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On the Usefulness of Financial Variables Realised Volatility for Recession Forecasting and Business Cycles Turning Points Dating

100%
Łupińśki M.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2013
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vol. 93: Expectations and Forecasting
29-44
EN
The main goal of this paper was to check usefulness of introducing measures of the financial markets risk into multivariate forecasting and business cycle dating models to improve their predictive and turning points detection power. Realised volatility was selected as market risk synthetic measure and introduced into two recession dating algorithms: Harding & Pagan (2002) mechanical procedure and Markov Switching Dynamic Factor Model (MS-DFM) with mixed frequencies and missing data handling. In the theoretical part of the article mathematical background of the realised volatility concept and MS-DFM model were presented. It was also described how the output of the MS-DFM model can be used to date turning points. This approach to local maxima detection was compared with Harding and Pagan competitor algorithm. In the practical part of the paper recession detection improvements stemming from introduction of realised volatility measures into MS-DFM model/Harding & Pagan procedure were examined for US and four Western Europe countries (Germany, France, United Kingdom and Italy) in the time span of 20 years between 1990 and 2010.
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Porównanie jakości prognozowania polskiego PKB dynamicznymi modelami czynnikowymi oraz czynnikowymi modelami MIDAS

100%
Łupiński M.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2013
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issue 4(42)
85-102
EN
The article is a continuation of the previous author’s papers (2007, 2009, 2012) devoted to the optimal methods of forecasting Polish macroeconomic variables, with the sample of GDP. The research was aimed at a comparison of the quality of nowcasts (”fore-casts” of the present time) and forecasts prepared with a dynamic factor model with mixed frequency and data gaps handling (MFDG-DFM) proposed by Mariano and Murasawa [2003] and MIDAS model augmented with factor structure (DFM-MIDAS), described for the first time in the paper of Marcellino and Schumacher [2008]. Mathematical backgrounds of both models were presented and a combination of Kalman filter and Maximum likelihood estimation was hinted as the estimation framework for both of them. The gained results show an advantage of Mariano and Murasawa approach in the field of the forecasts (approx-imately 15% more adequate forecasts for 2 and more quarters ahead) but this model is less adequate than the competitor for one quarter ahead forecasts and nowcasts.
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Zastosowanie modeli czynnikowych do konstrukcji barometru koniunktury na podstawie badań ankietowych

63%
Dudek S., Zając T.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2012
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vol. 90: Badania koniunktury - zwierciadło gospodarki. Część I
183-213
PL
W opracowaniu przedstawiono syntetyczny wskaźnik aktywności gospodarczej w Polsce (barometr koniunktury), zbudowany na podstawie danych ankietowych Instytutu Rozwoju Gospodarczego SGH i Instytutu Transportu Samochodowego. Do oszacowania barometru koniunktury zastosowano podejście wykorzystujące modele czynnikowe, w tym dynamiczne. Estymacje przeprowadzono trzema alternatywnymi metodami. Oszacowany na ich podstawie nieobserwowalny czynnik potraktowano jako syntetyczny wskaźnik aktywności gospodarczej. Dla odniesienia analizą objęto również barometr koniunktury IRG SGH, konstruowany wg podejścia tradycyjnego. W świetle przeprowadzonych badań zastosowanie modeli czynnikowych do estymacji wskaźnika aktywności gospodarczej opartego na danych ankietowych nieznacznie poprawia użyteczność tego typu wskaźników w monitorowaniu wahań zmiennej referencyjnej (PKB). Prezentowane podejście wymaga dalszych analiz, które pomogą poprawić jakość diagnostyczną barometru koniunktury.
EN
This paper presents a new business cycle indicator of the Polish economy based on survey data from the Research Institute for Economic Development and the Motor Transport Institute. In order to deal with large number of series the factor model approach (FM) is used. Models are estimated using three different methods. The unobserved factor from these models represents a composite indicator. It is compared with the RIED business cycle indicator. The results suggest that the former performs only slightly better than the latter as far as their ability to indicate changes in GDP is concerned. Further research is needed to improve qualities of the proposed business cycle indocator.
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