This paper proposes a new test of overidentifying restrictions (called the Q test) with high-dimensional data. This test is based on estimation and inference for a quadratic form of high-dimensional parameters. It is shown to have the desired asymptotic size and power properties under...

This paper develops a general framework for models and games, static or dynamic, in which individuals simultaneously make both discrete and continuous choices. The framework incorporates a wide range of unobserved heterogeneity. I show that such models are nonparametrically identified. Based on...

Frequently error function occurs in physics, probability and engineering. In thisarticle, the Sawi transform of error function are studied. In the application section, somenumerical applications are considered for solving improper integrals, containing errorfunction. At last we conclude that the...

This paper introduces a unified multivariate overnight GARCH-Ito model for volatility matrix estimation and prediction both in the low- and high-dimensional set-up. To account for whole-day market dynamics in the financial market, the proposed model has two different instantaneous volatility...

This paper introduces a unified parametric modeling approach for time-varying market betas that can accommodate continuous-time diffusion and discrete-time series models based on a continuous-time series regression model to better capture the dynamic evolution of market betas.We call this the...

In this paper, we propose a two-step less volatile value-at-risk (LVaR) estimation using the generalized nearly-isotonic regression (GNIR) model. The first step of our LVaR estimation is to produce a VaR sequence under the generalized autoregressive conditional heteroskedasticity (GARCH)...

This study develops a novel distribution-free maximum-likelihood estimator and formulates it for linear regression and binary response models. The estimator is consistent and asymptotically normally distributed (at rate N^(-1/2)). The Monte Carlo simulation results for the linear and binary...

When constructing parametric models to predict the cost of future claims, several important details have to be taken into account: (i) models should be designed to accommodate deductibles, policy limits, and coinsurance factors, (ii) parameters should be estimated robustly to control the...

Two election forensics methods provide estimates of the magnitude of election fraud: Bayesian version of the finite mixture model developed by Walter Mebane and his students, and nonparametric approach based on vote-turnout histograms proposed by Sergey Shpilkin. While Mebane's approach offers...

Robust Estimation of Loss Models for Lognormal Insurance Payment Severity Data Chudamani Poudyal1Department of Statistics and Data Science University of Central Florida. The primary objective of this scholarly work is to develop two estimation procedures –maximum likelihood estimator(MLE) and...