Forecasting levels of log variables in vector autoregressions
AbstractSometimes forecasts of the original variable are of interest, even though a variable appears in logarithms (logs) in a system of time series. In that case, converting the forecast for the log of the variable to a naïve forecast of the original variable by simply applying the exponential transformation is not theoretically optimal. A simple expression for the optimal forecast under normality assumptions is derived. However, despite its theoretical advantages, the optimal forecast is shown to be inferior to the naïve forecast if specification and estimation uncertainty are taken into account. Hence, in practice, using the exponential of the log forecast is preferable to using the optimal forecast.
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Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 27 (2011)
Issue (Month): 4 (October)
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Web page: http://www.elsevier.com/locate/ijforecast
Vector autoregressive model Cointegration Forecast root mean square error;
Other versions of this item:
- Gunnar Bardsen & Helmut Luetkepohl, 2009. "Forecasting Levels of log Variables in Vector Autoregressions," Economics Working Papers ECO2009/24, European University Institute.
- Gunnar Bårdsen & Helmut Lütkepohl, 2009. "Forecasting Levels of log Variables in Vector Autoregressions," Working Paper Series 10409, Department of Economics, Norwegian University of Science and Technology.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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