Consistency properties of a simulation-based estimator for dynamic processes

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper considers a simulation-based estimator for a general class of Markovian processes and explores some strong consistency properties of the estimator. The estimation problem is defined over a continuum of invariant distributions indexed by a vector of parameters. A key step in the method of proof is to show the uniform convergence (a.s.) of a family of sample distributions over the domain of parameters. This uniform convergence holds under mild continuity and monotonicity conditions on the dynamic process. The estimator is applied to an asset pricing model with technology adoption. A challenge for this model is to generate the observed high volatility of stock markets along with the much lower volatility of other real economic aggregates.

Original languageEnglish (US)
Pages (from-to)196-213
Number of pages18
JournalAnnals of Applied Probability
Volume20
Issue number1
DOIs
StatePublished - Feb 2010

Fingerprint

Dynamic Process
Uniform convergence
Estimator
Volatility
Technology Adoption
Markovian Process
Asset Pricing
Invariant Distribution
Simulation
Strong Consistency
Stock Market
Monotonicity
Continuum
Economics
Model
Dynamic process

Keywords

  • Invariant probability
  • Markov process
  • Monotonicity
  • Sample distribution
  • Simulation-based estimation
  • Strong consistency

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Consistency properties of a simulation-based estimator for dynamic processes. / Santos, Manuel.

In: Annals of Applied Probability, Vol. 20, No. 1, 02.2010, p. 196-213.

Research output: Contribution to journalArticle

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