conditional Choice Probabilities And The Estimation Of Dynamic Discrete Choice Models
No abstract is available for this item.
RePEc:fth:chicer:89-02 Save to MyIDEAS - Bruneel-Zupanc, Christophe Alain (2021): Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation
This paper develops a general framework for models, static or dynamic, in which agents simultaneously make both discrete and continuous choices. ... Based on the constructive identification arguments, I build a novel two-step estimation method in the lineage of Hotz and Miller (1993) but extended to discrete and continuous choice models.
RePEc:tse:wpaper:125232 Save to MyIDEAS - V. Joseph Hotz & Robert A. Miller (undated): Conditional Choice Probabilities and the Estimation of Dynamic Discrete Choice Models
No abstract is available for this item.
RePEc:fth:chiprc:89-2a Save to MyIDEAS - Peter Arcidiacono & Robert A. Miller (2011): Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity
No abstract is available for this item.
RePEc:ecm:emetrp:v:79:y:2011:i:6:p:1823-1867 Save to MyIDEAS - HOTZ, V.J. & MILLER, R.A. (1988): Conditional Choice Probabilities And The Estimation Of Dynamic Discrete Choice Models
No abstract is available for this item.
RePEc:cmu:gsiawp:88-89-10 Save to MyIDEAS - Yu Hao & Hiroyuki Kasahara (2024): Conditional Choice Probability Estimation of Dynamic Discrete Choice Models with 2-period Finite Dependence
This paper extends the work of Arcidiacono and Miller (2011, 2019) by introducing a novel characterization of finite dependence within dynamic discrete choice models, demonstrating that numerous models display 2-period finite dependence. ... With the estimated weights, we develop a computationally attractive Conditional Choice Probability estimator with 2-period finite dependence.
RePEc:arx:papers:2405.12467 Save to MyIDEAS - V. Joseph Hotz & Robert A. Miller & Seth Sanders & Jeffrey Smith (1992): A Simulation Estimator for Dynamic Models of Discrete Choice
This paper extends the recent work of Hotz and Miller (1991) on the use of conditional choice probabilities to represent the valuation functions in the estimation of dynamic, discrete choice models. They derive a N1/2 consistent and asymptotically normal estimator of the structural parameters of agents' optimal decision rules that relies on nonparametric estimates of the conditional choice probabilities of future choices. This paper extends their work by deriving a related estimator that does not require the estimation of the conditional choice probabilities of all future paths associated with a current action, but rather only those associated with a simulated path or paths. ... By reducing the number of conditional choice probabilities that must be estimated, this new estimator allows the consideration of models with large numbers of alternative choices.
RePEc:har:wpaper:9205 Save to MyIDEAS