Forecasting with difference-stationary and trend-stationary models
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Forecasting with difference-stationary and trend-stationary models by Michael P. Clements

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Published by University of Warwick, Department of Economics in Coventry .
Written in English


Book details:

Edition Notes

Statementby Michael P. Clements and David F. Hendry.
SeriesWarwick economic research papers -- no.516, Economic research paper series / University of Warwick, Department of Economics -- no.516, Economic research paper (University of Warwick, Department of Economics) -- no.516.
ContributionsHendry, David F.
ID Numbers
Open LibraryOL18074567M

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  Although difference‐stationary (DS) and trend‐stationary (TS) processes have been subject to considerable analysis, there are no direct comparisons for each being the data‐generation process (DGP). We examine incorrect choice between these models for forecasting for both known and estimated parameters. Three sets of Monte Carlo simulations . Forecasting with difference-stationary and trend-stationary models Article (PDF Available) in Econometrics Journal 4(1) February with Reads How we measure 'reads'. Downloadable! Although difference-stationary (DS) and trend-stationary (TS) processes have been subject to considerable analysis, there are no direct comparisons for each being the data-generation process (DGP). We examine incorrect choice between these models for forecasting for both known and estimated parameters. Three sets of Monte Carlo simulations illustrate the .   The distinction between difference stationary and trend stationary models has been debated. Our simplest form of the random walk model with drift consists of a non-zero mean and a shock, while expanding the confidence interval over forecasting horizons.

A trend-stationary (TS) tionary and difference stationary time series. tical approach to building an effecti ve NN forecasting model. (Here I deliberately left out the qualification that the series can be transformed to a stationary series using first differencing and that the OP is interested in forecasting using ARIMA in particular.) The problem with nonstationary data is that for most of the time series models, the model assumptions are violated when nonstationary data is. In this paper I describe the effect of parameter uncertainty on the way conditional forecast variances grow as the forecast horizon increases. Without parameter uncertainty, forecast variances for the unit root model grow linearly with the forecast horizon while with the trend stationary model they are bounded. Perform financial forecasting, reporting, and operational metrics tracking, analyze financial data, create financial models use to predict future revenues Sales Revenue Sales revenue is the income received by a company from its sales of goods or the provision of services. In accounting, the terms "sales" and "revenue" can be, and often are.

Part of the Palgrave Texts in Econometrics book series (PTEC) Abstract In part, such interest lies in a critique of a procedure that models the trend component of a series as a deterministic function of time, usually as a simple low-order polynomial of . Although difference-stationary (DS) and trend-stationary (TS) processes have been subject to considerable analysis, there are no direct comparisons for each being the data-generation process (DGP). We examine incorrect choice between these models for forecasting for both known and estimated parameters. Three sets of Monte Carlo simulations illustrate the .   Ratios of mean squared errors for trend-stationary to difference-stationary models for h-years-ahead forecasts, –, based on models fitted to – h GNP-R. Macroeconometric models are a very imperfect tool for forecasting this highly complicated and changing process. Ignoring these factors leads to a wide discrepancy between theory and practice. In their second book on economic forecasting, Michael P. Clements and David F. Hendry ask why some practices seem to work empirically despite a lack of.