Improving wind power forecasts: combination through multivariate dimension reduction techniques

Wind energy and wind power forecast errors have a direct impact on operational decision problems involved in the integration of this form of energy into the electricity system. As the relationship between wind and the generated power is highly nonlinear and time-varying, and given the increasing number of available forecasting techniques, it is possible to use alternative models to obtain more than one prediction for the same hour and forecast horizon. To increase forecast accuracy, it is possible to combine the different predictions to obtain a better one or to dynamically select the best one in each time period. Hybrid alternatives based on combining a few selected forecasts can be considered when the number of models is large. One of the most popular ways to combine forecasts is to estimate the coefficients of each prediction model based on its past forecast errors. As an alternative, we propose using multivariate reduction techniques and Markov chain models to combine forecasts. The combination is thus not directly based on the forecast errors. We show that the proposed combination strategies based on dimension reduction techniques provide competitive forecasting results in terms of the Mean Square ErrorThe second author, Pilar Poncela, acknowledges financial support from the Spanish Government, Ministry of Science, contract grant PID2019-108079GB-C22/AEI/10.13039/50110001103

Measuring uncertainty and assessing its predictive power in the euro area

Expectations and uncertainty play a key role in economic behavior. This paper deals with both, expectations and uncertainty derived from the European Central Bank Survey of Professional Forecasters. Given the strong turbulences that the euro area macroeconomic indicators observe since 2007, the aim of the paper is to check whether there is any room for improvement of the consensus forecast accuracy for GDP growth and inflation when accounting for uncertainty. We propose a new measure of uncertainty, alternative to the ad hoc equal weights commonly used, based on principal components. We test the role of uncertainty in forecasting macroeconomic performance in the euro area between 2005 and 2015. We also check the role of surprises in the considered forecasting sampleMinisterio de Economía y CompetitividadFinancial support from the Spanish Ministry of Economy and Competitiveness, project numbers ECO2015-70331-C2-1-R, ECO2015-66593-P and ECO2014-56676C2-2-P and Universidad de Alcalá is acknowledged

Small versus big-data factor extraction in Dynamic Factor Models: an empirical assessment

In the context of Dynamic Factor Models (DFM), we compare point and interval estimates of the underlying unobserved factors extracted using small and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather than on prediction of a given variable in the system. Second, the comparisons are carried out by implementing the procedures considered to the same data. Third, we are interested not only on point estimates but also on confidence intervals for the factors. Based on a simulated system and the macroeconomic data set popularized by Stock and Watson (2012), we show that, for a given procedure, factor estimates based on different cross-sectional dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the Maximum Likelihood Kalman filter and smoother (KFS) factor estimates are highly correlated with those obtained using hybrid Principal Components (PC) and KFS procedures. The PC estimates are somehow less correlated. Finally, the PC intervals based on asymptotic approximations are unrealistically tiny

Green shoots in the euro area : a real time measure

We show that an extension of the Markov-switching dynamic factor models that accounts for the speci cities of the day to day monitoring of economic developments such as ragged edges, mixed frequencies and data revisions is a good tool to forecast the Euro area recessions in real time. We provide examples that show the nonlinear nature of the relations between data revisions, point forecasts and forecast uncertainty. According to our empirical results, we think that the real time probabilities of recession are an appropriate statistic to capture what the press call green shoot

Extracting non-linear signals from several economic indicators

Incluye bibliografíaWe develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non-linear multivariate specifi cation (one-step approach) with the “shortcut” of using a linear factor model to obtain a coincident indicator which is then used to compute the Markov-switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US dataEn este trabajo analizamos cómo la información proveniente de varios indicadores económicos puede utilizarse en un modelo de factores dinámicos con estructura de cadenas de Markov para identifi car puntos de giro del ciclo económico. Primero comparamos cómo un modelo con una completa especifi cación no lineal (una sola etapa) predice los puntos de giro en comparación con un modelo donde se estima un modelo de factores dinámicos lineal y luego se computan las probabilidades de cambio de régimen usando un modelo estándar univariante al factor (dos etapas). Segundo, analizamos el hecho de incrementar nuestro conjunto de información y de dónde proviene la ganancia de incrementar el número de las variables consideradas en el modelo. Nuestros resultados sugieren que, pese a que estimar el modelo en un solo paso es mejor que estimarlo en varias etapas, la ganancia marginal disminuye cuanto mejor sean los indicadores utilizados y más variables se utilicen en la estimación del signo no lineal. Usando las cuatro series que constituyen el índice coincidente de Stock y Watson, ilustramos este resultado para la economía de EEU

Markov-switching dynamic factor models in real time

Incluye bibliografíaWe extend the Markov-switching dynamic factor model to account for some of the specifi cities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as ragged edges and mixed frequencies. We examine the theoretical benefi ts of this extension and corroborate the results through several Monte Carlo simulations. Finally, we assess its empirical reliability to compute real-time inferences of the US business cycleEn este trabajo extendemos el modelo factorial dinámico con cadenas de Markov para tener en cuenta alguna de las especifi cidades del análisis diario de los indicadores macroeconómicos, tales como el retraso en la publicación de las variables y la mezcla de frecuencias. Analizamos los benefi cios teóricos de estas extensiones y corroboramos los resultados a través de varios experimentos de Montecarlo. Finalmente evaluamos la robustez empírica de los resultados haciendo inferencia en tiempo real sobre el ciclo económico american

Short-term forecasting for empirical economists : a survey of the recently proposed algorithms

Incluye bibliografíaEn muchos casos, los profesionales de la predicción económica no utilizan los resultados de la investigación econométrica porque esta no se realiza de forma apropiada para su implementación práctica. Este documento intenta cerrar ese hueco que existe entre la investigación en predicción en tiempo real y su aplicación práctica. Con ese objetivo, revisamos las contribuciones recientes más relevantes de la literatura, examinamos sus ventajas e inconvenientes y proponemos nuevas líneas de investigación futura. En nuestro análisis incluimos funciones de transferencia, MIDAS, VARs, modelos factoriales y modelos de cadenas de Markov, en todos los casos, permitiendo mezcla de frecuencias y fechas de publicación diferentes. Usando los cuatro indicadores mensuales del indicador coincidente de Stock y Watson —producción industrial, empleo, renta y ventas—, evaluamos su poder predictivo sobre el PIB de Estados Unidos en tiempo real. Finalmente, revisamos los principales resultados con respecto al número de predictores en modelos de factores y cómo debe realizarse la selección de las series más informativas o representativasPractitioners do not always use research findings, as the research is not always conducted in a manner relevant to real-world practice. This survey seeks to close the gap between research and practice in respect of short-term forecasting in real time. To this end, we review the most relevant recent contributions to the literature, examining their pros and cons, and we take the liberty of proposing some avenues of future research. We include bridge equations, MIDAS, VARs, factor models and Markov-switching factor models, all allowing for mixed-frequency and ragged ends. Using the four constituent monthly series of the Stock-Watson coincident index, industrial production, employment, income and sales, we evaluate their empirical performance to forecast quarterly US GDP growth rates in real time. Finally, we review the main results having regard to the number of predictors in factorbased forecasts and how the selection of the more informative or representative variables can be mad

Risk-sharing among European Countries

This technical report details the results of risk sharing in the EU country by country. The great recession and the subsequent sovereign debt crisis in Europe have shown an asymmetric behavior of the different member countries of the EU, also with regards of risk sharing. We provide country specific measures decomposing risk sharing as that obtained via the capital markets, international transfers and savings or the credit markets channel. Afterwords, we use a mean group estimator to measure average risk sharing for the group of countries. This can help to identify where risk sharing is working and through which channels.JRC.B.1-Finance and Econom

Circulant singular spectrum analysis: a new automated procedure for signal extraction

Sometimes, it is of interest to single out the fluctuations associated to a given frequency. We propose a new variant of SSA, Circulant SSA (CiSSA), that allows to extract the signal associated to any frequency specified beforehand. This is a novelty when compared with other SSA procedures that need to iden- tify ex-post the frequencies associated to the extracted signals. We prove that CiSSA is asymptotically equivalent to these alternative procedures although with the advantage of avoiding the need of the subse- quent frequency identification. We check its good performance and compare it to alternative SSA methods through several simulations for linear and nonlinear time series. We also prove its validity in the nonsta- tionary case. We apply CiSSA in two different fields to show how it works with real data and find that it behaves successfully in both applications. Finally, we compare the performance of CiSSA with other state of the art techniques used for nonlinear and nonstationary signals with amplitude and frequency varying in time.MINECO/FEDE

Circulant Singular Spectrum Analysis to monitor the state of the economy in real time.

Real-time monitoring of the economy is based on activity indicators that show regular patterns such as trends, seasonality and business cycles. However, parametric and non-parametric methods for signal extraction produce revisions at the end of the sample, and the arrival of new data makes it difficult to assess the state of the economy. In this paper, we compare two signal extraction procedures: Circulant Singular Spectral Analysis, CiSSA, a non-parametric technique in which we can extract components associated with desired frequencies, and a parametric method based on ARIMA modelling. Through a set of simulations, we show that the magnitude of the revisions produced by CiSSA converges to zero quicker, and it is smaller than that of the alternative procedure.Ministerio de Ciencia e Innovació