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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 27 (2011)
Issue (Month): 4 (October)
Contact details of provider:
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 BÃ¥rdsen & Helmut LÃ¼tkepohl, 2009. "Forecasting Levels of log Variables in Vector Autoregressions," Working Paper Series, Department of Economics, Norwegian University of Science and Technology 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
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Helmut Lütkepohl & Fang Xu, 2012.
"The role of the log transformation in forecasting economic variables,"
Springer, vol. 42(3), pages 619-638, June.
- Helmut Luetkepohl & Fang Xu, 2009. "The Role of the Log Transformation in Forecasting Economic Variables," CESifo Working Paper Series 2591, CESifo Group Munich.
- Ariï¿½o, 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.
- Arino, Miguel A. & Franses, Philip Hans, 2000. "Forecasting the levels of vector autoregressive log-transformed time series," International Journal of Forecasting, Elsevier, Elsevier, vol. 16(1), pages 111-116.
- Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198774501, October.
- Tommaso, Proietti & Helmut, Luetkepohl, 2011.
"Does the Box-Cox transformation help in forecasting macroeconomic time series?,"
32294, University Library of Munich, Germany.
- Proietti, Tommaso & LÃ¼tkepohl, Helmut, 2013. "Does the Boxâ€“Cox transformation help in forecasting macroeconomic time series?," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(1), pages 88-99.
- Tommaso Proietti & Helmut Luetkepohl, 2011. "Does the Box-Cox Transformation Help in Forecasting Macroeconomic Time Series?," Economics Working Papers ECO2011/29, European University Institute.
- LÃ¼tkepohl, Helmut & Proietti, Tommaso, 2011. "Does the Box-Cox transformation help in forecasting macroeconomic time series?," Working Papers 1 OMEWP, University of Sydney Business School, Discipline of Business Analytics.
- Lorenzo Pascual & Esther Ruiz & Diego Fresoli, 2011. "Bootstrap forecast of multivariate VAR models without using the backward representation," Statistics and Econometrics Working Papers, Universidad Carlos III, Departamento de EstadÃstica y EconometrÃa ws113426, Universidad Carlos III, Departamento de EstadÃstica y EconometrÃa.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.