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...

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...

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...

In this paper, we propose a new family of multivariate loss functions that can be used to test the rationality of vector forecasts without assuming independence across individual variables. When only one variable is of interest, the loss function reduces to the flexible asymmetric family...

Is there a common model inherent in macroeconomic data? Macroeconomic theory suggests that market economies of various nations should share many similar dynamic patterns; as a result, individual-country empirical models, for a wide variety of countries often include the same variables. Yet,...

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.