scholarly journals Finite Sample Inference for the Maximum Score Estimand

2020 ◽  
Author(s):  
Takuya Ura ◽  
Adam Rosen
Keyword(s):  
Finite Sample ◽  
Maximum Score ◽  
Finite Sample Inference

scholarly journals Monte Carlo Inference on Two-Sided Matching Models

Econometrics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 16
Author(s):  
Taehoon Kim ◽  
Jacob Schwartz ◽  
Kyungchul Song ◽  
Yoon-Jae Whang
Keyword(s):  
Monte Carlo ◽  
Finite Sample ◽  
Cross Sectional ◽  
Monte Carlo Simulation Study ◽  
Complicated Form ◽  
Cross Sectional Dependence ◽  
Matching Mechanism ◽  
Matching Models ◽  
Finite Sample Inference ◽  
Asymptotic Validity

This paper considers two-sided matching models with nontransferable utilities, with one side having homogeneous preferences over the other side. When one observes only one or several large matchings, despite the large number of agents involved, asymptotic inference is difficult because the observed matching involves the preferences of all the agents on both sides in a complex way, and creates a complicated form of cross-sectional dependence across observed matches. When we assume that the observed matching is a consequence of a stable matching mechanism with homogeneous preferences on one side, and the preferences are drawn from a parametric distribution conditional on observables, the large observed matching follows a parametric distribution. This paper shows in such a situation how the method of Monte Carlo inference can be a viable option. Being a finite sample inference method, it does not require independence or local dependence among the observations which are often used to obtain asymptotic validity. Results from a Monte Carlo simulation study are presented and discussed.

scholarly journals Finite sample inference for quantile regression models

2009 ◽  
Vol 152 (2) ◽  
pp. 93-103 ◽  
Author(s):  
Victor Chernozhukov ◽  
Christian Hansen ◽  
Michael Jansson
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Finite Sample ◽  
Finite Sample Inference

Finite Sample Inference for Multiply Imputed Synthetic Data under a Multiple Linear Regression Model

2019 ◽  
Vol 71 (2) ◽  
pp. 63-82
Author(s):  
Martin D. Klein ◽  
John Zylstra ◽  
Bimal K. Sinha
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Synthetic Data ◽  
Multiple Linear Regression Model ◽  
Finite Sample ◽  
Current State ◽  
Finite Sample Inference ◽  
Multiply Imputed

In this article, we develop finite sample inference based on multiply imputed synthetic data generated under the multiple linear regression model. We consider two methods of generating the synthetic data, namely posterior predictive sampling and plug-in sampling. Simulation results are presented to confirm that the proposed methodology performs as the theory predicts and to numerically compare the proposed methodology with the current state-of-the-art procedures for analysing multiply imputed partially synthetic data. AMS 2000 subject classification: 62F10, 62F25, 62J05

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.

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