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A Dynamic Multi-Level Factor Model with Long-Range Dependence

Author

Listed:
  • Yunus Emre Ergemen

    (Aarhus University and CREATES)

  • Carlos Vladimir Rodríguez-Caballero

    (Aarhus University and CREATES)

Abstract

A dynamic multi-level factor model with stationary or nonstationary global and regional factors is proposed. In the model, persistence in global and regional common factors as well as innovations allows for the study of fractional cointegrating relationships. Estimation of global and regional common factors is performed in two steps employing canonical correlation analysis and a sequential least-squares algorithm. Selection of the number of global and regional factors is discussed. The small sample properties of our methodology are investigated by some Monte Carlo simulations. The method is then applied to the Nord Pool power market for the analysis of price comovements among different regions within the power grid. We find that the global factor can be interpreted as the system price of the power grid as well as a fractional cointegration relationship between prices and the global factor.

Suggested Citation

  • Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2016-23
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    References listed on IDEAS

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    Cited by:

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    2. Carlos Vladimir Rodríguez-Caballero, 2016. "Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure," CREATES Research Papers 2016-31, Department of Economics and Business Economics, Aarhus University.

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    More about this item

    Keywords

    Multi-level factor; long memory; fractional cointegration; electricity prices;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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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