Bootstrap-based bias correction and inference for dynamic panels with fixed effects
(2015) In Stata Journal 15(4). p.986-1018- Abstract
- In this article, we describe a new command, xtbcfe, that performs the iterative bootstrap-based bias correction for the fixed-effects estimator in dynamic panels proposed by Everaert and Pozzi (2007, Journal of Economic Dynamics and Control 31: 1160–1184). We first simplify the core of their algorithm by using the invariance principle and subsequently extend it to allow for unbalanced and higher order dynamic panels. We implement various bootstrap error resampling schemes to account for general heteroskedasticity and contemporaneous cross-sectional dependence. Inference can be performed using a bootstrapped variance–covariance matrix or percentile intervals. Monte Carlo simulations show that the simplification of the original algorithm... (More)
- In this article, we describe a new command, xtbcfe, that performs the iterative bootstrap-based bias correction for the fixed-effects estimator in dynamic panels proposed by Everaert and Pozzi (2007, Journal of Economic Dynamics and Control 31: 1160–1184). We first simplify the core of their algorithm by using the invariance principle and subsequently extend it to allow for unbalanced and higher order dynamic panels. We implement various bootstrap error resampling schemes to account for general heteroskedasticity and contemporaneous cross-sectional dependence. Inference can be performed using a bootstrapped variance–covariance matrix or percentile intervals. Monte Carlo simulations show that the simplification of the original algorithm results in a further bias reduction for very small T. The Monte Carlo results also support the bootstrap-based bias correction in higher order dynamic panels and panels with cross-sectional dependence. We illustrate the command with an empirical example estimating a dynamic labor–demand function. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d208a147-fdd8-478f-a4ad-de872d290a33
- author
- De Vos, Ignace LU ; Everaert, Gerdie and Ruyssen, Ilse
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- xtbcfe, bootstrap-based bias correction, dynamic panel data, unbalanced, higher order, Heteroskedasticity, cross-sectional dependence, Monte Carlo, labor demand, Bootstrap
- in
- Stata Journal
- volume
- 15
- issue
- 4
- pages
- 986 - 1018
- publisher
- StataCorp LP
- external identifiers
-
- scopus:85000916330
- ISSN
- 1536-867X
- language
- English
- LU publication?
- no
- id
- d208a147-fdd8-478f-a4ad-de872d290a33
- alternative location
- http://www.stata-journal.com/article.html?article=st0396
- date added to LUP
- 2018-11-08 14:33:36
- date last changed
- 2022-04-25 18:47:00
@article{d208a147-fdd8-478f-a4ad-de872d290a33,
abstract = {{In this article, we describe a new command, xtbcfe, that performs the iterative bootstrap-based bias correction for the fixed-effects estimator in dynamic panels proposed by Everaert and Pozzi (2007, Journal of Economic Dynamics and Control 31: 1160–1184). We first simplify the core of their algorithm by using the invariance principle and subsequently extend it to allow for unbalanced and higher order dynamic panels. We implement various bootstrap error resampling schemes to account for general heteroskedasticity and contemporaneous cross-sectional dependence. Inference can be performed using a bootstrapped variance–covariance matrix or percentile intervals. Monte Carlo simulations show that the simplification of the original algorithm results in a further bias reduction for very small T. The Monte Carlo results also support the bootstrap-based bias correction in higher order dynamic panels and panels with cross-sectional dependence. We illustrate the command with an empirical example estimating a dynamic labor–demand function.}},
author = {{De Vos, Ignace and Everaert, Gerdie and Ruyssen, Ilse}},
issn = {{1536-867X}},
keywords = {{xtbcfe; bootstrap-based bias correction; dynamic panel data; unbalanced; higher order; Heteroskedasticity; cross-sectional dependence; Monte Carlo; labor demand; Bootstrap}},
language = {{eng}},
number = {{4}},
pages = {{986--1018}},
publisher = {{StataCorp LP}},
series = {{Stata Journal}},
title = {{Bootstrap-based bias correction and inference for dynamic panels with fixed effects}},
url = {{http://www.stata-journal.com/article.html?article=st0396}},
volume = {{15}},
year = {{2015}},
}