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The Viterbi paths in an analysis of business cycle synchronization

100%
Bernardelli M., Dędys M.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2015
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vol. 97: Economic cycles and uncertainty
9-23
EN
In the paper we investigate possibility of using the Viterbi paths to analyze two-dimensional macroeconomic time series. We build a two-dimensional Gaussian Markov-switching model with a four-state hidden Markov chain. The model is tested with two pairs of monthly indexes of industrial production for: Poland vs. France, and Poland vs. Germany. The most likely sequence of states of the hidden Markov chain is found for each pair. We compare that sequence with analogous sequences determined for a one-dimensional model with a two-state hidden Markov chain. The results of the comparison suggests the four state Viterbi path provides more valuable information about business cycle synchronization between the two economies than two separate two-state Viterbi paths.
2
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Time stability of the impact of institutions on economic growth and real convergence of the EU countries: implications from the hidden Markov models analysis

88%
Bernardelli M., Próchniak M., Witkowski B.
Equilibrium. Quarterly Journal of Economics and Economic Policy
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2021
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vol. 16
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issue 2
285-323
EN
Research background: It is not straightforward to identify the role of institutions for the economic growth. The possible unknown or uncertain areas refer to nonlinearities, time stability, transmission channels, and institutional complementarities. The research problem tackled in this paper is the analysis of the time stability of the relationship between institutions and economic growth and real economic convergence. Purpose of the article: The article aims to verify whether the impact of the institutional environment on GDP dynamics was stable over time or diffed in various subperiods. The analysis covers the EU28 countries and the 1995?2019 period. Methods: We use regression equations with time dummies and interactions to assess the stability of the impact of institutions on economic growth. The analysis is based on the partially overlapping observations. The models are estimated with the use of Blundell and Bond?s GMM system estimator. The results are then averaged with the Bayesian Model Averaging (BMA) approach. Structural breaks are identified on the basis of the Hidden Markov Models (HMM). Findings & value added: The value added of the study is threefold. First, we use the HMM approach to find structural breaks. Second, the BMA method is applied to assess the robustness of the outcomes. Third, we show the potential of HMM in foresighting. The results of regression estimates indicate that good institution reflected in the greater scope of economic freedom and better governance lead to the higher economic growth of the EU countries. However, the impact of institutions on economic growth was not stable over time.
3
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Hidden Markov Models as a Tool for the Assessment of Dependence of Phenomena of Economic Nature

51%
Bernardelli M.
Acta Universitatis Lodziensis. Folia Oeconomica
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2018
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vol. 5
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issue 338
7-20
PL
Ocena zależności między szeregami czasowymi jest zagadnieniem, które jest często rozwiązywane za pomocą współczynnika korelacji Pearsona. Niestety, czasami wyniki mogą być bardzo mylące. W artykule przedstawiono alternatywną miarę badania zależności, opartą na ukrytych modelach Markowa oraz ścieżkach Viterbiego. Zaproponowana metoda nie jest uniwersalna, ale wydaje się dość dokładnie odzwierciedlać podobieństwo między szeregami czasowymi, eksponując okresy zbieżności i rozbieżności. Przydatność tej nowej miary została zweryfikowana na przykładach, jak również realnych danych makroekonomicznych. Zaletami tej metody są: słabe założenia stosowalności, łatwość interpretacji wyników, możliwość generalizacji i wysoka skuteczność w ocenie zależności różnych szeregów czasowych o charakterze ekonomicznym. Nie należy jej jednak trakto­wać jako substytutu korelacji Pearsona, a raczej jako uzupełniającą metodę pomiaru zależności.
EN
The assessment of dependence between time series is a common dilemma, which is often solved by the use of the Pearson’s correlation coefficient. Unfortunately, sometimes, the results may be highly misleading. In this paper, an alternative measure is presented. It is based on hidden Markov models and Viterbi paths. The proposed method is in no way universal but seems to provide quite an accurate image of the similarities between time series, by disclosing the periods of convergence and divergence. The usefulness of this new measure is verified by specially crafted examples and real‑life macroeconomic data. There are some definite advantages to this method: the weak assumptions of applicability, ease of interpretation of the results, possibility of easy generalization, and high effectiveness in assessing the dependence of different time series of an economic nature. It should not be treated as a substitute for the Pearson’s correlation, but rather as a complementary method of dependence measure.
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