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: A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment. This note points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results. 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.

Keywords: Hurwicz bias; Auto-Regressive Distributed-Lag (ARDL) models; Dynamic Panel Data (DPD) models; DPD estimators; long-run impact; long-run propensity; Fieller’s method; indirect inference; jackknifing (search for similar items in EconPapers)
JEL-codes: C22 C23 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2016-07-28
New Economics Papers: this item is included in nep-ets
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https://repec.canterbury.ac.nz/cbt/econwp/1616.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 (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:cbt:econwp:16/16

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