Department of Economics and Business Economics

Estimation of a Dynamic Multi-Level Factor Model with Possible Long-Range Dependence

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Estimation of a Dynamic Multi-Level Factor Model with Possible Long-Range Dependence. / Ergemen, Yunus Emre; Rodríguez-Caballero, Carlos Vladimir.

In: International Journal of Forecasting, 01.2022.

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@article{06fcc37960064ad0822d9ddcb6ed4004,
title = "Estimation of a Dynamic Multi-Level Factor Model with Possible Long-Range Dependence",
abstract = "A dynamic multi-level factor model with possible stochastic time trends is proposed. In the model, long-range dependence and short memory dynamics are allowed in global and local common factors as well as model innovations. Estimation of global and local common factors is performed on the prewhitened series, for which the prewhitening parameter is estimated semiparametrically from the cross-sectional and local average of the observable series. Employing canonical correlation analysis and a sequential least-squares algorithm on the prewhitened series, the resulting multi-level factor estimates have centered asymptotic normal distributions under certain rate conditions depending on the bandwidth and cross-section size. Asymptotic results for common components are also established. The selection of the number of global and local factors is discussed. The methodology is shown to lead to good small-sample performance via Monte Carlo simulations. The method is then applied to the Nord Pool electricity market for the analysis of price comovements among different regions within the power grid. The global factor is identified to be the system price, and fractional cointegration relationships are found between local prices and the system price, motivating a long-run equilibrium relationship. Two forecasting exercises are then discussed.",
keywords = "Multi-level factorsLong-range dependenceFractional cointegrationNord Pool power marketElectricity price forecasting",
author = "Ergemen, {Yunus Emre} and Rodr{\'i}guez-Caballero, {Carlos Vladimir}",
year = "2022",
month = jan,
doi = "10.1016/j.ijforecast.2021.12.004",
language = "English",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Estimation of a Dynamic Multi-Level Factor Model with Possible Long-Range Dependence

AU - Ergemen, Yunus Emre

AU - Rodríguez-Caballero, Carlos Vladimir

PY - 2022/1

Y1 - 2022/1

N2 - A dynamic multi-level factor model with possible stochastic time trends is proposed. In the model, long-range dependence and short memory dynamics are allowed in global and local common factors as well as model innovations. Estimation of global and local common factors is performed on the prewhitened series, for which the prewhitening parameter is estimated semiparametrically from the cross-sectional and local average of the observable series. Employing canonical correlation analysis and a sequential least-squares algorithm on the prewhitened series, the resulting multi-level factor estimates have centered asymptotic normal distributions under certain rate conditions depending on the bandwidth and cross-section size. Asymptotic results for common components are also established. The selection of the number of global and local factors is discussed. The methodology is shown to lead to good small-sample performance via Monte Carlo simulations. The method is then applied to the Nord Pool electricity market for the analysis of price comovements among different regions within the power grid. The global factor is identified to be the system price, and fractional cointegration relationships are found between local prices and the system price, motivating a long-run equilibrium relationship. Two forecasting exercises are then discussed.

AB - A dynamic multi-level factor model with possible stochastic time trends is proposed. In the model, long-range dependence and short memory dynamics are allowed in global and local common factors as well as model innovations. Estimation of global and local common factors is performed on the prewhitened series, for which the prewhitening parameter is estimated semiparametrically from the cross-sectional and local average of the observable series. Employing canonical correlation analysis and a sequential least-squares algorithm on the prewhitened series, the resulting multi-level factor estimates have centered asymptotic normal distributions under certain rate conditions depending on the bandwidth and cross-section size. Asymptotic results for common components are also established. The selection of the number of global and local factors is discussed. The methodology is shown to lead to good small-sample performance via Monte Carlo simulations. The method is then applied to the Nord Pool electricity market for the analysis of price comovements among different regions within the power grid. The global factor is identified to be the system price, and fractional cointegration relationships are found between local prices and the system price, motivating a long-run equilibrium relationship. Two forecasting exercises are then discussed.

KW - Multi-level factorsLong-range dependenceFractional cointegrationNord Pool power marketElectricity price forecasting

U2 - 10.1016/j.ijforecast.2021.12.004

DO - 10.1016/j.ijforecast.2021.12.004

M3 - Journal article

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

ER -