Markov switching panel with endogenous synchronization effects

Agudze, Komla; Billio, Monica; Casarin, Roberto; Ravazzolo, Francesco

doi:10.1016/j.jeconom.2021.04.004

Cited by 6 publications

(7 citation statements)

References 25 publications

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“…Lastly, the regime switching dynamics could be made more general from the aggregate process presented in our model. The Markov-switching dynamic panel models of Kaufmann (2010), Billio et al (2016), Casarin et al (2018), and Agudze et al (2021) offer a prospective framework that could capture industry-specific cycle synchronization while still controlling for stochastic trends when added to our unobserved components model.…”

Section: Discussionmentioning

confidence: 99%

Industrial Connectedness and Business Cycle Comovements

Guisinger

Owyang

Soques

2024

Econometrics and Statistics

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While aggregate shocks account for most business cycle fluctuations, sectoral shocks have become relatively more important since the 1980s. Previous studies show that sectoral shocks propagate through industry supply chains. Typically, sectors are defined by similarities in function and/or market. While some industries have supply chains within their own sector (vertical), others have supply chains across a number of sectors (horizontal). Similarity in these supply chain characteristics appear to be a determining factor in how industries comove. Using industrial production data of 82 four-digit NAICS industries over the period 1972 to 2019, this comovement is analyzed in a panel Markov-switching model incorporating a number of features relevant for sub-national analysis: (i) industry-specific trends that differentiate cyclical downturns from secular declines; (ii) a nationallevel business cycle; and (iii) factors that represent industrial comovement. While national-level shocks are typically still the most important driver of cyclical fluctuations, endogenously clustering by industry comovement highlights the role of sectoral shocks.

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Section: Discussionmentioning

confidence: 99%

Industrial Connectedness and Business Cycle Comovements

Guisinger

Owyang

Soques

2024

Econometrics and Statistics

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“…Figure 5 depicts the day-by-day evolution of the cross-sectional average of

and the probabilities of being in the second state estimated using the MSM. It is important to note that our data is panel data, and hence we follow Agudze et al (2021) and estimate the probabilities as the average of the smoothed probabilities of all firms. There are two important observations in the figure.…”

Section: Robustness Analysis Based On the Markov Switching Modelmentioning

confidence: 99%

Volatility and dark trading: Evidence from the Covid-19 pandemic

Ibikunle

Rzayev

2023

The British Accounting Review

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“…It is essential to understand the realization of some functions [44]. For example, Josephson junction, collective behavior in the power grid, collective discharge of brain neurons, synchronous flicker of fireflies, deer herd activities, and the emergence of traffic accidents, as well as crowd congestion in the social system, all exhibit synchronous characteristics [45][46][47][48]. Therefore, an exploration of synergistic states and intrinsic dynamics is important to understand the population dynamics of complex systems, promoting relevant experimental and potential application research [49,50].…”

Section: Synchronous Effect Modelmentioning

confidence: 99%

Growth Mechanism and Synchronization Effect of China’s New Energy Vehicle Enterprises: An Empirical Analysis Based on Moving Logistic and Kuramoto Model

Chen

Wang

2022

Sustainability

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The primary purpose of this paper is to discuss whether NEV enterprises can achieve synchronous effects with the whole Chinese automobile industry in terms of growth mode. In this paper, we study the development of new energy vehicles from the perspective of ecosystem. Growth mechanisms and synchronization effects also exist in new energy enterprise populations, just like biological populations in natural ecosystems. Here, we propose a moving logistic model to analyze the growth mechanism of new energy vehicle enterprises and obtain serial data of intrinsic growth rate, internal inhibition coefficient, and theoretical maximum sales volume. The intrinsic growth rate and theoretical maximum sales volume show an initial trend of decline followed by recovery. The evaluation results of coupling degree and synchronization of the new energy vehicle population were obtained through the measurement of the Kuramoto model and its derivative model. The coupling degree of the new energy vehicle population is not high, and the synchronization effect fluctuates and oscillates. The change trend of synchronization effect is similar to that of intrinsic growth rate and theoretical maximum sales volume. This phenomenon shows that the new energy vehicle population has been significantly affected by changes in the external market environment. The analysis method of enterprise growth mechanism based on the moving logistic model and the measurement method of coupling degree and synchronization effect based on the Kuramoto model and its derivative models proposed in this paper effectively achieve the research objectives of this paper.

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Markov switching panel with endogenous synchronization effects

Cited by 6 publications

References 25 publications

Industrial Connectedness and Business Cycle Comovements

Industrial Connectedness and Business Cycle Comovements

Volatility and dark trading: Evidence from the Covid-19 pandemic

Growth Mechanism and Synchronization Effect of China’s New Energy Vehicle Enterprises: An Empirical Analysis Based on Moving Logistic and Kuramoto Model

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