The U.S. economy has experienced a reduction in volatility since the mid-1980s. In this paper we investigate the changes in the response of the economy to an oil price shock and the role of the systematic monetary policy response in accounting for changes in the response of output, prices, inventories, sales, and the overall decline in volatility. Our results suggest a smaller and more short-lived response of most macro variables during the Volcker-Greenspan period. It also appears that whereas the systematic monetary policy response dampened fluctuations in economic activity during the 1970s, it has had virtually no effect after the "Great Moderation."

This paper proposes a theoretical explanation to the common empirical results in which different tests for cointegration give different answers. Using local to unity parametrization I compute the analytical power of some tests for the null of no cointegration: The ADF test on the residuals of the cointegration regression, Johansen's maximum eigenvalue test, the t-test on the Error Correction term and Boswijk (1994) Wald test. The tests are shown to be functions of Brownian Motions and Ornstein-Uhlenbeck processes and to depend on a single nuisance parameter, which is, in turn determined by the correlation at frequency zero of the independent variables with the errors of the cointegration regression. Monte Carlo experiments show that the tests can have significantly different performances for different values of the nuisance parameter. An application to the money demand equation is presented.JEL classification: C32

SUMMARYExisting methods for constructing confidence bands for multivariate impulse response functions may have poor coverage at long lead times when variables are highly persistent. The goal of this paper is to propose a simple method that is not pointwise and that is robust to the presence of highly persistent processes. We use approximations based on local-to-unity asymptotic theory, and allow the horizon to be a fixed fraction of the sample size. We show that our method has better coverage properties at long horizons than existing methods, and may provide different economic conclusions in empirical applications. We also propose a modification of this method which has good coverage properties at both short and long horizons.

Point estimates suggest mean reversion in real exchange rates; however, it still remains uncomfortable that models without any mean reversion are often compatible with data from the floating period. Studies with data over longer periods find mean reversion, but at the cost of mixing in data from earlier exchange rate arrangements. Pooling the floating period data potentially mixes country pairs with and without mean reversion. We examine tests for mean reversion for individual country pairs where greater power against close alternatives is gained through modeling other economic variables with the real exchange rate. By increasing the power of the tests we find strong evidence of mean reversion.

Theory often specifies a particular cointegrating vector among integrated variables, and testing for a unit root in the known cointegrating vector is often required. Although it is common to simply use a univariate test for a unit root for this test, it is known that this does not take into account all available information. We show here that in such testing situations, a family of tests with optimality properties exists. We use this to characterize the extent of the loss in power from using popular methods, as well as to derive a test that works well in practice. We also characterize the extent of the losses of not imposing the cointegrating vector in the testing procedure. We apply various tests to the hypothesis positing that price forecasts from the Livingston data survey are cointegrated with prices, and find that although most tests fail to reject the presence of a unit root in forecast errors, the tests presented here strongly reject this (implausible) hypothesis.