In a recent paper, Engel, C. (1999) presents monte-carlo evidence to suggest that unit root tests cannot detect a non-stationary component in the real exchange rate even when this component accounts for almost half of its longhorizon forecast error variance. This hidden non-stationary component...

In a recent paper, Engel, C. (1999) presents monte-carlo evidence to suggest that unit root tests cannot detect a non-stationary component in the real exchange rate even when this component accounts for almost half of its longhorizon forecast error variance. This hidden non-stationary component...

It is widely known that when there are negative moving average errors, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and BIC tend to select a truncation lag that is very small. Furthermore, size distortions...

Datasets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data. While more data are better than less, diminishing returns suggest that we may not need terabytes of data to estimate a parameter or test a hypothesis. But which...

We consider the situation when there is a large number of series, $N$, each with $T$ observations, and each series has some predictive ability for the variable of interest, $y$. A methodology of growing interest is to first estimate common factors from the panel of data by the method of...

This paper studies the error in forecasting a dynamic time series with a deterministic component. We show that when the data are strongly serially correlated, forecasts based on a model which detrends the data before estimating the dynamic parameters are much less precise than those based on an...

We consider issues related to the order of an autoregression selected using information criteria. We study the sensitivity of the estimated order to i) whether the effective number of observations is held fixed when estimating models of different order, ii) whether the estimate of the variance...

This paper considers the implications of omitted mean shifts for estimation and inference in VARs. It is shown that the least squares estimates are inconsistent, and the F test for Granger causality diverges. While model selection rules have the tendency to incorrectly select a lag length that...

Common factors play an important role in many disciplines of social science. In economics, the factors are the common shocks that underlie the co-movements of the large number of economic time series. The question of interest is whether some observable economic variables are in fact the...

Datasets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data. While more data are better than less, diminishing returns suggest that we may not need terabytes of data to estimate a parameter or test a hypothesis. But which...