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Long-term investors are often hesitant to invest in assets or strategies prone to significant drawdowns, primarily due to the challenge of predicting these drawdowns. This study presents a multivariate Markov-switching model for small- and large-cap returns in the U.S. equity markets, demonstrating that three distinct regimes are necessary to capture the negative trends in expected returns during financial crises. Our findings indicate that this framework enhances the prediction of conditional drawdowns compared to standard alternative models of financial returns. Furthermore, out-of-sample analysis shows that investment strategies based on these predictions outperform those relying on models with one or two regimes. [ABSTRACT FROM AUTHOR]

In recent works in manpower planning interest has been awakened in modeling manpower systems in departmentalized framework. This, as a form of disaggregation, may solve the problem of observable heterogeneity but not latent heterogeneity; it rather opens up other aspects of latent heterogeneity hitherto unaccounted for in classical (non departmentalized) manpower models. In this article, a multinomial Markov-switching model is formulated for investigating latent heterogeneity in intra-departmental and interdepartmental transitions in departmentalized manpower systems. The formulation incorporates extensions of the mover-stayer principle resulting in several competing models. The best manpower model is chosen based on the optimum number of hidden states established by the use of Expectation-Maximization iterative algorithm for estimation of the model parameters and a search procedure for assessing model performance against one another. The illustration establishes the usefulness of the model formulation in highlighting hidden disparities in personnel transitions in a departmentalized manpower system and in avoiding wrong model specification. [ABSTRACT FROM AUTHOR]

This paper explores the comparative role of sovereign default risk and several high-frequency macrofinancial indicators that may explain the drop in European renewable energy stocks observed during the 2008 financial crisis and the European debt crisis. We use a two-state time-varying transition probability Markov-switching model to investigate how they impact the bull and bear market trends of renewable stocks. Our main finding is that public financing conditions, captured by sovereign default risks, play a key role in both market regimes, while the other variables affect the renewable energy stocks only in calm or turmoil periods. Moreover, sovereign risk is identified as the main determinant of the European renewable energy stock dynamics in both regimes in the period under review. Finally, we suggest that this effect may be due to the sensitivity of investors to the energy policy uncertainty, entailed by such a pressure on public finances. JEL Classification: G01, G15, Q42 [ABSTRACT FROM AUTHOR]

ABSTRACT The occurrences and domination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are still crucial factors for determining the coronavirus disease 19 (COVID-19) policies. We collected weekly Phylogenetic Assignment of Named Global Outbreak sub-lineages, naming genetically distinct lineages of SARS-CoV-2, including variants of concern, in the United Kingdom, South Africa, South Korea, Denmark, Germany, the United States, and worldwide. This study included 12,296,756 samples of the max share of the sub-lineages from the 33rd week of 2020 to the 40th week of 2022. This study conducted a two-state Markov-switching model to estimate the probability of the phase shift state and predicted the probability of each regime with the Hamilton filter and Kim’s smoothing algorithm. We discovered different weekly patterns based on dominant SARS-CoV-2 variants in target area. Due to differences in containment policies and outbreak waves, we observed a time lag in dominant variants in these countries. Using the inferred probability of the phase shift regime for forecasting, it showed significant probabilities that the stable phase will still be stable in the next week. It also showed significant probabilities that the unstable phase will still be unstable in the next week. Our findings present the probability of observing the phase shift regime each week. Until a new SARS-CoV-2 variant occurs, the regime tended to stay with a low probability of phase shift regime. When a new SARS-CoV-2 variant would occur, the regime would immediately react and change the probability. We propose the Markov-switching model to determine COVID-19 policies and predict SARS-CoV-2 variants. IMPORTANCE Using regime-switching models, we attempted to determine whether there is a link between changes in severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) variants and infection waves, as well as forecasting new SARS-Cov-2 variants. We believe that our study makes a significant contribution to the field because it proposes a new approach for forecasting the ongoing pandemic, and the spread of other infectious diseases, using a statistical model which incorporates unpredictable factors such as human behavior, political factors, and cultural beliefs.

Many recent papers have investigated the role played by volatility in determining the cross-section of currency returns. This paper employs two time-varying factor models: a threshold model and a Markov-switching model to price the excess returns from the currency carry trade. We show that the importance of volatility depends on whether the currency markets are unexpectedly volatile. Volatility innovations during relatively tranquil periods are largely unrewarded in the market, whereas during the unexpected volatile period, this risk has a substantial impact on currency returns. The empirical results show that the two time-varying factor models fit the data better and generate a smaller pricing error than the linear model, while the Markov-switching model outperforms the threshold factor models not only by generating lower pricing errors but also distinguishes two regimes endogenously and without any predetermined state variables. [ABSTRACT FROM AUTHOR]

Increased spending has been one of the main concerns of the financial policies for various countries in the last two decades. Specifically, in those countries where the debt is at relatively high levels, examining the financial sustainability becomes more crucial; since high levels of debt limit the government's choices in issuing debt, imposing taxes, and printing money to correct the deficit. Therefore, one of the criteria for evaluating financial policies is the sustainability of financial policy. This study attempts to estimate the coefficient of influence of the variables affecting the financial balance of the Iranian government in stable and unstable financial regimes for the period 1971-2019. To this end, the financial balance was considered as a dependent variable in the regimes, and the estimated coefficient of the first interval of the debt-to-GDP ratio variable was chosen as the determining factor. Five states of the research model were estimated by considering different explanatory variables and the best fit was selected. The results showed that both stable and unstable regimes have a high probability of permanence, and the absolute value of the first lag coefficient of the debt-to-GDP ratio variable is significantly higher in the instable regime than in the stable regime. Thus, the greater reduction effect of the ratio of debt to GDP of the previous period on the financial balance of the government in the unstable regime compared to its increasing effect in the stable regime; also, during the period of time, the stable financial regime can be the dominant financial regime and it can be said that the NPG condition (No Ponzi game condition) is valid in this era.In this study, the estimation of the financial balance of the government of Iran in sustainable and unsustainable financial regimes in the period of 1971 to 2019 has been done in the form of Markov-switching model. For this purpose, the financial balance was estimated as a dependent variable in the regimes, and the estimated coefficient of the first lag of the debt-to-GDP ratio variable was chosen as the determining factor of the regime. 5 states of the model were estimated by considering different explanatory variables and the best fit (estimate) was selected. The obtained results showed that both sustainable and unsustainable regimes have a high probability of permanence and the absolute value of the coefficient of the first lag of the debt-to-GDP ratio variable in the significant mode is higher in the unsustainable regime than in the sustainable regime,

In this article, a new autoregressive process for finite-range time series of counts is proposed to analyse the finite-range integer-valued data based on an invisible Markov chain. We derive the probabilistic and statistical properties of the model. Conditional least squares (CLS) method and conditional maximum likelihood (CML) method are employed to estimate the parameters of interest. Furthermore, the forecasting problem is addressed. In addition, multiple simulation studies are performed to investigate the finite sample performance of parameter estimators and to compare the proposed estimation methods. The proposed model is applied to a finite-range data series of measles infections in Germany in 2004–2005. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate and compare investment opportunities in the financial markets of Saudi Arabia, a net oil-exporting country, and Turkey, a net oil-importing country, in the Middle East. The international capital asset pricing model (ICAPM) was extended by considering local factors proxied by country risk (CR) and oil price risk exposures of the excess returns of Saudi Arabia and Turkey. In this study, we employed the extended ICAPM in a two-state Markov-switching setting for the sample period of January 2005 to December 2018 to explore whether the risk premium is time-varying. The results suggested that systematic risk is time-varying depending on the state of the financial markets and is affected by both global and local factors. Saudi Arabia offered higher excess returns during the high-volatility regime compared to that of the World Index and enjoyed higher returns during the low-risk regime from oil price shocks. Turkey was negatively affected by oil price shocks and was rather sensitive to the country's risk factor, which varied with both the state of the market and the time factor. These findings will be useful to international investors in diversifying their risks. This study differs from others in estimating the risk premium (beta) by taking into account both the local and global factors and the dynamic nature of systematic risk. [ABSTRACT FROM AUTHOR]

This study develops two types of robust test statistics applicable to Markov-switching autoregressive models. The test statistics can be constructed by sum functionals of the "smoothed" probabilities that a given observation came from a particular regime and do not require the estimation of additional parameters. Monte Carlo experiments show that the tests have good finite-sample size and power properties. The tests are applied to investigate the fluctuations in real GNP growth in the U.S. [ABSTRACT FROM AUTHOR]

Previous "oil curse" studies primarily estimate a single, linear effect of oil rents on income using time-invariant parameters over entire sample periods. This means the true effects of oil dependence cannot be captured if structural changes are taking place, or effects are non-linear. We introduce a two regime Markov-switching model into the resource effects literature to assess the time-varying effects of oil rent dependence on the Malaysian manufacturing sector. We also allow for non-linear threshold effects. We find the impact of oil rents is regime-dependent. Under a rarer "first regime" structure, there is no significant effect. Under a predominant "second regime," there is an inverted U-shaped effect, with oil rents' share of GDP up to 8% positively associated with manufacturing, and negatively associated beyond this. We find connections between regime changes and the 1997 Asian financial crisis and 2008 global financial crisis. Implications for effective diversification policies are discussed. [ABSTRACT FROM AUTHOR]

We examine the dynamics of the impact of the evolving policy response during the COVID‐19 pandemic on the equity market sentiment in India. We operationalise our study by examining the India VIX, the fear gauge of the Indian equity market as an indicator for the market sentiment, and the country level Government Response Index of the Blavatnik School of Government, Oxford University as an indicator for the policy response. The relation is examined through the Markov‐switching model using high‐frequency daily data from January 30, 2020, to May 31, 2021. The evidence suggests that the policy response has a positive impact on the market sentiment when the market is fearful. Further, the evidence suggests that both the high‐fear state and the low‐fear state of the market sentiment given by the model are short‐lived indicating heightened volatility and possible speculation during the ongoing pandemic in the Indian equity market. [ABSTRACT FROM AUTHOR]

The contention in the literature is the relative contribution of private and public investment on economic growth and whether the relationship is linear or non-linear. In addition, there is the issue of whether the impact of investment on economic growth changes depending on public and private investment Purpose. The study examines the relationship between investment (public and private) and economic growth in Nigeria over the period 1970-2016. Design/Methodology/Approach. The study employs Markov regime-switching approach developed by Hamilton (1989, 1990). Specifically, a multivariate dynamic Markov-switching model is estimated using maximum likelihood estimation techniques. The study employs annual time-series sourced from Central Bank of Nigeria, Statistical Bulletin and World Bank, World Development Indicator. Findings/Implications. The results show that the relationship between investment and economic growth is non-linear. Also, both public and private investments have a significant positive impact on economic growth. However, private investment contributes more to economic growth than public investment during the period of expansion. The reverse is the case during the period of contraction. The results support the basic neoclassical framework, with emphasis on savings and investment for analyzing long-term growth performance. Also, it is crucial to make a distinction between the impact of investment (public and private) on growth in two stages of growth. Originality. Government needs to be innovative by spending more during period of slump as more public investment will be required to pump prime the economy for increased private investment.

We investigate the influence of moves in oil prices on exchange rates of Indonesia, Malaysia, the Philippines, Singapore and Thailand (the ASEAN-5 countries). We disentangle oil shocks, representing them by three components: demand shock, supply shock and risk shock, and examine their impact on the ASEAN-5 exchange rates by employing high-/low-volatility Markov regime-switching regressions for the period 2006 to Beckmann, Czudaj, and Arora 2020. We find that demand shocks make forex rates increase for net oil-producing as well as net oil-consuming economies. The impacts of supply shocks on forex rates for most economies are rather low. The risk shocks lead to depreciating effects on the ASEAN-5 currencies, supporting the notion that the open-oriented nature of ASEAN-5 economies makes them susceptible to constant fluctuations in the global oil market. We study interactions between the price of crude oil and exchange rates of the ASEAN-5 economies. We decompose changes in oil price into demand-, supply- and risk-driven components. We show non-linear interrelations between movements in forex rates and price of oil. Demand shocks appreciate forex rates for both net oil-producer and net oil-consumer economies. Supply-driven moves in oil prices exercise a marginal influence on forex rates for most countries. Risk shocks have depreciating effects on the ASEAN-5 exchange rates. [ABSTRACT FROM AUTHOR]

Forecasting tourism demand taking cyclical patterns into account has gained popularity. The focus has traditionally been on univariate time‐series models, which does not consider the influence of varying elasticities in upswings and downswings. This article aims to fill this void by forecasting tourism demand to five destinations, using a Markov‐switching approach. Rolling forecasts using two variants of the Markov‐Switching model are compared to traditional models, including autoregressive distributed lag, autoregressive integrated moving average and naïve forecasts. The findings show that accounting for asymmetric behaviour in the tourism cycle itself or in price and income elasticities improves forecasts, especially for long‐haul destinations. [ABSTRACT FROM AUTHOR]

This paper advances a volatility-regime-switching mechanism to investigate the intensity and direction of the volatility spillover effect in carbon–energy markets. Switching between a low-volatility (LV) and high-volatility (HV) regime, our mechanism involves a four-state system (i.e., LV-LV, HV-LV, LV-HV and HV-HV). Our findings are listed as follows: First, the highest EUA–WTI correlation occurs when both are in an HV regime (i.e., HV-HV), revealing the intensity of the volatility spillover effect. Second, when EUA and WTI are experiencing an opposite volatility regime (one in LV and the other in HV), a higher EUA–WTI correlation is observed when WTI is in an HV regime. This result implies that the direction of the volatility spillover effect is from the energy market to the carbon market. Third, the regime-switching model involving the non-uniform volatility–correlation relations outperforms the conventional GARCH and DCC models in volatility forecasting and portfolio construction. [ABSTRACT FROM AUTHOR]

A class of integer-valued autoregressive models is considered, which are based on a latten process e.g., a Markov chain. Some statistical properties of this class of models are discussed. Moreover, parameter estimation and forecasting are addressed. A Monte Carlo simulation study is conducted to examine the finite sample performance of the given estimation procedure. Finally, the performance of these models is illustrated by empirical applications to two sets of real counts. [ABSTRACT FROM AUTHOR]

This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency of the ML estimator and local asymptotic normality for the models under general conditions, which allow for autoregressive dynamics in the observable process, Markov regime sequences with covariate‐dependent transition matrices, and possible model misspecification. A Monte Carlo study examines the finite‐sample properties of the ML estimator in correctly specified and misspecified models. An empirical application is also discussed. [ABSTRACT FROM AUTHOR]

This study analyzes the co-integration relationship between sovereign bonds and credit default swaps (CDS) and then examines the impact of CDS-bond deviation from the relationship on market volatility using the Markov-switching approach. Our empirical sample consists of the daily CDS premium and bond yield spread obtained with the DataStream database for the period from 2008 to 2014. Our empirical results show that the absolute value of the CDS-bond deviation is positively related to the probability of a high volatility regime and negatively related to the probability of a low volatility regime. This result implies a positive association between the CDS-bond deviation and the volatility in the CDS-bond market. Our findings are consistent across mature-market and emerging-market countries. Moreover, the evidence we uncover suggests that the practice of managing default risk of bonds via the use of CDS may increase the interest rate risk of the bond, which implies both wins and woes from the introduction of CDS, particularly for mature-market countries. [ABSTRACT FROM AUTHOR]

This paper attempts to develop a financial vulnerability indicator for China as a barometer for the state of financial vulnerability in the Chinese financial market, possibly for real-time application. Twelve variables from different sectors are utilised to extract a common vulnerability component using a dynamic approximate factor model. Through the implementation of a Markovswitching Bayesian vector autoregression (MSBVAR) model, the empirical results indicate that a high-vulnerability episode is associated with substantially lower economic activity, but a low-vulnerability episode does not incur substantial changes in economic activity. Notably, the constructed indicator can serve as a real-time early warning system to signify vulnerabilities in the Chinese financial market. First published online 20 November 2020

In this research, the role of nominal exchange rate volatility and business cycles on the banking nonperforming loans was investigated by using Markov-Switching model during 2005-2018 using seasonal data. Business cycles were extracted from GDP by using the Hodrick Prescott filter. Also, the wavelet transform model was used to extract nominal exchange rate fluctuations. The results showed that the exchange rate volatility varies in different periods of time and in longer period of time, the foreign exchange rate volatility has a greater negative and significant effect on nonperforming loans of banking network. It shows a dependence of government on banking network. Also, the impact of business cycles depends on the nonperforming loans regime. The sustainability of low regime is bigger than high regime. The results also show that the impact of value added of different sectors of economy varies in different regimes of nonperforming loans. These results indicate that banking system should take into account the value added of different sectors of economy and nonperforming loans regimes which could decrease nonperforming loans.

This paper empirically examines South Africa's fiscal sustainability through a Markovswitching model which utilizes quarterly datasets for the period from 1960 to 2019. The results show that public debt responds positively, demonstrating a sustainable fiscal policy. Furthermore, considering the regime-specific feedback coefficients of the fiscal policy rule and the durations of fiscal regimes, the study finds that South Africa's fiscal policy satisfies the No-Ponzi game condition. Therefore, from a policy perspective, the South African government should take measures such as pension reforms, reducing operational expenses, reducing subsidies, and funding micro and small enterprises to gain the double dividend on the expenditure side along with revenue-enhancing measures on consumption taxes to achieve stable public finances and lower debt levels. [ABSTRACT FROM AUTHOR]

The contention in the literature is the relative contribution of private and public investment on economic growth and whether the relationship is linear or non-linear. In addition, there is the issue of whether the impact of investment on economic growth changes depending on public and private investment Purpose. The study examines the relationship between investment (public and private) and economic growth in Nigeria over the period 1970-2016. Design/Methodology/Approach. The study employs Markov regime-switching approach developed by Hamilton (1989,1990). Specifically, a multivariate dynamic Markov-switching model is estimated using maximum likelihood estimation techniques. The study employs annual time-series sourced from Central Bank of Nigeria, Statistical Bulletin and World Bank, World Development Indicator. Findings/Implications. The results show that the relationship between investment and economic growth is non-linear. Also, both public and private investments have a significant positive impact on economic growth. However, private investment contributes more to economic growth than public investment during the period of expansion. The reverse is the case during the period of contraction. The results support the basic neoclassical framework, with emphasis on savings and investment for analyzing long-term growth performance. Also, it is crucial to make a distinction between the impact of investment (public and private) on growth in two stages of growth. Originality. Government needs to be innovative by spending more during period of slump as more public investment will be required to pump prime the economy for increased private investment. [ABSTRACT FROM AUTHOR]

High diamond price volatility can have significant impact on Botswana's diamond‐driven economy. The global economic crisis of 2008–2009 saw the local economy characterised by heightened commodity price uncertainty, falling stock prices and dwindling international demand for diamonds. In this paper we employ a number of techniques to analyse and assess the effect of diamond price volatility on stock returns in Botswana. Firstly, estimation of a Markov Switching model reveals that high volatility regimes in diamond prices have become more frequent and persistent since the recession. Secondly, a bivariate GARCH‐in‐Mean VAR model is estimated and the results recognize that diamond price volatility has a positive and significant influence on stock returns in Botswana. [ABSTRACT FROM AUTHOR]

Introduction: Today, the food-energy nexus is a vital issue. Energy in the food production chain is an essential feature of agricultural development and a critical factor in achieving food security. Energy use in the agricultural sector has increased to respond to the growing demand of the population, as well the limited supply of cultivated lands, and the desire for high standards of living. Therefore, the agricultural sector is heavily dependent on energy that affects agricultural prices. Agricultural price fluctuations are one of the most critical challenges for policymakers. The rapid rise in food prices has a significant negative impact on social welfare, especially the poor in developing countries, which is an issue that is more critical in developing countries than in developed countries. According to the Food and Agriculture Organization (FAO) report in 2018, the food world price index increased from 89.6 to 229.9 during the period from 2002 to 2011. Our literature review shows a distinct lack of research on modeling and analyzing the linkage between agricultural input price shock, especially energy and agricultural commodity prices in Iran. Materials and Methods: The Markov Switching model is a popular non-linear time-series model that involves multiple equations and can characterize the time-series behaviors in different regimes. This model is suitable for describing correlated data that exhibit distinct dynamic patterns during different periods. So, considering the sensitivity of food security and the impact of agricultural input, the main objective of this paper is to develop an econometric model to gain reliable insight into the impact of energy consumption on agricultural inflation, using the Markov Switching approach. To estimate this equation, we will run a MS-AR model, some preliminary tests, such as unit root test and stability test, are employed to ensure the reliability of MS-AR estimation results. Results and Discussion: Due to use of time series data, it is necessary to check the stationary status of variables. We performed a common non-linear unit root test (Kapetanios, Shin and Shell (KSS), Zivot and Andrews, Lee and Strazicich). These results reveal that we can significantly reject the null hypothesis of unit root for API, PPI, FPI, and EC, implying that all four variables considered in this study are stationary with structural breaks at levels. The Markov-Switching model has the various types that each of these is a particular component of the regime-dependent equation. Therefore, to choose the best type, the Akaike information criterion was used, and the model with the minimum value was selected as the optimal one. After model estimation and selection, the LR test indicated that the hypothesis of linearity could be rejected in favor of a Markov switching model. According to this model, the period of the Markov switching model estimation is classified into two regimes. Approximately, all the estimated coefficients of the MSIAH (2) - AR (5) model are found to be significant at the conventional level. Conclusion: The estimation results are consistent with theoretical foundations illustrating the importance of input prices and energy consumption on agricultural commodity prices. As with most experimental studies reviewed, this study has also shown energy consumption has a negative impact on agricultural commodity prices. In other words, it can be contended that during the study period, agricultural input prices have been influential factors on agricultural commodity prices. The findings revealed that the low inflation rate and high inflation rate regimes are stable and that only extreme events can switch regimes. The results of the MS model showed that the effect of input prices on agricultural inflation is different in regimes. In the case of energy, the impact of energy consumption on agricultural commodity prices in the high inflation rate regime is less than the low inflation rate regime because the elimination of energy subsidies policy has been applied in the second regime (high inflation rate). Thus, the results indicate the asymmetric impact of energy consumption shocks on agricultural commodity prices. The effect of agricultural input prices on agricultural commodity prices indicates that Iranian agriculture is significantly affected by changes in input prices. In this study, changes in input prices were caused by various shocks, such as the elimination of energy subsidies and drought. Therefore, it can be concluded that the elimination of energy subsidies and drought were, directly and indirectly, able to affect agricultural inflations through the price of inputs. In conclusion, planners and policymakers must pay attention to this asymmetry in agricultural commodity prices volatility to increase the price stability in agriculture as much as possible by appropriate policy tools. [ABSTRACT FROM AUTHOR]

Volatility indexes provide a tool for investors to speculate and trade on market sentiment regarding future volatility. The risk of trading on volatility indexes can be measured by their second moments, namely, variance and correlation. This study considers the four representative volatility indexes published by the CBOE: stock market volatility index (VIX), crude oil volatility index (OVX), foreign exchange rate volatility index (EVZ), and gold price volatility index (GVZ). To examine their risk, we develop an extended multivariate Markov switching ARCH (MSARCH) model in which regime-switching variances, correlations, and variance-correlation relations are designed. Our empirical sample consists of the four volatility indexes from June 2008 to April 2020 for 612 weekly observations (Wednesday to Wednesday). For the conditional variances, we find evidence of regime-switching processes (switching between low and high volatility regimes) for the individual volatility index returns, with the exception of the GVZ. The estimated probability of the high volatility regime may be used to track economic distress and uncertainty shocks. These results provide evidence for volatility-of-volatility risk. For the conditional correlations, we find a regime-switching relation between variances and correlations. That is, the highest correlation appears when the paired volatility markets are simultaneously experiencing a state of high volatility. By contrast, when the paired volatility markets are encountering different volatility states, the correlation is weaker. These results indicate that the volatility-of-volatility risk is a factor affecting the dynamics of correlations between volatility indexes. [ABSTRACT FROM AUTHOR]

Monetary policy is an effective tool in influencing the macroeconomic variables such as production, employment and the general level of prices. The theoretical literature and empirical evidence show that the effects of monetary policy on macroeconomic variables are asymmetric depending on the level of development and depth of financial markets. The aim of this study is to evaluate the role of financial development in the effectiveness of monetary policy on real output growth during business cycles. To this end, the effect of monetary policy on the real output growth is examined by applying a Markov-switching model and using the quarterly time-series data of Iran during 2001:1 to 2013:4. The results show that the effect of monetary policy on the real output growth during the recessions is significant and positive, while it is insignificant in the boom periods. In other words, the effect of monetary policy on the real output growth is asymmetric. Moreover, the results show that the net effect of monetary policy on the real output growth during business cycles depend on the level of financial development. During business cycles, if the level of financial development increases, then the effects of monetary policy on real output growth will decrease.

Objective: We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time. Existing methods for community detection focus only on single-subject analysis of dynamic networks; while recent extensions to multiple-subjects analysis are limited to static networks. Method: To overcome these limitations, we propose a multi-subject, Markov-switching stochastic block model (MSS-SBM) to identify state-related changes in brain community organization over a group of individuals. We first formulate a multilayer extension of SBM to describe the time-dependent, multi-subject brain networks. We develop a novel procedure for fitting the multilayer SBM that builds on multislice modularity maximization which can uncover a common community partition of all layers (subjects) simultaneously. By augmenting with a dynamic Markov switching process, our proposed method is able to capture a set of distinct, recurring temporal states with respect to inter-community interactions over subjects and the change points between them. Results: Simulation shows accurate community recovery and tracking of dynamic community regimes over multilayer networks by the MSS-SBM. Application to task fMRI reveals meaningful non-assortative brain community motifs, e.g., core-periphery structure at the group level, that are associated with language comprehension and motor functions suggesting their putative role in complex information integration. Our approach detected dynamic reconfiguration of modular connectivity elicited by varying task demands and identified unique profiles of intra and inter-community connectivity across different task conditions. Conclusion: The proposed multilayer network representation provides a principled way of detecting synchronous, dynamic modularity in brain networks across subjects. [ABSTRACT FROM AUTHOR]

The primary purpose of this paper is to investigate whether a novel Markov regime‐switching mixed‐data sampling (MRS‐MIADS) model we design can improve the prediction accuracy of the realized variance (RV) of Bitcoin. Moreover, to verify whether the importance of jumps for RV forecasting changes over time, we extend the standard MIDAS model to characterize two volatility regimes and introduce a jump‐driven time‐varying transition probability between the two regimes. Our results suggest that the proposed novel MRS‐MIDAS model exhibits statistically significant improvement for forecasting the RV of Bitcoin. In addition, we find that jump occurrences significantly increase the persistence of the high‐volatility regime and switch between high‐ and low‐volatility regimes. A wide range of checks confirm the robustness of our results. Finally, the proposed model shows significant improvement for 2‐week and 1‐month horizon forecasts. [ABSTRACT FROM AUTHOR]

This paper examines the impact of unemployment and inflation on family income shares. Unemployment and inflation are decomposed into their structural (long-term), cyclical (short-term), anticipated and unanticipated components, respectively. I propose a new framework to analyze family income quintile shares by using a two-state Markov-switching model, also known as Hamilton's regime-switching model. The benefit of using a Markov-switching model is that it permits capturing complex dynamic behavior of non-linear time series macroeconomic variables. The switching mechanism follows a first-order Markov chain of unobservable state variables and subsequent estimated conditional transition probabilities. This paper applies a two-state regime-switching process to capture the asymmetrical effects of unemployment and inflation on family income shares. The two states of family income quintile shares are state 1 (lower income) and state 2 (upper income). This approach differs from previous studies because it examines income inequality within each quintile rather than between quintiles. It is well documented in the literature that there is income inequality between lower and higher income quintiles. Therefore, the analysis of income inequality within the quintile allows for greater insight into changes in family income share corresponding to changes in the state variables. The general findings are that unemployment has adverse effects on lower income shares while inflation acts like a progressive tax on higher income shares. [ABSTRACT FROM AUTHOR]

The present paper fitted the monthly data set into the Markov regime switching model to examine the relationship between the iron ore price and the Australian dollar (AUD) exchange rate. The study dichotomised the AUD into state 1 (depreciation) and state 2 (appreciation). The empirical results indicate evidence of an asymmetric relationship between an iron ore price change and the AUD exchange rate fluctuation based on states. The AUD appreciates with a fall in iron price and depreciates with a rise in iron ore price. The results contradict with the understanding of the commodity-currency theory. Additionally, iron ore price reduces the AUD state expected duration and the switching probability, but increases the AUD volatility. Based on the transition probability, the AUD has a higher chance of depreciating than appreciating. The statistical economic impact of the AUD currency is higher when appreciating than depreciating. [ABSTRACT FROM AUTHOR]

Markov-modulated Lévy processes with two different regimes of restarting are studied. These regimes correspond to the completely renewed process and to the process of Markov modulation, accompanied by jumps. We give explicit expressions for the Lévy–Khintchine exponent in the case of a two-state underlying Markov chain. For the renewal case, the limit distributions (as t → ∞ ) are obtained. In the case of processes with jumps, we present some results for the exponential functional. [ABSTRACT FROM AUTHOR]

Activities in the construction sector are assumed to be influenced by inflow of mortgage funding in the private housing sector and public finances targeted at large infrastructure projects, apart from climate variables. In this study, we modeled seasonal time series representing monthly output in the Czech construction sector in CZK mil. during 2000:1 through 2016 : 12 (T = 204) adjusted for calendar variations and seasonal movements via TRAMO‑SEATS and then transformed to natural logarithms of gross returns. A Markov‑Switching model with two states, no intercept, average monthly temperature, average monthly precipitation and parameters of first‑order autoregression process was specified and estimated by the Expectation‑Maximization. In State 1 of regular performance, the log‑differenced returns were significantly and positively influenced by precipitation levels, but not by ambient outdoor temperature. In State 2 of non‑standard operation of the construction sector, the transformed series was unaffected by precipitation levels, but instead by ambient outdoor temperatures. First‑order autocorrelation dependency in both regimes was established. Changes in legal and macroeconomic environment pertinent to tax law amendments affecting VAT or corporate tax, country’s accession to EU or large construction project deadlines were shown to induce nonstandard regime in the construction sector (State 2). The model classified 91 % observations in the first state, while only 9 % data belonged to the State 2. Transition probability matrix indicates that change from model State 1 to State 2 is difficult to attain. At the same time, once State 2 was established, it tends to persist or change to State 1 with near equal probability. Ability of the Markov‑Switching model to identify both states is reasonably good.

This paper examines whether the five BRICS countries share similar business cycles and determines the probability of any of the countries moving from a contractionary regime to an expansionary regime. The study further examines the extent to which changes in monetary policy affect industrial output in expansions relative to contractions. Employing the Peersman and Smets (2001) Markov-Switching Model (MSM) and monthly data from 1994.01–2013.12, the study reveals that the five BRICS countries have similar business cycles. The results further demonstrate that the BRICS countries’ business cycles are characterized by two distinct growth rate phases: a contractionary regime and an expansionary regime. It can also be observed that the area-wide monetary policy has significantly large effects on industrial output in recessions as well as in booms. It has also been established that there is a high probability of moving from state one (recession) to state two (expansion) and that on average, the probabilities of staying in state 2 (expansion) are high for each of the five countries. It is, therefore, recommended that the BRICS countries should sustain uniform policy consistency (monetary policy), especially as they formulate and implement economic policies to stimulate industrial output.

Inflation volatility is one of the characteristics of Iranian economy over the past four decades. Inflation volatility by creating macroeconomic instability can affect the relation of economic variables. The purpose of this study is the evaluation of the impact of nonlinear inflation volatility on the relationships between the Iranian trade balance with the exchange rate during the 1973-2016. For this purpose, firstly inflation volatility by using EGARCH method has been estimated and the model was estimated by Markov-switching model. The results show that the behavior of trade balance in Iran can be divided in 3 regimes (high, medium and low trade deficit). Increased exchange rate has induced the improvement of trade balance in 3 regimes. The effect of inflation volatility on the relationship of exchange rate to trade balance in the high and medium trade deficit regime is insignificant. Whereas in the regime 3 (low trade balance deficit) is negative and significant. So that in the regime 3(low trade deficit) inflation volatility has caused to debilitation of exchange rate effect on the trade balance and with the increase in inflation volatility exchange rate effect on the trade balance is further debilitation. [ABSTRACT FROM AUTHOR]

Activities in the construction sector are assumed to be influenced by inflow of mortgage funding in the private housing sector and public finances targeted at large infrastructure projects, apart from climate variables. In this study, we modeled seasonal time series representing monthly output in the Czech construction sector in CZK mil. during 2000:1 through 2016 : 12 (T = 204) adjusted for calendar variations and seasonal movements via TRAMO‑SEATS and then transformed to natural logarithms of gross returns. A Markov‑Switching model with two states, no intercept, average monthly temperature, average monthly precipitation and parameters of first‑order autoregression process was specified and estimated by the Expectation‑Maximization. In State 1 of regular performance, the log‑differenced returns were significantly and positively influenced by precipitation levels, but not by ambient outdoor temperature. In State 2 of non‑standard operation of the construction sector, the transformed series was unaffected by precipitation levels, but instead by ambient outdoor temperatures. First‑order autocorrelation dependency in both regimes was established. Changes in legal and macroeconomic environment pertinent to tax law amendments affecting VAT or corporate tax, country’s accession to EU or large construction project deadlines were shown to induce nonstandard regime in the construction sector (State 2). The model classified 91 % observations in the first state, while only 9 % data belonged to the State 2. Transition probability matrix indicates that change from model State 1 to State 2 is difficult to attain. At the same time, once State 2 was established, it tends to persist or change to State 1 with near equal probability. Ability of the Markov‑Switching model to identify both states is reasonably good. [ABSTRACT FROM AUTHOR]