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Inflation forecasting using dynamic factor analysis. SAS 4GL programming approach

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  • Adam Jêdrzejczyk

    (Warsaw School of Economics)

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    Abstract

    The purpose of this article is to introduce an original macro code written in SAS 4GL. This macro is used to automate the process of forecasting with dynamic factor analysis. Automation of the process helps to save significant amounts of time and effort for the researcher. It also enables to compare different model specifications directly and, hence, to make conclusions that would be imperceptible without such automation, which is shown on the empirical study example.

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    File URL: http://kolegia.sgh.waw.pl/pl/KAE/struktura/IE/struktura/ZES/Documents/Working_Papers/aewp04-12.pdf
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    Bibliographic Info

    Paper provided by Department of Applied Econometrics, Warsaw School of Economics in its series Working Papers with number 63.

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    Length: 29
    Date of creation: 16 Sep 2012
    Date of revision:
    Handle: RePEc:wse:wpaper:63

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    Keywords: statistical programming; forecasting; factor models; inflation;

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    1. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
    2. Eickmeier, Sandra, 2004. "Business Cycle Transmission from the US to Germany: a Structural Factor Approach," Discussion Paper Series 1: Economic Studies 2004,12, Deutsche Bundesbank, Research Centre.
    3. Geweke, John F. & Singleton, Kenneth J., 1981. "Latent variable models for time series : A frequency domain approach with an application to the permanent income hypothesis," Journal of Econometrics, Elsevier, vol. 17(3), pages 287-304, December.
    4. Del Negro, Marco & Otrok, Christopher, 2007. "99 Luftballons: Monetary policy and the house price boom across U.S. states," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 1962-1985, October.
    5. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 27-42, March.
    6. Altissimo, Filippo & Bassanetti, Antonio & Cristadoro, Riccardo & Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia & Veronese, Giovanni, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers 3108, C.E.P.R. Discussion Papers.
    7. Angelini, Elena & Henry, Jérôme & Mestre, Ricardo, 2001. "Diffusion index-based inflation forecasts for the euro area," Working Paper Series 0061, European Central Bank.
    8. Riccardo Cristadoro & Mario Forni & Lucrezia Reichlin & Lucrezia Reichlin & Giovanni Veronese, 2005. "A core inflation indicator for the Euro area," ULB Institutional Repository 2013/10131, ULB -- Universite Libre de Bruxelles.
    9. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    10. George Kapetanios & Vincent Labhard & Simon Price, 2007. "Forecast combination and the Bank of England’s suite of statistical forecasting models," Bank of England working papers 323, Bank of England.
    11. Vansteenkiste, Isabel, 2009. "How important are common factors in driving non-fuel commodity prices? A dynamic factor analysis," Working Paper Series 1072, European Central Bank.
    12. Forni, Mario & Lippi, Marco, 1997. "Aggregation and the Microfoundations of Dynamic Macroeconomics," OUP Catalogue, Oxford University Press, number 9780198288008.
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