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.

Financial markets witness high levels of activity at certain times, but remain calm at others. This makes the flow of physical time discontinuous. Therefore using physical time scales for studying financial time series, runs the risk of missing important activities. An alternative approach is...

The current economic crisis requires fast information to predict economic behavior early, which is difficult at times of structural changes. This paper suggests an innovative new method of using data on internet activity for that purpose. It demonstrates strong correlations between keyword...

It is intuitively plausible that the demand for cinema services may be partly driven by addiction or habit. Yet there is almost no empirical literature which tests for whether cinema demand is addictive. We estimate addiction models for cinema demand using Korean time series data from 1963 to...

We estimate a multisector sticky-price model for the U.S. economy in which the degree of price stickiness is allowed to vary across sectors. For this purpose, we use a specification that allows us to extract information about the underlying cross-sectional distribution from aggregate data....

This paper analyses the pure time-series properties of doctors’ fees in Ireland to assess whether a structural change in the series is observed at the time of the change in reimbursement in 1989. Such a break would be consistent with doctors responding to the reimbursement change in a manner...