IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Vector-Autoregression Approach to Forecast Italian Imports

  • Carmine Pappalardo

    ()

    (ISAE - Institute for Studies and Economic Analyses)

  • Gianfranco Piras

    ()

    (University of Pescara, Faculty of Economics)

Imports represent a relevant component of total economic resources. For the Italian case, they mainly consist of raw materials and intermediate goods. In this paper, we evaluate several econometric models performing shorthorizon forecasts of Italian imports of goods. Year-to-year growth rate of the monthly seasonally unadjusted series is the variable to predict. VAR forecasting ability has been compared to that of a linear univariate benchmark (ARIMA) model. Main forecast diagnostics have been presented. Finally, we perform two types of forecast encompassing tests (Diebold-Mariano, 1995; Fair-Shiller, 1990) for which we present main results.

If 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.

File URL: http://lipari.istat.it/digibib/Working_Papers/PappalardoPiras_wpn422004.pdf
Download Restriction: no

Paper provided by ISTAT - Italian National Institute of Statistics - (Rome, ITALY) in its series ISAE Working Papers with number 42.

as
in new window

Length: 32 pages
Date of creation: Feb 2004
Date of revision:
Handle: RePEc:isa:wpaper:42
Contact details of provider: Postal: Via Cesare Balbo 16, Roma
Phone: +390646732606
Web page: http://www.istat.it/en/
Email:


More information through EDIRC

References listed on IDEAS
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.:

as in new window
  1. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  2. Lyhagen, Johan & Löf, Mårten, 2000. "On seasonal error correction when the processes include different numbers of unit roots," SSE/EFI Working Paper Series in Economics and Finance 0418, Stockholm School of Economics, revised 15 Mar 2001.
  3. Hylleberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal, Integration And Cointegration," Papers 6-88-2, Pennsylvania State - Department of Economics.
  4. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
  5. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  6. Krolzig, Hans-Martin & Hendry, David F., 2001. "Computer automation of general-to-specific model selection procedures," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 831-866, June.
  7. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-89, June.
  8. Osborn, Denise R. & Heravi, Saeed & Birchenhall, C. R., 1999. "Seasonal unit roots and forecasts of two-digit European industrial production," International Journal of Forecasting, Elsevier, vol. 15(1), pages 27-47, February.
  9. J. Joseph Beaulieu & Jeffrey A. Miron, 1992. "Seasonal Unit Roots in Aggregate U.S. Data," NBER Technical Working Papers 0126, National Bureau of Economic Research, Inc.
  10. Giancarlo Bruno & Claudio Lupi, 2004. "Forecasting industrial production and the early detection of turning points," Empirical Economics, Springer, vol. 29(3), pages 647-671, 09.
  11. Agustín Maravall, 1996. "Unobserved Components in Economic Time Series," Banco de Espa�a Working Papers 9609, Banco de Espa�a.
  12. Clements, Michael P. & Hendry, David F., 1997. "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, Elsevier, vol. 13(3), pages 341-355, September.
  13. Hans-Martin Krolzig, 2001. "General--to--Specific Reductions of Vector Autoregressive Processes," Computing in Economics and Finance 2001 164, Society for Computational Economics.
  14. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:isa:wpaper:42. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Stefania Rossetti)

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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.