This paper surveys recent developments in the evaluation of point forecasts. Taking West’s (2006) survey as a starting point, we briefly cover the state of the literature as of the time of West’s writing. We then focus on recent developments, including advancements in the evaluation of...

Recent monetary policy experience suggests a simple diagnostic for models of monetary non-neutrality. Suppose the central bank pegs the nominal interest rate below steady state for a reasonably short period of time. Familiar intuition suggests that this should be modestly inflationary, and a...

This paper develops a new financial stress measure (Cleveland Financial Stress Index, CFSI) that considers the supervisory objective of identifying risks to the stability of the financial system. The index provides a continuous signal of financial stress and broad coverage of the areas that...

A demonstration of time series techniques used to forecast quarterly money supply levels. The results indicate that a bivariate model, including an interest rate and M1 predicts M1 better than the univariate model using M1 only, and as well as a 5-variable model which adds prices, output, and...

This paper proposes an extension of Granger causality when more than two variables are used in a multivariate time series model, and it is necessary to consider more than one-period-ahead forecasts.

In this paper, we present a forecasting technique that uses contemporaneous correlations for forecasting in a time series model when only a subset of the variables are available for the current period. This method potentially provides more accurate forecasts than the standard time series...

The purposes of this study are two: 1) to compare the forecasting abilities of the three methods: univariate autoregressive integrated moving average (ARIMA), multivariate autoregressive integrated moving average (MARIMA), and vector autoregression (both unconstrained — VAR — and Bayesian...

An examination of whether one should seasonally adjust data before developing multivariate time series models to provide forecasts.

A presentation of multivariate time series forecasting in which the data consist of a mixture of quarterly and monthly series. In particular, a monthly series of M1 is used to forecast quarterly GNP.

The "ideal" band-pass filter can be used to isolate the component of a time series that lies within a particular band of frequencies, but applying this filter requires a data set of infinite length. In practice, some sort of approximation is needed. Using projections, the authors derive...