On Estimating Long-Run Effects in Models with Lagged Dependent Variables

W. Reed () and Min Zhu

Working Papers in Economics from University of Canterbury, Department of Economics and Finance

Abstract: This note points out the hazards of estimating long-run effects from models with lagged dependent variables. We use Monte Carlo experiments to demonstrate that this practice often fails to produce reliable estimates. Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While alternative procedures such as jackknifing and indirect inference address the first issue, associated estimates of long-run effects remain unreliable.

Keywords: Hurwicz bias; Auto-Regressive Distributed-Lag models; ARDL; Dynamic Panel Data models; DPD; Anderson-Hsaio; Arellano-Bond; Difference GMM; System GMM; indirect inference; jackknifing; long-run impact; long-run propensity (search for similar items in EconPapers)
JEL-codes: C22 C23 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2015-11-30
New Economics Papers: this item is included in nep-ecm and nep-ets
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https://repec.canterbury.ac.nz/cbt/econwp/1518.pdf (application/pdf)

Related works:
Journal Article: On estimating long-run effects in models with lagged dependent variables (2017)
Working Paper: On Estimating Long-Run Effects In Models with Lagged Dependent Variables (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:cbt:econwp:15/18

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