Forecasting GNP using monthly M1

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.Time-series analysis ; Forecasting ; Gross national product

Extension of Granger causality in multivariate time series models

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.Time-series analysis

Intervention, exchange-rate volatility, and the stable paretian distribution

A look at whether the United States' decision to cease intervention after March 1981 had a perceptible influence on the day-to-day behavior of exchange rates, using the stable paretian distribution.Foreign exchange rates ; Foreign exchange - Law and legislation

Stability in a model of staggered-reserve accounting

An investigation of the nature of the dynamic process implied by staggered-reserve accounting, using a simple reduced-form model of the money-supply process.Bank reserves ; Money supply

Velocity: a multivariate time-series approach

The Federal Reserve announces targets for the monetary aggregates that are implicitly conditioned on an assumption about future velocity for each of the monetary aggregates. In this paper we present explicit models of velocity for constructing rigorous tests to determine whether the behavior of velocity has changed from what was expected when the targets were chosen. We use time-series methods to develop alternative forecasts of velocity. Multivariate time-series models of velocity that include information about past interest rates produce significantly better out-of-sample forecasts than do univariate methods. Using this multivariate time-series framework, we analyze the Federal Reserve's decisions to change, miss, and switch targets from 1980:IQ to l984:IIQ. For this period, we find that when the Federal Reserve deviated from its announced target, velocity deviated significantly from its predicted value.Money supply ; Time-series analysis

Estimating multivariate ARIMA models: when is close not good enough?

The purpose of this study is to examine the forecasting abilities of the same multivariate autoregressive model estimated using two methods. The first method is the "exact method" used by the SCA System from Scientific Computing Associates. The second method is an approximation method as implemented in the MTS system by Automatic Forecasting Systems, Inc. ; The two methods were used to estimate a five-series multivariate autoregressive model for the Quenouille series on hog numbers, hog prices, corn prices, corn supply, and farm wage rates. The 82 observations were arbitrarily divided into two periods: the first 60 observations were used to estimate the models; then forecasts for one through eight years ahead were calculated for each possible point in the remaining 22 observations. The root mean square error (RMSE) using the SCA-estimated parameters was smaller than the RMSE using the MTS-estimated parameters for 38 of the 40 possible values (five variables by eight forecast horizons) and tied for one point. The average increase in the RMSE when using the MTS parameters was approximately 9 percent. Using the SCA parameters for forecasting provided smaller mean absolute error (MAE) for 35 of the 40 values, with the average increase from using the MTS parameters being approximately 5.6 percent. Using the SCA parameters provided smaller mean errors (ME) for 39 of the 40 values, with the average increase from using the MTS parameters being approximately .023. Thus, the SCA estimation method is shown to provide better forecasts than the MTS method for this one example.Forecasting ; Time-series analysis

Univariate and multivariate ARIMA versus vector autoregression forecasting

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 — BVAR) and 2) to study the idea that one advantage of vector autoregressions is that the models can easily and inexpensively be reestimated after each additional data point. All of these methods have been shown to provide forecasts that are more accurate than many econometric methods, which require more resources to implement. ; These methods were applied to seven economic variables: real GNP, annual inflation rates, unemployment rate, the money supply (Ml), gross private domestic investment, the rate on four- to six-month commercial paper, and the change in business inventories. The major results of this study are: 1) on average, the method that performs best in terms of the root mean square error (RMSE) is the multivariate ARIMA model; 2) the univariate ARIMA and BVAR methods perform approximately the same on average; 3) reestimating the VAR model after each data point increases the accuracy of this method; 4) reestimating the BVAR model after each data point becomes available decreases the accuracy of this method; and 5) the VAR method using reestimation is approximately as accurate as the BVAR method.Economic forecasting ; Time-series analysis

An investigation of the preparation of the science teachers in the public schools in New England.

Thesis (M.A.)--Boston Universit

Comparison of univariate ARIMA, multivariate ARIMA and vector autoregression forecasting

A comparison of the forecasting abilities of univariate ARIMA, multivariate ARIMA, and VAR, and examination of whether series should be differenced before estimating models for forecasting purposes.Forecasting ; Time-series analysis