摘要:

AbstractThis paper provides an introduction to vector auto regression models, explaining their origins and their use for modeling and forecasting. The recent developments of structural modeling and the treatment of non‐stationary variables are also considere

ISSN:0277-6693    

DOI:10.1002/for.3980140302

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractA Bayesian vector autoregressive (BVAR) model is developed for the Connecticut economy to forecast the unemployment rate, nonagricultural employment, real personal income, and housing permits authorized. The model includes both national and state variables. The Bayesian prior is selected on the basis of the accuracy of the out‐of‐sample forecasts. We find that a loose prior generally produces more accurate forecasts. The out‐of‐sample accuracy of the BVAR forecasts is also compared with that of forecasts from an unrestricted VAR model and of benchmark forecasts generated from univariate ARIMA models. The BVAR model generally produces the most accurate short‐ and long‐term out‐of‐sample forecasts for 1988 through 1992. It also correctly predicts the dire

ISSN:0277-6693    

DOI:10.1002/for.3980140303

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractCategory management—a relatively new function in marketing—involves large‐scale, real‐time forecasting of multiple data series in complex environments. In this paper, we illustrate how Bayesian Vector Auto regression (BVAR) fulfils the category manager's decision‐support requirements by providing accurate forecasts of a category's state variables (prices, volumes and advertising levels), incorporating management interventions (merchandising events such as end‐aisle displays), and revealing competitive dynamics through impulse response analyses. Using 124 weeks of point‐of‐sale scanner data comprising 31 variables for four brands, we compare the out‐of‐sample forecasts from BVAR to forecasts from exponential smoothing, univariate and multivariate Box‐Jenkins transfer function analyses, and multivariate ARMA models. TheilU'sindicate that BVAR forecasts are superior to those from alternate approaches. In large‐scale forecasting applications, BVAR's ease of identification and parsimonious use of degrees of freedom ar

ISSN:0277-6693    

DOI:10.1002/for.3980140304

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractThis paper compares the out‐of‐sample forecasting accuracy of a wide class of structural, BVAR and VAR models for major sterling exchange rates over different forecast horizons. As representative structural models we employ a portfolio balance model and a modified uncovered interest parity model, with the latter producing the more accurate forecasts. Proper attention to the long‐run properties and the short‐run dynamics of structural models can improve on the forecasting performance of the random walk model. The structural model shows substantial improvement in medium‐term forecasting accuracy, whereas the BVAR model is the more accurate in the short term. BVAR and VAR models in levels strongly out predict these models formulated in difference form at all forecast

ISSN:0277-6693    

DOI:10.1002/for.3980140305

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractThis study uses Bayesian vector autoregressive models to examine the usefulness of survey data on households' buying attitudes for homes in predicting sales of homes. We find a negligible deterioration in the accuracy of forecasts of home sales when buying attitudes are dropped from a model that includes the price of homes, the mortgage rate, real personal disposable income, and die unemployment rate. This suggests that buying attitudes do not add much to the information contained in these variables. We also find that forecasts from the model that includes both buying attitudes and the aforementioned variables are similar to those generated from a model that excludes the survey data but contains the other variables. Additionally, the variance decompositions suggest that the gain from including the survey data in the model that already contains other economic variables is small.

ISSN:0277-6693    

DOI:10.1002/for.3980140306

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractA combination of VAR estimation and state space model reduction techniques are examined by Monte Carlo methods in order to find good, simple to use, procedures for determining models which have reasonable prediction properties. The presentation is largely graphical. This helps focus attention on the aspects of the model determination problem which are relatively important for forecasting. One surprising result is that, for prediction purposes, knowledge of the true structure of the model generating the data is not particularly useful unless parameter values are also known. This is because the difficulty in estimating parameters of the true model causes more prediction error than results from a more parsimonious approximate model.

ISSN:0277-6693    

DOI:10.1002/for.3980140307

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractRecent innovations in the Welsh economy have led to the view that the region's economy is likely to exhibit differential responses to financial and external shocks compared to the rest of the UK. This is examined with the aid of a structural VAR forecasting model for Wales.

ISSN:0277-6693    

DOI:10.1002/for.3980140308

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractWhy are forecasts of inflation from VAR models so much worse than their forecasts of real variables? This paper documents that relatively poor performance, and finds that the price equation of a VAR model fitted to US post‐war data is poorly specified. Statistical work by other authors has found that coefficients in such price equations may not be constant. Based on specific monetary actions, two changes in monetary policy regimes are proposed. Accounting for those two shifts yields significantly more accurate forecasts and lessens the evidence of misspecificatio

ISSN:0277-6693    

DOI:10.1002/for.3980140309

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractThe analysis and forecasting of electricity consumption and prices has received considerable attention over the past forty years. In the 1950s and 1960s most of these forecasts and analyses were generated by simultaneous equation econometric models. Beginning in the 1970s, there was a shift in the modeling of economic variables from the structural equations approach with strong identifying restrictions towards a joint time‐series model with very few restrictions. One such model is the vector auto regression (VAR) model. It was soon discovered that the unrestricted VAR models do not forecast well. The Bayesian vector auto regression (BVAR) approach as well the error correction model (ECM) and models based on the theory of co integration have been offered as alternatives to the simple VAR model. This paper argues that the BVAF., ECM, and co integration models are simply VAR models with various restrictions placed on the coefficients. Based on this notion of a restricted VAR model, a four‐step procedure for specifying VAR forecasting models is presented and then applied to monthly data on US electricity consumption and pri

ISSN:0277-6693    

DOI:10.1002/for.3980140310

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY

摘要:

AbstractJohansen's test for co integration is applied to Litterman's original six‐variable Bayesian vector auto regression (BVAR) model to obtain vector error correction mechanism (VECM) and Bayesian error correction (BECM) versions of the model. The Brock, Dechert, and Scheinkman (BDS) test for independence from the non‐linear dynamics literature is then applied to the error structures of each estimated equation of the BECM and VECM models, plus two BVAR versions of the model. The results show that none of the models produce independent and identically distributed (IID) errors for all six equations. However, the BDS results suggest the elimination of the Bayesian prior from the BECM model, given that the univariate VECM errors are IID in five equations, compared to only two or three equations under the three Bayesian restricted models. These results combined with previous evidence regarding the superior forecasting performance of BECM over ECM models suggest future experimentation with less restrictive BVAR priors, BECM models corrected for heteroscedasticity, or hybrid specifications based on the nonlinear dynamics literat

ISSN:0277-6693    

DOI:10.1002/for.3980140311

出版商:John Wiley&Sons, Ltd.    

年代:1995

数据来源: WILEY