Arbeitspapier

Parameter Driven Multi-state Duration Models: Simulated vs. Approximate Maximum Likelihood Estimation

Likelihood based inference for multi-state latent factor intensity models is hindered by the fact that exact closed-form expressions for the implied data density are not available. This is a common and well-known problem for most parameter driven dynamic econometric models. This paper reviews, adapts and compares three different approaches for solving this problem. For evaluating the likelihood, two of the methods rely on Monte Carlo integration with importance sampling techniques. The third method, in contrast, is based on fully deterministic numerical procedures. A Monte Carlo study is conducted to illustrate the use of each method, and assess its corresponding finite sample performance.

Language
Englisch

Bibliographic citation
Series: Tinbergen Institute Discussion Paper ; No. 08-021/2

Classification
Wirtschaft
Statistical Simulation Methods: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
Duration Analysis; Optimal Timing Strategies
Subject
Multi-state Duration models
Parameter Driven models
Simulated Maximum Likelihood
Importance Sampling
Statistische Bestandsanalyse
Maximum-Likelihood-Methode
Stichprobenverfahren
Zeitreihenanalyse
Theorie

Event
Geistige Schöpfung
(who)
Monteiro, André A.
Event
Veröffentlichung
(who)
Tinbergen Institute
(where)
Amsterdam and Rotterdam
(when)
2008

Handle
Last update
10.03.2025, 11:45 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Monteiro, André A.
  • Tinbergen Institute

Time of origin

  • 2008

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