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Analyzing and Forecasting Movements of the Philippine Economy using the Dynamic Factor Models (DFM)

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  • Mapa, Dennis S.
  • Simbulan, Maria Christina

Abstract

The country’s small and open economy is vulnerable to both internal and external shocks. Is it therefore important for policy makers to have timely forecasts on the movement of the country’s Gross Domestic Product (GDP), whether it will increase or decrease in the current quarter, to be able to guide them in coming up with appropriate policies to mitigate say, the impact of a shock. The current method used to forecast the movements of the GDP is the composite Leading Economic Indicators System (LEIS) developed by the National Economic Development Authority (NEDA) and the National Statistical Coordination Board (NSCB). The LEIS, using 11 economic indicators, provides one-quarter forecast of the movement of the GDP. This paper presents an alternative, and perhaps better, procedure to the LEIS in nowcasting the movements of the GDP using the Dynamic Factor Model (DFM). The idea behind the DFM is the stylized fact that economic movements evolve in a cycle and are correlated with co-movements in a large number of economic series. The DFM is a commonly used data reduction procedure that assumes economic shocks driving economic activity arise from unobserved components or factors. The DFM aims to parsimoniously summarize information from a large number of economic series to a small number of unobserved factors. The DFM assumes that co-movements of economic series can be captured using these unobserved common factors. This paper used 31 monthly economic indicators in capturing a common factor to nowcast movements of GDP via the DFM. The results show that the common factor produced by the DFM performed better in capturing the movements of the GDP when compared with the LEIS. The DFM is a promising and useful methodology in extracting indicators of the country’s economic activity.

Suggested Citation

  • Mapa, Dennis S. & Simbulan, Maria Christina, 2014. "Analyzing and Forecasting Movements of the Philippine Economy using the Dynamic Factor Models (DFM)," MPRA Paper 54478, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:54478
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    References listed on IDEAS

    as
    1. Hwee Kwan Chow & Keen Meng Choy, 2009. "Analyzing and forecasting business cycles in a small open economy: A dynamic factor model for Singapore," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2009(1), pages 19-41.
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    3. Christian Schulz, 2007. "Forecasting economic growth for Estonia : application of common factor methodologies," Bank of Estonia Working Papers 2007-09, Bank of Estonia, revised 04 Sep 2007.
    4. Shin-ichi Fukuda & Takashi Onodera, 2001. "A New Composite Index of Coincident Economic Indicators in Japan: How can we improve the forecast performance? ," CIRJE F-Series CIRJE-F-101, CIRJE, Faculty of Economics, University of Tokyo.
    5. Martin, Vance L, 1990. "Derivation of a Leading Index for the United States Using Kalman Filters," The Review of Economics and Statistics, MIT Press, vol. 72(4), pages 657-663, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Dynamic Factor Model; Leading Economic Indicators; Common Factor;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination

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