Small open economies and external shocks: an application of Bayesian global vector autoregression model

This study assesses the impact of external shocks on select small open economies (SOEs) using the Bayesian variant of the global vector autoregression model with time varying parameters and stochastic volatility. We account for the curse of dimensionality in the multi-country VAR system by implementing three different priors in the estimation of the parameters of the model: the Minnesota (M-N) prior of Doan-Litterman et al. (1984; Litterman 1986); the Normal-Gamma (N-G) prior of Park and Casella (Bayesian Anal 1:515-533, 2008); and the Stochastic Search Variable Selection (SSVS) prior of George and McCulloch (1995) as extended by Koop and Korobilis (2010, 2013). From our simulation results, we found that global economies of the USA, Western Europe and China are the major drivers of cyclical fluctuation in the SOEs. However, in spite of the perceived superior influence of China on the SOEs GDPs' response to external shocks, we found no evidence to conclude that the influence is significantly greater than those exerted by the United States or Europe on the bloc's economies.

Does technology-seeking OFDI improve the productivity of Chinese firms under the COVID-19 pandemic?

This paper empirically investigates the impact of technology-seeking outward foreign direct investment (OFDI) on firms' productivity under the influence of negative external shocks, taking as a sample the investment data of Chinese firms before and during COVID-19. The results show that technology-seeking OFDI improves productivity, but not under negative external shocks. The dampening effect of such shocks is more significant when the host country is a developed country and in firms with multiple branches. Technology-seeking OFDI particularly improves the productivity of research and development and processing firms, and (among the productivity measures tested) most prominently affects total factor productivity.

Effects of COVID-19 on Global Financial Markets: Evidence from Qualitative Research for Developed and Developing Economies

The research aims to prioritize the pandemic's impact on the financial markets of developed and developing economies using a multi-criteria decision-making approach. The results revealed that COVID-19's pandemic effects on financial markets differ between developed and developing nations. COVID-19 pandemic affects developed countries' financial markets more through supply reduction, demand reduction, and economic instability. Regarding developing nations, confidence and expectations, changes in consumption patterns, and the bandwagon effect are the three most significant impacts of COVID-19 pandemic on financial markets. The best decisions to lower the effect of COVID-19 pandemic on developed nations' financial markets are the declaration of the stimulus package and support of small-and-medium-sized enterprises. Contrastingly, in developing countries, support for vulnerable households and declaration of the stimulus package are the best decisions to combat COVID-19's negative impact on their financial markets. As practical policy implications for lowering COVID-19's negative impact on financial markets, the promotion of new financing instruments, reconstruction of the relationship between public and private sectors, and support of vulnerable households and enterprises are highly recommended.

Reliability analysis for the fractional-order circuit system subject to the uncertain random fractional-order model with Caputo type

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We investigate the independent competing failure process of a RC system in an uncertain random environment.

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Continuous degradation of the system is subject to an uncertain fractional process.

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External shocks obey a random distribution given by real data.

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Two shock models that lead to hard failure are considered.

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Analytical express the system reliability and perform numerical simulations.

Introduction

According to the competing failure theorem, the fractional-order Resistor Capacitance (RC) circuit system suffers not only from internal degradation but also from external shocks. However, due to the general differences of each failure type in the data availability and cognitive uncertainty, a better model is needed to describe the degradation process within the system. Also, a new reliability analysis method is needed for the circuit system under internal degradation and external shocks.

Objectives

To demonstrate this problem, this paper proposes a novel class of Caputo-type uncertain random fractional-order model that focuses on the reliability analysis of a fractional-order RC circuit system.

Methods

First, an uncertain Liu process is used to describe the internal degradation of soft faults and a stochastic process is used to describe the external random shocks of hard faults. Secondly, taking into account the correlation and competition among the fault types, an extreme shock model and a cumulative shock model are constructed, and chance theory is introduced to further explore the fault mechanisms, from which the corresponding reliability indices are derived. Finally, the predictor–corrector method is applied and numerical examples are given.

Results

This paper presents a reliability analysis of a fractional-order RC circuit system with internal failure obeying an uncertain process and external failure obeying a stochastic process, and gives the calculation of the reliability indexes for different cases and the corresponding numerical simulations.

Conclusion

A new competing failure model for a fractional-order RC circuit system is presented and analyzed for reliability, which is proved to be of practical importance by numerical simulations.