Forecasting Levels of log Variables in Vector Autoregressions
Sometimes forecasts of the original variable are of interest although 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 naive forecast of the original variable by simply applying the exponential transformation is not optimal theoretically. A simple expression for the optimal forecast under normality assumptions is derived. Despite its theoretical advantages the optimal forecast is shown to be inferior to the naive 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.
|Date of creation:||2009|
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- Ari�o, M.A. & Franses, Ph.H.B.F., 1996.
"Forecasting the Levels of Vector Autoregressive Log-Transformed Time Series,"
Econometric Institute Research Papers
EI 9669-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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- Helmut Luetkepohl & Fang Xu, 2009.
"The Role of the Log Transformation in Forecasting Economic Variables,"
CESifo Working Paper Series
2591, CESifo Group Munich.
- Helmut Lütkepohl & Fang Xu, 2012. "The role of the log transformation in forecasting economic variables," Empirical Economics, Springer, vol. 42(3), pages 619-638, June.
- Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, March.
- repec:hal:journl:hal-00287137 is not listed on IDEAS
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