Recursive estimation in large panel data models: Theory and practice

A-Tier
Journal: Journal of Econometrics
Year: 2021
Volume: 224
Issue: 2
Pages: 439-465

Authors (4)

Jiang, Bin (not in RePEc) Yang, Yanrong (not in RePEc) Gao, Jiti (Monash University) Hsiao, Cheng (University of Southern Califor...)

Score contribution per author:

1.005 = (α=2.01 / 4 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

Bai (2009) proposes recursive estimation for panel data models with interactive effects. We study the behaviours of this recursive estimator. The recursive formula is established that shows the behaviours of recursive estimators depend on the initial estimator, the population structure and the iterative steps. Under some general scenarios, we find that the recursive estimator becomes consistent after the first iteration from any initials. We also obtain the optimal number of iterative steps under some prescribed conditions. The central limit theorem of the recursive estimator is established when the initial estimator is OLS. Various simulations are conducted to support our theoretical findings.

Technical Details

RePEc Handle
repec:eee:econom:v:224:y:2021:i:2:p:439-465
Journal Field
Econometrics
Author Count
4
Added to Database
2026-01-25