Cointegration and Long-Horizon Forecasting
Peter Christoffersen,Peter Christoffersen,Peter Christoffersen,Francis X. Diebold,Francis X. Diebold +4 more
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TLDR
In this article, the forecasting of cointegrated variables is considered and it is shown that at long horizons" nothing is lost by ignoring cointegration when forecasts are evaluated using standard multivariate" forecast accuracy measures.Abstract:
We consider the forecasting of cointegrated variables, and we show that at long horizons" nothing is lost by ignoring cointegration when forecasts are evaluated using standard multivariate" forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. " Our results highlight a potentially important deficiency of standard forecast accuracy" measures they fail to value the maintenance of cointegrating relationships among" variables and we suggest alternatives that explicitly do so.read more
Citations
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Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive?
Yin-Wong Cheung,Yin-Wong Cheung,Menzie D. Chinn,Menzie D. Chinn,Antonio Garcia Pascual,Antonio Garcia Pascual +5 more
TL;DR: In this paper, the authors re-assess exchange rate prediction using a wider set of models that have been proposed in the last decade: interest rate parity, productivity based models, and behavioral equilibrium exchange rate' models.
Book
Elements of Forecasting
TL;DR: The Linear Regression Model, Unit Roots, Stochastic Trends, ARIMA Forecasting Models, and Smoothing; Volatility Measurement, Modeling and Forecasting.
Journal ArticleDOI
Bridge Models to Forecast the Euro Area GDP
TL;DR: In this article, the forecast ability of bridge models (BM) for GDP growth in the euro area is examined, where BM is used to bridge the gap between the information content of timely updated indicators and the delayed (but more complete) NA.
Posted Content
A joint econometric model of macroeconomic and term structure
Journal ArticleDOI
Forecasting methods in energy planning models
TL;DR: A systematic and critical review of forecasting methods used in 483 EPMs, finding that computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data.
References
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Comparing Predictive Accuracy
TL;DR: The authors describes the advantages of these studies and suggests how they can be improved and also provides aids in judging the validity of inferences they draw, such as multiple treatment and comparison groups and multiple pre- or post-intervention observations.
ReportDOI
Comparing Predictive Accuracy
TL;DR: In this article, explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts are proposed and evaluated, and asymptotic and exact finite-sample tests are proposed, evaluated and illustrated.
Journal ArticleDOI
Testing for Common Trends
James H. Stock,Mark W. Watson +1 more
TL;DR: In this article, two tests for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift are developed.
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Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data
TL;DR: This book focuses on the exploration of relationships among integrated data series and the exploitation of these relationships in dynamic econometric modelling, and the asymptotic theory of integrated processes is described.
Journal ArticleDOI
Forecasting and testing in co-integrated systems
Robert F. Engle,Byung Sam Yoo +1 more
TL;DR: In this article, the authors examined the behavior of forecasts made from a co-integrated system as introduced by Granger (1981), Granger and Weiss (1983), and Engle and Granger (1987).