Inferences on the regression coefficients in panel data models: parametric bootstrap approach

A Esmaeli-Ayan,A Malekzadeh,F Hormozinejad

doi:10.1007/s40096-019-00316-6

Abstract

This article presents a parametric bootstrap approach to inference on the regression coefficients in panel data models. We aim to propose a method that is easily applicable for implement hypothesis testing and construct confidence interval of the regression coefficients vector of balanced and unbalanced panel data models. We show the results of our simulation study to compare of our parametric bootstrap approach with other approaches and approximated methods based on a Monte Carlo simulation study.

Highlights

Panel data are the combination of observations on a cross section of individuals, cities, factories, etc., over many time periods
We aim to propose a method that is applicable for implement hypothesis testing and construct confidence interval of the regression coefficient vector of balanced and unbalanced panel data models
We present the results of our simulation study to compare the size and powers of our PB approach with generalized p values by [23] and approximated methods based on a Monte Carlo simulation study. we use the abbreviation PB, GPV and AP to refer these three methods

Summary

Introduction

Panel data are the combination of observations on a cross section of individuals, cities, factories, etc., over many time periods. We aim to propose a method that is applicable for implement hypothesis testing and construct confidence interval of the regression coefficient vector of balanced and unbalanced panel data models. Our PB approaches for hypothesis testing and constructing confidence region about the regression coefficients vector are presented for the balanced and unbalanced panel data models in section “PB inferences for the regression coefficients”. To construct a confidence region for in this case, we propose to use a similar random quantity H in (2.15) and PB approach to approximated its distribution. [23] only proposed a generalized p value method for testing H0 ∶ = ∗ v.s H1 ∶ ≠ ∗ in balanced panel data state. For a given (N, T) and ( , , 2, 2) , generate and compute s21, s2ν, ̃ 0 , ̃ 0 and observed value of H from (2.15), i.e. h0 , respectively

Generate

Conclusions