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The procedure of business cycle turning points identification based on hidden Markov models

100%

Bernardelli M.

Prace i Materiały Instytutu Rozwoju Gospodarczego SGH

2015

vol. 96: Analyzing and forecasting economic fluctuations

5-23

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, and the approach seems to be an excellent alternative for classical methods.

Ukryte modele Markowa w analizie wyników testu koniunktury gospodarczej

72%

Bernardelli M., Dędys M.

Prace i Materiały Instytutu Rozwoju Gospodarczego SGH

2012

vol. 90: Badania koniunktury - zwierciadło gospodarki. Część I

159-181

W pracy zbadana została możliwość wykorzystania algorytmu Viterbiego do analizy sald odpowiedzi respondentów na pytania testu koniunktury w przemyśle, prowadzonego przez Instytut Rozwoju Gospodarczego Szkoły Głównej Handlowej w Warszawie. W badaniu rozważane były pytania dotyczące oceny stanu obecnego. Do analizy wykorzystane zostały ukryte modele Markowa z warunkowymi rozkładami normalnymi. Pod uwagę brane były modele, w których łańcuchy Markowa mają dwuelementową i trójelementową przestrzeń stanów. Uzyskane wyniki zostały skonfrontowane z pochodzącymi z różnych źródeł datowaniami punktów zwrotnych cyklu koniunkturalnego. Badane modele zostały porównane pod względem skuteczności w wychwytywaniu sygnałów o nadchodzących zmianach w koniunkturze. Przeprowadzone analizy przemawiają za stosowaniem modeli z trzystanowymi łańcuchami Markowa. Wyniki badania sugerują ponadto, iż należy brać pod uwagę opóźnienia między odpowiedziami respondentów a zmianami klimatu koniunktury.

The paper considers the possibility of using the Viterbi algorithm to analyse results of the RIED WSE business surveys in the manufacturing industry.The analysis was focused on the state balances. The hidden Markov models with conditional normal distributions were applied. There were considered models with two-state and three-state Markov chains. The results were compared with the timing of turning points taken from other sources. The tested models were compared in terms of effectiveness in detecting of coming changes in economic conditions. The analysis suggests models with three-state Markov chains be used. The results also suggest that it is necessary to take into account a delay between the opinions of survey respondents and changes in economic climate.

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2 Prace i Materiały Instytutu Rozwoju Gospodarczego SGH

2 Bernardelli M.

1 Dędys M.

1 2015

1 2012

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The procedure of business cycle turning points identification based on hidden Markov models

Ukryte modele Markowa w analizie wyników testu koniunktury gospodarczej