|
Activity Number:
|
403
- SPAAC Poster Competition
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Nonparametric Statistics
|
|
Abstract #301754
|
|
|
Title:
|
Estimation of Semiparametric Functional Coefficients Panel Data Model
|
|
Author(s):
|
Shaymal Halder* and Emir Malikov
|
|
Companies:
|
Auburn University and Auburn University
|
|
Keywords:
|
panel data;
functional coefficient;
fixed effect;
local linear regression;
monte carlo simulation;
finite sample performance
|
|
Abstract:
|
We consider the problem of estimating a semiparametric varying coefficient panel data model where the unobserved individual effects are correlated with explanatory variables in an unknown arbitrary way using a local linear regression approach. We present a new technique to estimate this model along the lines of Sun & Malikov’s (2015; 2018) whereby, unlike Rodriguez & Soberon’s (2014) alternative approach, we locally approximate the fixed-effects-free transformed equation around two different points. Using Monte Carlo simulations, we study potential gains in the finite sample performance and/or the computational time of the proposed estimation procedure over available alternatives under different scenarios.
|
Authors who are presenting talks have a * after their name.
