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Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks

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  • Claudio Morana

Abstract

In the paper a general framework for large scale modeling of macroeconomic and financial time series is introduced. The proposed approach is characterized by simplicity of implementation, performing well independently of persistence and heteroskedasticity properties, accounting for common deterministic and stochastic factors. Monte Carlo results strongly support the proposed methodology, validating its use also for relatively small cross-sectional and temporal samples.

Suggested Citation

  • Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
  • Handle: RePEc:mib:wpaper:273
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    File URL: http://dems.unimib.it/repec/pdf/mibwpaper273.pdf
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    References listed on IDEAS

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    Cited by:

    1. Morana, Claudio, 2014. "Insights on the global macro-finance interface: Structural sources of risk factor fluctuations and the cross-section of expected stock returns," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 64-79.
    2. Fabio C. Bagliano & Claudio Morana, 2017. "It ain’t over till it’s over: A global perspective on the Great Moderation-Great Recession interconnection," Applied Economics, Taylor & Francis Journals, vol. 49(49), pages 4946-4969, October.
    3. Claudio, Morana, 2015. "The US$/€ exchange rate: Structural modeling and forecasting during the recent financial crises," Working Papers 321, University of Milano-Bicocca, Department of Economics, revised 28 Dec 2015.

    More about this item

    Keywords

    long and short memory; structural breaks; common factors; principal components analysis; fractionally integrated heteroskedastic factor vector autoregressive model;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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