In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional...

There are over 3 billion searches globally on Google every day. This report examines whether Google search queries can be used to predict the present and the near future unemployment rate in Finland. Predicting the present and the near future is of interest, as the official records of the state...

Empirical research over the last decade has uncovered predictive relationships between the slope of the yield curve and subsequent real activity and inflation. Some of these relationships are highly significant, but their theoretical motivations suggest that they may not be stable over time. We...

Quality Function Deployment (QFD) is widely used customer driven process for product development. Thus, Customer Requirements (CRs) play a key role in QFD process. However, the diversification in marketplace makes these CRs more dynamic and changing, giving rise the need to forecast CRs to...

This study examines the problem of forecasting an aggregate of cointegrated disaggregates. It first establishes conditions under which forecasts of an aggregate variable obtained from a disaggregate VECM will be equal to those from an aggregate, univariate time series model, and develops a...

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

In a factor-augmented regression, the forecast of a variable depends on a few factors estimated from a large number of predictors. But how does one determine the appropriate number of factors relevant for such a regression? Existing work has focused on criteria that can consistently estimate the...

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.