While differencing transformations can eliminate nonstationarity, they typically reduce signal strength and correspondingly reduce rates of convergence in unit root autoregressions. The present paper shows that aggregating moment conditions that are formulated in differences provides an orderly...

We analyze optimality properties of maximum likelihood (ML) and other estimators when the problem does not necessarily fall within the locally asymptotically normal (LAN) class, therefore covering cases that are excluded from conventional LAN theory such as unit root nonstationary time series....

Using the power kernels of Phillips, Sun and Jin (2006, 2007), we examine the large sample asymptotic properties of the t-test for different choices of power parameter (rho). We show that the nonstandard fixed-rho limit distributions of the t-statistic provide more accurate approximations to the...

Maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size and large cross section sample size asymptotics. This paper proposes a general, computationally inexpensive method of bias...

The asymptotic local power of various panel unit root tests are investigated. The (Gaussian) power envelope is obtained under homogeneous and heterogeneous alternatives. The envelope is compared with the asymptotic power functions for the pooled t- test, the Ploberger-Phillips (2002) test, and a...

Explicit asymptotic bias formulae are given for dynamic panel regression estimators as the cross section sample size N\rightarrow\infty. The results extend earlier work by Nickell (1981) in several directions that are relevant for practical work, including models with unit roots, deterministic...

An infinite-order asymptotic expansion is given for the autocovariance function of a general stationary long-memory process with memory parameter d[set membership, variant](-1/2,1/2). The class of spectral densities considered includes as a special case the stationary and invertible...