摘要：This paper considers estimation and inference in panel vector autoregressions (PVARs) with
fixed effects when the time dimension of the panel is finite, and the cross-sectional dimension
is large. A Maximum Likelihood (ML) estimator based .on a transformed. likelihood function
is proposed and shown to be consistent and asymptotically normally distributed. irrespective of
the unit root and cointegrating properties of the underlying PVAR model. The transformed.
likelihood framework is also used to derive unit root and cointegration tests in panels with short
time dimension; these tests have the attractive feature that they are based. on standard chisquare
and normal distributed statistics. Examining Generalized Method of Moments (GMM)
estimation as an alternative to our proposed. ML estimator, it is shown that conventional GMM
estimators based on standard orthogonality conditions break down if the underlying time series
contain unit roots. Also, the implementation of extended GMM estimators making use of
variants of homosked.asticity and stationarity restrictions as suggested in the literature in a
univariate context is subject to difficulties. Monte Carlo evidence is adduced suggesting that
the ML estimator and parameter hypothesis and cointegration tests based on it perform well in
small sample; this is in marked contrast to the small sample performance of the G MM estimators
