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

  • Adam Jêdrzejczyk

    (Warsaw School of Economics)

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    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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    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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    1. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
    2. Mario Forno & Marco Lippi & Lucrezia Reichlin & Filippo Altissimo & Antonio Bassanetti, 2003. "Eurocoin: A Real Time Coincident Indicator Of The Euro Area Business Cycle," Computing in Economics and Finance 2003 242, Society for Computational Economics.
    3. Elena Angelini & Jérôme Henry & Ricardo Mestre, 2001. "Diffusion index-based inflation forecasts for the euro area," BIS Papers chapters, in: Bank for International Settlements (ed.), Empirical studies of structural changes and inflation, volume 3, pages 109-138 Bank for International Settlements.
    4. Riccardo Cristadoro & Mario Forni & Lucrezia Reichlin & Giovanni Veronese, 2005. "A core inflation indicator for the Euro area," ULB Institutional Repository 2013/10131, ULB -- Universite Libre de Bruxelles.
    5. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 27-42, March.
    6. 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.
    7. 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.
    8. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    9. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
    10. 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.
    11. Forni, Mario & Lippi, Marco, 1997. "Aggregation and the Microfoundations of Dynamic Macroeconomics," OUP Catalogue, Oxford University Press, number 9780198288008.
    12. 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.
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