scholarly journals Smoothed Maximum Score Estimation of Discrete Duration Models

2019 ◽  
Vol 12 (2) ◽  
pp. 64 ◽  
Author(s):  
Sadat Reza ◽  
Paul Rilstone
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Discrete Time ◽  
Hazard Rate ◽  
Duration Models ◽  
Finite Sample ◽  
Time Duration ◽  
Maximum Score ◽  
Finite Sample Properties ◽  
Error Distributions

This paper extends Horowitz’s smoothed maximum score estimator to discrete-time duration models. The estimator’s consistency and asymptotic distribution are derived. Monte Carlo simulations using various data generating processes with varying error distributions and shapes of the hazard rate are conducted to examine the finite sample properties of the estimator. The bias-corrected estimator performs reasonably well for the models considered with moderately-sized samples.

9. Dynamic Discrete-Time Duration Models: Estimation Viamarkov Chain Monte Carlo

1997 ◽  
Vol 27 (1) ◽  
pp. 417-452 ◽  
Author(s):  
Ludwig Fahrmeir ◽  
Leonhard Knorr-Held
Keyword(s):  
Monte Carlo ◽  
Discrete Time ◽  
Duration Models ◽  
Time Duration

scholarly journals STRUCTURAL CHANGE IN NONSTATIONARY AR(1) MODELS

2017 ◽  
Vol 34 (5) ◽  
pp. 985-1017 ◽  
Author(s):  
Tianxiao Pang ◽  
Terence Tai-Leung Chong ◽  
Danna Zhang ◽  
Yanling Liang
Keyword(s):  
Monte Carlo ◽  
Structural Change ◽  
Monte Carlo Simulations ◽  
Change Point ◽  
Indicator Function ◽  
The Other ◽  
Finite Sample ◽  
Mild Conditions ◽  
Finite Sample Properties ◽  
Image Position

This article revisits the asymptotic inference for nonstationary AR(1) models of Phillips and Magdalinos (2007a) by incorporating a structural change in the AR parameter at an unknown time k0. Consider the model ${y_t} = {\beta _1}{y_{t - 1}}I\{ t \le {k_0}\} + {\beta _2}{y_{t - 1}}I\{ t > {k_0}\} + {\varepsilon _t},t = 1,2, \ldots ,T$, where I{·} denotes the indicator function, one of ${\beta _1}$ and ${\beta _2}$ depends on the sample size T, and the other is equal to one. We examine four cases: Case (I): ${\beta _1} = {\beta _{1T}} = 1 - c/{k_T}$, ${\beta _2} = 1$; (II): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 - c/{k_T}$; (III): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 + c/{k_T}$; and case (IV): ${\beta _1} = {\beta _{1T}} = 1 + c/{k_T}$, ${\beta _2} = 1$, where c is a fixed positive constant, and kT is a sequence of positive constants increasing to ∞ such that kT = o(T). We derive the limiting distributions of the t-ratios of ${\beta _1}$ and ${\beta _2}$ and the least squares estimator of the change point for the cases above under some mild conditions. Monte Carlo simulations are conducted to examine the finite-sample properties of the estimators. Our theoretical findings are supported by the Monte Carlo simulations.

scholarly journals Evaluation of Heliostat Field Global Tracking Error Distributions by Monte Carlo Simulations

2014 ◽  
Vol 49 ◽  
pp. 1308-1317 ◽  
Author(s):  
L.A. Díaz-Félix ◽  
M. Escobar-Toledo ◽  
J. Waissman ◽  
N. Pitalúa-Díaz ◽  
C.A. Arancibia-Bulnes
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Tracking Error ◽  
Global Tracking ◽  
Error Distributions

scholarly journals Fixed and Long Time Span Jump Tests: New Monte Carlo and Empirical Evidence

Econometrics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 13 ◽  
Author(s):  
Mingmian Cheng ◽  
Norman Swanson
Keyword(s):  
Monte Carlo ◽  
Fixed Time ◽  
Time Span ◽  
Finite Sample ◽  
Long Span ◽  
Finite Sample Properties ◽  
Jump Intensity ◽  
Data Generating Process ◽  
Long Time ◽  
Robust Tests

Numerous tests designed to detect realized jumps over a fixed time span have been proposed and extensively studied in the financial econometrics literature. These tests differ from “long time span tests” that detect jumps by examining the magnitude of the jump intensity parameter in the data generating process, and which are consistent. In this paper, long span jump tests are compared and contrasted with a variety of fixed span jump tests in a series of Monte Carlo experiments. It is found that both the long time span tests of Corradi et al. (2018) and the fixed span tests of Aït-Sahalia and Jacod (2009) exhibit reasonably good finite sample properties, for time spans both short and long. Various other tests suffer from finite sample distortions, both under sequential testing and under long time spans. The latter finding is new, and confirms the “pitfall” discussed in Huang and Tauchen (2005), of using asymptotic approximations associated with finite time span tests in order to study long time spans of data. An empirical analysis is carried out to investigate the implications of these findings, and “time-span robust” tests indicate that the prevalence of jumps is not as universal as might be expected.

Sign in / Sign up

Export Citation Format

Share Document