Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702278
Title: Finite sample properties of the maximum likelihood estimator in continuous time models
Author: Hoyos Gomez, Nancy Milena
ISNI:       0000 0004 6057 1585
Awarding Body: University of Essex
Current Institution: University of Essex
Date of Award: 2017
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Abstract:
This dissertation consists of three papers on finite sample properties of the maximum likelihood (ML) estimator of parameters in continuous time dynamic models. In the first chapter, we obtain analytical expressions to approximate the bias and variance of the ML estimator in a univariate model with a known mean. We analyze two cases, when the variable of interest is a stock and when it is a flow. We also study the effect of the initial condition by considering both a fixed and a random initial value. A Monte Carlo study suggests that the performance of the formulae is reasonably good. Analytical bias expressions are then used in the second chapter to compute bias corrected estimators. This chapter also explores other methods for bias reduction that have been employed in the literature, these being the bootstrap, jackknife, and indirect inference. A Monte Carlo experiment shows that all approaches deliver substantial bias reductions. We also explore the robustness of the results to model misspecifications, and provide an empirical application to the broad effective exchange rate series for euro area. The third chapter derives the exact discrete representation corresponding to a cointegrated system of mixed first- and second-order stochastic differential equations with mixed sampling and observable stochastic trends. We also provide some formulae to implement the Gaussian estimation and conduct a Monte Carlo experiment to examine the finite sample properties of the Gaussian estimator. Monte Carlo simulations suggest that the bias and variance of the estimators of the short-run, long-run and adjustment coefficients as well as the variance of the intercepts are mainly determined by the data span, while the bias and variance of the covariance coefficients seem to depend on the sample size.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.702278  DOI: Not available
Keywords: HB Economic Theory
Share: