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Autor
Bernardelli Michał (Warsaw School of Economics, Poland)
Tytuł
The procedure of business cycle turning points identification based on hidden Markov models
Źródło
Prace i Materiały Instytutu Rozwoju Gospodarczego / Szkoła Główna Handlowa, 2015, nr 96, s. 5-23, rya., tab., bibliogr. 30 poz.
Tytuł własny numeru
Analyzing and forecasting economic fluctuations
Słowa kluczowe
Modele Markowa, Badania koniunktury, Łańcuch Markowa, Metoda Monte Carlo, Algorytm Viterbiego, Ukryty model Markowa
Markov models, Business surveys, Markov chain, Monte Carlo method, Viterbi algorithm, Hidden Markov model
Uwagi
summ.
Abstrakt
In the paper the procedure, based on hidden Markov chains with conditional normal distributions and uses algorithms such as time series decompositions (STL), Baum-Welch algorithm, Viterbi algorithm and Monte Carlo simulations, is proposed to analyze data out of the business tendency survey conducted by the Research Institute for Economic Development, Warsaw School of Economics. There are considered three types of models, namely, with two-state, three-state and four-state Markov chains. Results of the procedure could be treated as an approximation of business cycle turning points. The performed analysis speaks in favor of multistate models. Due to, an increasing with the number of states, numerical instability, it is not obvious which model should be considered as the best one. For this purpose various optimization criteria are taken into consideration: information criteria (AIC, BIC) and the maximum-likelihood, but also frequency of obtaining a given set of parameters in the Monte Carlo simulations. The results are confronted with the turning points dated by OECD. The tested models were compared in terms of their effectiveness in detecting of turning points. The procedure is a step into automation of business cycle analysis based on results of business tendency surveys. Though this automation covers only some models from millions of possibilities, the procedure turns out to be extremely accurate in business cycle turning points identification,(original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej w Warszawie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu
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Bibliografia
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  7. Bernardelli, M. (2013b). Optimization criteria in the algorithm using hidden Markov models in the analysis of the economic data, in Rola informatyki w naukach ekonomicznych i społecznych. Innowacje i implikacje interdyscyplinarne, vol. 2, Zieliński Z. (ed.), Kielce: Wydawnictwo Wyższej Szkoły Handlowej, 43-53.
  8. Bernardelli, M. (2014). Parallel deterministic procedure based on hidden Markov models for the analysis of economic cycles in Poland, Roczniki Kolegium Analiz Ekonomicznych SGH, 34, 75-87.
  9. Bernardelli, M., Dędys, M. (2012). Hidden Markov models in analysis of results of business tendency surveys, Prace i Materiały Instytutu Rozwoju Gospodarczego SGH, 90, 159-181.
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ISSN
0866-9503
Język
eng
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