Testing for Common Deterministic Trend Slopes

We propose tests for hypotheses on the parameters for deterministic trends. The model framework assumes a multivariate structure for trend-stationary time series variables. We derive the asymptotic theory and provide some relevant critical values. Monte Carlo simulations suggest which tests are more useful in practice than others. We apply our tests to examine if monthly temperatures in The Netherlands, measured from 1706 onwards, have a trend and if these trends are the same across months. We find that the January and March temperatures have the same upward trend, that the September temperature has decreased and that the temperatures in the other months do not have a trend. Hence, only winters in The Netherlands seem to get warmer.

Asymmetric Time Aggregation and its Potential Benefits for Forecasting Annual Data

For many economic time-series variables that are observed regularly and frequently, for example weekly, the underlying activity is not distributed uniformly across the year. For the aim of predicting annual data, one may consider temporal aggregation into larger subannual units based on an activity time scale instead of calendar time. Such a scheme may strike a balance between annual modelling (which processes little information) and modelling at the finest available frequency (which may lead to an excessive parameter dimension), and it may also outperform modelling calendar time units (with some months or quarters containing more information than others). We suggest an algorithm that performs an approximate inversion of the inherent seasonal time deformation. We illustrate the procedure using weekly data for temporary staffing services.Seasonality, time deformation, prediction, time series

"Does the FOMC Have Expertise, and Can It Forecast?"

The primary purpose of the paper is to answer the following two questions regarding the performance of the influential Federal Open Market Committee (FOMC) of the Federal Reserve System, in comparison with the forecasts contained in the "Greenbooks" of the professional staff of the Board of Governors: Does the FOMC have expertise, and can it forecast better than the staff? The FOMC forecasts that are analyzed in practice are nonreplicable forecasts. In order to evaluate such forecasts, this paper develops a model to generate replicable FOMC forecasts, and compares the staff forecasts, non-replicable FOMC forecasts, and replicable FOMC forecasts, considers optimal forecasts and efficient estimation methods, and presents a direct test of FOMC expertise on nonreplicable FOMC forecasts. The empirical analysis of Romer and Romer (2008) is reexamined to evaluate whether their criticisms of the FOMC's forecasting performance should be accepted unreservedly, or might be open to alternative interpretations.