Monte Carlo Methodology and the Finite Sample Properties of Instrumental Variables Statistics for Testing Nested and Non-Nested Hypotheses

Econometrica ◽  
1991 ◽  
Vol 59 (5) ◽  
pp. 1249 ◽  
Author(s):  
Neil R. Ericsson
Keyword(s):  
Monte Carlo ◽  
Instrumental Variables ◽  
Finite Sample ◽  
Finite Sample Properties

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.

scholarly journals Additive Nonparametric Instrumental Regressions: A Guide to Implementation

2017 ◽  
Vol 6 (1) ◽  
Author(s):  
Samuele Centorrino ◽  
Frederique Feve ◽  
Jean-Pierre Florens
Keyword(s):  
Instrumental Variables ◽  
Regularization Method ◽  
Regression Models ◽  
Regression Function ◽  
Data Driven ◽  
Finite Sample ◽  
Partially Linear ◽  
Regularization Parameters ◽  
Finite Sample Properties ◽  
Additive Nonparametric Regression

AbstractWe present a review on the implementation of regularization methods for the estimation of additive nonparametric regression models with instrumental variables. We consider various versions of Tikhonov, Landweber-Fridman and Sieve (Petrov-Galerkin) regularization. We review data-driven techniques for the sequential choice of the smoothing and the regularization parameters. Through Monte Carlo simulations, we discuss the finite sample properties of each regularization method for different smoothness properties of the regression function. Finally, we present an application to the estimation of the Engel curve for food in a sample of rural households in Pakistan, where a partially linear specification is described that allows one to embed other exogenous covariates.

scholarly journals Monte Carlo Simulation Of The Portfolio-Balance Model Of Exchange Rates: Finite Sample Properties Of The GMM Estimator

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Hong-Ghi Min
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Exchange Rates ◽  
Balance Model ◽  
Finite Sample ◽  
Gmm Estimator ◽  
Portfolio Balance ◽  
Finite Sample Properties ◽  
Chi Squared ◽  
Better Than

Using Monte Carlo simulation of the Portfolio-balance model of the exchange rates, we report finite sample properties of the GMM estimator for testing over-identifying restrictions in the simultaneous equations model. F-form of Sargans statistic performs better than its chi-squared form while Hansens GMM statistic has the smallest bias.

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.

scholarly journals Testing for Multiple Structural Changes with Non-Homogeneous Regressors

2015 ◽  
Vol 7 (1) ◽  
pp. 1-35 ◽  
Author(s):  
Eiji Kurozumi
Keyword(s):  
Monte Carlo ◽  
Structural Changes ◽  
Linear Trend ◽  
Critical Values ◽  
Test Statistics ◽  
Finite Sample ◽  
Multiple Structural Changes ◽  
Finite Sample Properties ◽  
Polynomial Trends ◽  
Weighted Type

AbstractThis paper investigates tests for multiple structural changes with non-homogeneous regressors, such as polynomial trends. We consider exponential-type, supremum-type and average-type tests as well as the corresponding weighted-type tests suggested in the literature. We show that the limiting distributions depend on regressors in general, and we need to tabulate critical values depending on them. Then, we focus on the linear trend case and obtain the critical values of the test statistics. The Monte Carlo simulations are conducted to investigate the finite sample properties of the tests proposed in the paper, and it is found that the specification of the number of breaks is an important factor for the finite sample performance of the tests. Since it is often the case that we cannot prespecify the number of breaks under the alternative but can suppose only the maximum number of breaks, the weighted-type tests are useful in practice.

scholarly journals Bayesian Semiparametric Double Autoregressive Modeling

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Min Wang ◽  
Ji-Chun Liu
Keyword(s):  
Monte Carlo ◽  
Semiparametric Model ◽  
Finite Sample ◽  
Autoregressive Modeling ◽  
Return Distribution ◽  
Empirical Application ◽  
Semiparametric Modeling ◽  
Bayesian Semiparametric ◽  
Finite Sample Properties ◽  
Monte Carlo Investigation

This paper proposes a Bayesian semiparametric modeling approach for the return distribution in double autoregressive models. Monte Carlo investigation of finite sample properties and an empirical application are presented. The results indicate that the semiparametric model developed in this paper is valuable and competitive.

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