Nonstationary dynamic factor analysis

Peña, Daniel; Poncela, Pilar

doi:10.1016/j.jspi.2004.08.020

Cited by 123 publications

(122 citation statements)

References 35 publications

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“…c i is the constant of the model for the common factors, and its inclusion in the common factors model in Equation (2) can be particularly relevant to calculate long-term forecasts in the non-stationary case (which is the case of electricity prices). Furthermore, in this work, the specific components are assumed to be independent and have no dynamic structure along them (e.g., [11]). It should be noticed that the procedure of estimating the factors is independent of the procedure of estimating the ARIMA models.…”

Section: Dynamic Factor Model (Dfm)mentioning

confidence: 99%

“…To summarize, a key stage when estimating this kind of model is the selection of the number of common factors, r, as well as the model they follow, which implies selecting the orders: p, d, q, P, D, Q. r could be obtained using the test proposed in [11] and could also be selected such that diagnostic checking results (specific factors and errors of the observation equation must be uncorrelated between them, and specific factors without any cross correlation) are reasonable [5]. However, alternative values could satisfy these criteria.…”

Section: Dynamic Factor Model (Dfm)mentioning

confidence: 99%

“…More recently, dimensionality reduction techniques have gained popularity, in particular since the work by [9]. For example, [10,11] extended [6]'s model for the non-stationary case.…”

Section: Introductionmentioning

confidence: 99%

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Electricity Price Forecasting by Averaging Dynamic Factor Models

2016

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Abstract:In the context of the liberalization of electricity markets, forecasting prices is essential. With this aim, research has evolved to model the particularities of electricity prices. In particular, dynamic factor models have been quite successful in the task, both in the short and long run. However, specifying a single model for the unobserved factors is difficult, and it cannot be guaranteed that such a model exists. In this paper, model averaging is employed to overcome this difficulty, with the expectation that electricity prices would be better forecast by a combination of models for the factors than by a single model. Although our procedure is applicable in other markets, it is illustrated with an application to forecasting spot prices of the Iberian Market, MIBEL (The Iberian Electricity Market). Three combinations of forecasts are successful in providing improved results for alternative forecasting horizons.

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Section: Dynamic Factor Model (Dfm)mentioning

confidence: 99%

Section: Dynamic Factor Model (Dfm)mentioning

confidence: 99%

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Electricity Price Forecasting by Averaging Dynamic Factor Models

2016

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“…There is no a specific method for selecting the number of common factors in the presence of conditional heteroskedasticity, but there are some published results focused on the selection ofthe number of common factors in Dynamic Factor Analysis. Two of them are applied here: the graphical procedure by Forni et al (2000) whose details can be encountered in Appendix B as well as the a priori test by Peña and Poncela (2006). Thus, when applied to this data, both methodologies provide the same output about the number of common factors, r = 2.…”

Section: í Modelling Electricity Prices and Their Volatilities In Thmentioning

confidence: 99%

Forecasting electricity prices and their volatilities using Unobserved Components

2011

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Keywords:Conditional heteroskedasticity Dynamic factor analysis Iberian market Long run Non-stationary Short runThe liberalization of electricity markets more than ten years ago in the vast majority of developed countries has introduced the need of modelling and forecasting electricity prices and volatilities, both in the short and long term. Thus, there is a need of providing methodology that is able to deal with the most important features of electricity price series, which are well known for presenting not only structure in conditional mean but also time-varying conditional variances. In this work we propose a new model, which allows to extract conditionally heteroskedastic common factors from the vector of electricity prices. These common factors are jointly estimated as well as their relationship with the original vector of series, and the dynamics affecting both their conditional mean and variance. The estimation of the model is carried out under the state-space formulation. The new model proposed is applied to extract seasonal common dynamic factors as well as common volatility factors for electricity prices and the estimation results are used to forecast electricity prices and their volatilities in the Spanish zone of the Iberian Market. Several simplified/alternative models are also considered as benchmarks to ¡Ilústrate that the proposed approach is superior to all of them in terms of explanatory and predictive power.

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“…First, we allow factors to be nonstationary and the nonstationarity is not necessarily driven by unit roots. The latter was investigated in the context of factor models by, for example, Ahn (1997), and Peña & Poncela (2006). Secondly, our estimation method is new and it identifies the unobserved factors via expanding the white noise space step by step; therefore solving a high-dimensional optimization problem by several lowdimensional sub-problems.…”

Section: Introductionmentioning

confidence: 99%

Modelling multiple time series via common factors

Pan¹,

Yao²

2008

Biometrika

120

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This document is the author's final manuscript accepted version of the journal article, incorporating any revisions agreed during the peer review process. Some differences between this version and the published version may remain. You are advised to consult the publisher's version if you wish to cite from it. We propose a new method for estimating common factors of multiple time series. One distinctive feature of the new approach is that it is applicable to some nonstationary time series. The unobservable (nonstationary) factors are identified via expanding the white noise space step by step; therefore solving a high-dimensional optimization problem by several low-dimensional subproblems. Asymptotic properties of the estimation were investigated. The proposed methodology was illustrated with both simulated and real data sets. Modelling multiple time series via common factors

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Nonstationary dynamic factor analysis

Cited by 123 publications

References 35 publications

Electricity Price Forecasting by Averaging Dynamic Factor Models

Electricity Price Forecasting by Averaging Dynamic Factor Models

Forecasting electricity prices and their volatilities using Unobserved Components

Modelling multiple time series via common factors

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