Journal
JOURNAL OF ECONOMETRICS
Volume 193, Issue 1, Pages 54-75Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2016.01.006
Keywords
Random Coefficient Autoregression; Panel data; WLS estimator; Common factors
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Funding
- NSF [DMS 0905400]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1305858] Funding Source: National Science Foundation
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This paper studies the asymptotics of the Weighted Least Squares (WLS) estimator of the autoregressive root in a panel Random Coefficient Autoregression (RCA). We show that, in an RCA context, there is no unit root problem : the WLS estimator is always asymptotically normal, irrespective of the average value of the autoregressive root, of whether the autoregressive coefficient is random or not, and of the presence and degree of cross dependence. Our simulations indicate that the estimator has good properties, and that confidence intervals have the correct coverage even for sample sizes as small as (N, T) = (10, 25). We illustrate our findings through two applications to macroeconomic and financial variables. (C) 2016 Elsevier B.V. All rights reserved.
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