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Time Series Factor Analysis with an Application to Measuring Money

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  • Gilbert, Paul D.
  • Meijer, Erik

    (Groningen University)

Abstract

Time series factor analysis (TSFA) and its associated statistical theory is developed. Unlike dynamic factor analysis (DFA), TSFA obviates the need for explicitly modeling the process dynamics of the underlying phenomena. It also differs from standard factor analysis (FA) in important respects: the factor model has a nontrivial mean structure, the observations are allowed to be dependent over time, and the data does not need to be covariance stationary as long as differenced data satisfies a weak boundedness condition. The effects on the estimation of parameters and prediction of the factors is discussed. The statistical properties of the factor score predictor are studied in a simulation study, both over repeated samples and within a given sample. Some apparent anomalies are found in simulation experiments and explained analytically.

Suggested Citation

  • Gilbert, Paul D. & Meijer, Erik, 2005. "Time Series Factor Analysis with an Application to Measuring Money," Research Report 05F10, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    • Handle: RePEc:gro:rugsom:05f10
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    File URL: http://irs.ub.rug.nl/ppn/289322812
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    References listed on IDEAS

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    3. Mr. Nicolas Arregui & Mr. Selim A Elekdag & Mr. Gaston Gelos & Romain Lafarguette & Dulani Seneviratne, 2018. "Can Countries Manage Their Financial Conditions Amid Globalization?," IMF Working Papers 2018/015, International Monetary Fund.
    4. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Staff Working Papers 07-8, Bank of Canada.
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    8. Klomp, Jeroen & de Haan, Jakob, 2009. "Political institutions and economic volatility," European Journal of Political Economy, Elsevier, vol. 25(3), pages 311-326, September.

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