Forecasting levels of log variables in vector autoregressions

Gunnar Bårdsen and Helmut Lütkepohl

International Journal of Forecasting, 2011, vol. 27, issue 4, 1108-1115

Abstract: Sometimes 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.

Keywords: Vector; autoregressive; model; Cointegration; Forecast; root; mean; square; error (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207011000112
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Forecasting Levels of log Variables in Vector Autoregressions (2009)
Working Paper: Forecasting Levels of log Variables in Vector Autoregressions (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:27:y:2011:i:4:p:1108-1115

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().