scholarly journals Heteroskedasticity, temporal and spatial correlation matter

2013 ◽  
Vol 61 (7) ◽  
pp. 2151-2155
Author(s):  
Ladislava Grochová ◽  
Luboš Střelec
Keyword(s):  
Monte Carlo ◽  
Panel Data ◽  
Spatial Correlation ◽  
Serial Correlation ◽  
Asymptotic Theory ◽  
Standard Errors ◽  
Monte Carlo Experiment ◽  
Finite Sample ◽  
Cross Sectional ◽  
Cross Sectional Dimension

As economic time series or cross sectional data are typically affected by serial correlation and/or heteroskedasticity of unknown form, panel data typically contains some form of heteroskedasticity, serial correlation and/or spatial correlation. Therefore, robust inference in the presence of heteroskedasticity and spatial dependence is an important problem in spatial data analysis. In this paper we study the standard errors based on the HAC of cross-section averages that follows Vogelsang’s (2012) fixed-b asymptotic theory, i.e. we continue with Driscoll and Kraay approach (1998). The Monte Carlo simulations are used to investigate the finite sample properties of commonly used estimators both not accounting and accounting for heteroskedasticity and spatiotemporal dependence (OLS, GLS) in comparison to brand new estimator based on Vogelsang’s (2012) fixed-b asymptotic theory in the presence of cross-sectional heteroskedasticity and serial and spatial correlation in panel data with fixed effects. Our Monte Carlo experiment shows that the OLS exhibits an important downward bias in all of the cases and almost always has the worst performance when compared to the other estimators. The GLS corrected for HACSC performs well if time dimension is greater than cross-sectional dimension. The best performance can be attributed to the Vogelsang’s estimator with fixed-b version of Driscoll-Kraay standard errors.

PANEL DATA MODELS WITH FINITE NUMBER OF MULTIPLE EQUILIBRIA

2009 ◽  
Vol 26 (3) ◽  
pp. 863-881 ◽  
Author(s):  
Jinyong Hahn ◽  
Hyungsik Roger Moon
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Multiple Equilibria ◽  
Finite Sample ◽  
Finite Support ◽  
Time Dimension ◽  
Cross Sectional ◽  
Incidental Parameters ◽  
Cross Sectional Dimension ◽  
Fixed Effects Estimator

We study a nonlinear panel data model in which the fixed effects are assumed to have finite support. The fixed effects estimator is known to have the incidental parameters problem. We contribute to the literature by making a qualitative observation that the incidental parameters problem in this model may not be not as severe as in the conventional case. Because fixed effects have finite support, the probability of correctly identifying the fixed effect converges to one even when the cross sectional dimension grows as fast as some exponential function of the time dimension. As a consequence, the finite sample bias of the fixed effects estimator is expected to be small.

Nonparametric panel data regression with parametric cross-sectional dependence

2021 ◽  
Author(s):  
Alexandra Soberon ◽  
Juan M Rodriguez-Poo ◽  
Peter M Robinson
Keyword(s):  
Panel Data ◽  
Monte Carlo Study ◽  
Generalized Least Squares ◽  
Linear Estimator ◽  
Dependence Structure ◽  
Finite Sample ◽  
Cross Sectional ◽  
Panel Data Regression ◽  
Local Linear Estimator ◽  
Cross Sectional Dependence

Abstract In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A Generalized Least Squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterizing the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analyzing the implications of the European Monetary Union for its member countries.

scholarly journals Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

Canonical Cointegrating Regression and Testing for Cointegration in the Presence of I(1) and I(2) Variables

1997 ◽  
Vol 13 (6) ◽  
pp. 850-876 ◽  
Author(s):  
In Choi ◽  
Joon Y. Park ◽  
Byungchul Yu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Asymptotic Theory ◽  
Finite Sample ◽  
Cointegration Tests ◽  
Simulation Monte Carlo ◽  
Simulation Results ◽  
Canonical Cointegrating Regression

This paper introduces tests for the null of cointegration in the presence of I(1) and I(2) variables. These tests use residuals from Park's (1992, Econometrica 60,119–143) canonical cointegrating regression (CCR) and the leads-and-lags regression of Saikkonen (1991, Econometric Theory 9,1–21) and Stock and Watson (1993, Econometrica 61, 783–820). Asymptotic theory for CCR in the presence of I(1) and I(2) variables is also introduced. The distributions of the cointegration tests are nonstandard, and hence their percentiles are tabulated by using simulation. Monte Carlo simulation results to study the finite sample performance of the CCR estimates and the cointegration tests are also reported.

scholarly journals Inference on semiparametric multinomial response models

2021 ◽  
Vol 12 (3) ◽  
pp. 743-777 ◽  
Author(s):  
Shakeeb Khan ◽  
Fu Ouyang ◽  
Elie Tamer
Keyword(s):  
Panel Data ◽  
Sufficient Conditions ◽  
Rates Of Convergence ◽  
Dynamic Panel Data ◽  
Finite Sample ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Data Set ◽  
Response Models ◽  
Section Data

We explore inference on regression coefficients in semiparametric multinomial response models. We consider cross‐sectional, and both static and dynamic panel settings where we focus throughout on inference under sufficient conditions for point identification. The approach to identification uses a matching insight throughout all three models coupled with variation in regressors: with cross‐section data, we match across individuals while with panel data, we match within individuals over time. Across models, we relax the Indpendence of Irrelevant Alternatives (or IIA assumption, see McFadden (1974)) and allow for arbitrary correlation in the unobservables that determine utility of various alternatives. For the cross‐sectional model, estimation is based on a localized rank objective function, analogous to that used in Abrevaya, Hausman, and Khan (2010), and presents a generalization of existing approaches. In panel data settings, rates of convergence are shown to exhibit a curse of dimensionality in the number of alternatives. The results for the dynamic panel data model generalize the work of Honoré and Kyriazidou (2000) to cover the semiparametric multinomial case. A simulation study establishes adequate finite sample properties of our new procedures. We apply our estimators to a scanner panel data set.

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.

An Introduction to the Augmented Inverse Propensity Weighted Estimator

2010 ◽  
Vol 18 (1) ◽  
pp. 36-56 ◽  
Author(s):  
Adam N. Glynn ◽  
Kevin M. Quinn
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Regression Model ◽  
Interesting Property ◽  
Outcome Variable ◽  
Monte Carlo Experiment ◽  
Finite Sample ◽  
Propensity Score Model ◽  
Matching Estimator ◽  
Score Model

In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. Perhaps the most interesting property of this estimator is its so-called “double robustness.” Put simply, the estimator remains consistent for the ATE if either the propensity score model or the outcome regression is misspecified but the other is properly specified. After explaining the AIPW estimator, we conduct a Monte Carlo experiment that compares the finite sample performance of the AIPW estimator to three common competitors: a regression estimator, an inverse propensity weighted (IPW) estimator, and a propensity score matching estimator. The Monte Carlo results show that the AIPW estimator has comparable or lower mean square error than the competing estimators when the propensity score and outcome models are both properly specified and, when one of the models is misspecified, the AIPW estimator is superior.

SEMIPARAMETRIC ESTIMATION OF MULTIPLE EQUATION MODELS

2000 ◽  
Vol 16 (4) ◽  
pp. 551-575 ◽  
Author(s):  
Gabriel A. Picone ◽  
J.S. Butler
Keyword(s):  
Monte Carlo ◽  
Choice Model ◽  
Average Distance ◽  
Sample Selection ◽  
Semiparametric Estimation ◽  
Monte Carlo Experiment ◽  
Finite Sample ◽  
Semiparametric Estimator ◽  
Multinomial Choice ◽  
Multiple Equation Models

This paper proposes a semiparametric estimator for multiple equations multiple index (MEMI) models. Examples of MEMI models include several sample selection models and the multinomial choice model. The proposed estimator minimizes the average distance between the dependent variable unconditional and conditional on an index. The estimator is √N-consistent and asymptotically normally distributed. The paper also provides a Monte Carlo experiment to evaluate the finite-sample performance of the estimator.

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