ESTIMATION AND INFERENCE IN SHORT PANEL VECTOR AUTOREGRESSIONS WITH UNIT ROOTS AND COINTEGRATION

By Hashem Pesaran. Econometric Theory, 2005

361 citations recorded by the source corpus.

This paper considers estimation and inference in panel vector autoregressions where (i) the individual effects are either random or fixed, (ii) the time-series properties of the model variables are unknown a priori and may feature unit roots and cointegrating relations, and (iii) the time dimension of the panel is short and its cross-sectional dimension is large. Generalized method of moments (GMM) and quasi maximum likelihood (QML) estimators are obtained and compared in terms of their asymptotic and finite-sample properties. It is shown that the asymptotic variances of the GMM estimators that are based on levels in addition to first differences of the model variables depend on the variance of the individual effects, whereas by construction the fixed effects QML estimator is not subject to this problem. Monte Carlo evidence is provided showing that the fixed effects QML estimator tends to outperform the various GMM estimators in finite sample under both normal and nonnormal errors. The paper also shows how the fixed effects QML estimator can be successfully used for unit root and cointegration tests in short panels.We are grateful to Karim Abadir, Stephen Bond, Jinyong Hahn, Marc Nerlove, Ingmar Prucha, and, especially, Manuel Arellano, Peter Schmidt, Peter Phillips (the editor), and four anonymous referees for helpful and constructive comments. We have also benefited from useful suggestions by participants at various seminars and conferences.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/93e637c8cf571254826d9ca486b5c2d422353e28

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Citations in the corpus

Panel data analysis—advantages and challenges (2007, neutral): “It also has the advantage of making use of all the available sample, hence, may yield a more efficient estimator than (22) (e.g., Hsiao et al. 2002; Binder et al. 2005).”
Unit Roots and Cointegration in Panels (2005, neutral): “This confirms theoretical results on the local power of panel unit root tests derived by Breitung (2000), Ploberger and Phillips (2002) and Moon et al.”
Similarities and Convergence in G-7 Cycles (2007, background): “From an economic point of view, the decomposition in (4) is convenient since it naturally allows us to assess the relative importance of world and country specific influences for fluctuations in .”
Estimation of linear dynamic panel data models with time‐invariant regressors (2019, background): “However, as Binder et al. (2005) and Bun and Windmeijer (2010) emphasize, GMM estimators might suffer from a weak instruments problem when the autoregressive parameter approaches unity or when the variance of the unobserved unit-specific effects is large.”
Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure (2006, background): “4) where uwt = PN i=1 wiuit, uit is dened by (5) and the weights, wi, satisfy the conditions in (14).”
Forecasting with Panel Data (2008, background): “for a survey of nonstationary panels and Binder, Pesaran and Hsiao (2005) for estimation”
How Does Objective Quality Affect Perceived Quality? Short-Term Effects, Long-Term Effects, and Asymmetries (2006, result): “These results are consistent with Pesaran and Shin (1999) and Binder et al. (2000) who show that as the number of cross sections increase, autoregressive distributed lag models yield asymptotically consistent parameter estimates irrespective of stationary or nonstationary regressors.”
Maximum likelihood estimation and inference methods for the covariance stationary panel AR(1)/unit root model (2008, neutral): “An explicit expression for ( EHMρ,σ2 −EHFρ,σ2 ) is given in (8)-(9).”
Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models (2015, neutral): “Binder et al. (2005) have extended this approach for estimating panel VAR (PVAR) models.”