Статті в журналах: "Endogenous regime switching model"

1

Barthélemy, Jean, and Magali Marx. "Solving endogenous regime switching models." Journal of Economic Dynamics and Control 77 (April 2017): 1–25. http://dx.doi.org/10.1016/j.jedc.2017.01.011.

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2

Kim, Chang-Jin, Jeremy Piger, and Richard Startz. "Estimation of Markov regime-switching regression models with endogenous switching." Journal of Econometrics 143, no. 2 (April 2008): 263–73. http://dx.doi.org/10.1016/j.jeconom.2007.10.002.

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3

Branch, William A., Troy Davig, and Bruce McGough. "ADAPTIVE LEARNING IN REGIME-SWITCHING MODELS." Macroeconomic Dynamics 17, no. 5 (March 6, 2012): 998–1022. http://dx.doi.org/10.1017/s1365100511000800.

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We study adaptive learning in economic environments subject to recurring structural change. Stochastically evolving institutional and policymaking features can be described by regime-switching models with parameters that evolve according to finite state Markov processes. We demonstrate that in nonlinear models of this form, the presence of sunspot equilibria implies two natural schemes for learning the conditional means of endogenous variables: under mean value learning, agents condition on a sunspot variable that captures the self-fulfilling serial correlation in the equilibrium, whereas under vector autoregression learning (VAR learning), the self-fulfilling serial correlation must be learned. We show that an intuitive condition ensures convergence to a regime-switching rational expectations equilibrium. However, the stability of sunspot equilibria, when they exist, depends on whether agents adopt mean value or VAR learning: coordinating on sunspot equilibria via a VAR learning rule is not possible. To illustrate these phenomena, we develop results for an overlapping-generations model and a New Keynesian model.

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4

Calzolari, Giorgio, Maria Gabriella Campolo, Antonino Di Pino, and Laura Magazzini. "Maximum likelihood estimation of an across-regime correlation parameter." Stata Journal: Promoting communications on statistics and Stata 21, no. 2 (June 2021): 430–61. http://dx.doi.org/10.1177/1536867x211025834.

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In this article, we describe the mlcar command, which implements a maximum likelihood method to simultaneously estimate the regression coefficients of a two-regime endogenous switching model and the coefficient measuring the correlation of outcomes between the two regimes. This coefficient, known as the “across-regime” correlation parameter, is generally unidentified in the traditional estimation procedures.

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5

Seidl, Andrea. "Zeno points in optimal control models with endogenous regime switching." Journal of Economic Dynamics and Control 100 (March 2019): 353–68. http://dx.doi.org/10.1016/j.jedc.2018.09.010.

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6

Hubrich, Kirstin, and Daniel Waggoner. "The transmission of financial shocks and leverage of financial institutions: An endogenous regime switching framework." Finance and Economics Discussion Series, no. 2022-034 (June 2022): 1–53. http://dx.doi.org/10.17016/feds.2022.034.

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We conduct a novel empirical analysis of the role of leverage of financial institutions for the transmission of financial shocks to the macroeconomy. For that purpose we develop an endogenous regime-switching structural vector autoregressive model with time-varying transition probabilities that depend on the state of the economy. We propose new identification techniques for regime switching models. Recently developed theoretical models emphasize the role of bank balance sheets for the build-up of financial instabilities and the amplification of financial shocks. We build a market-based measure of leverage of financial institutions employing institution-level data and find empirical evidence that real effects of financial shocks are amplified by the leverage of financial institutions in the financial-constraint regime. We also find evidence of heterogeneity in how financial institutions, including depository financial institutions, global systemically important banks and selected nonbank financial institutions, affect the transmission of shocks to the macroeconomy. Our results confirm the leverage ratio as a useful indicator from a policy perspective.

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7

Hubrich, Kirstin, and Daniel Waggoner. "The transmission of financial shocks and leverage of financial institutions: An endogenous regime switching framework." Finance and Economics Discussion Series, no. 2022-034 (June 2022): 1–53. http://dx.doi.org/10.17016/feds.2022.034.

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We conduct a novel empirical analysis of the role of leverage of financial institutions for the transmission of financial shocks to the macroeconomy. For that purpose we develop an endogenous regime-switching structural vector autoregressive model with time-varying transition probabilities that depend on the state of the economy. We propose new identification techniques for regime switching models. Recently developed theoretical models emphasize the role of bank balance sheets for the build-up of financial instabilities and the amplification of financial shocks. We build a market-based measure of leverage of financial institutions employing institution-level data and find empirical evidence that real effects of financial shocks are amplified by the leverage of financial institutions in the financial-constraint regime. We also find evidence of heterogeneity in how financial institutions, including depository financial institutions, global systemically important banks and selected nonbank financial institutions, affect the transmission of shocks to the macroeconomy. Our results confirm the leverage ratio as a useful indicator from a policy perspective.

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8

Hayashi, Fumio, and Junko Koeda. "Exiting from quantitative easing." Quantitative Economics 10, no. 3 (2019): 1069–107. http://dx.doi.org/10.3982/qe1058.

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We propose an empirical framework for analyzing the macroeconomic effects of quantitative easing (QE) and apply it to Japan. The framework is a regime‐switching structural vector autoregression in which the monetary policy regime, chosen by the central bank responding to economic conditions, is endogenous and observable. QE is modeled as one of the regimes. The model incorporates an exit condition for terminating QE. We find that higher reserves at the effective lower bound raise inflation and output, and that terminating QE may be contractionary or expansionary, depending on the state of the economy at the point of exit.

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9

Kang, Kyu H. "Estimation of state-space models with endogenous Markov regime-switching parameters." Econometrics Journal 17, no. 1 (February 2014): 56–82. http://dx.doi.org/10.1111/ectj.12014.

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10

Alba, Joseph D., and Peiming Wang. "TAYLOR RULE AND DISCRETIONARY REGIMES IN THE UNITED STATES: EVIDENCE FROM A k-STATE MARKOV REGIME-SWITCHING MODEL." Macroeconomic Dynamics 21, no. 3 (August 1, 2016): 817–33. http://dx.doi.org/10.1017/s1365100515000693.

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We examine U.S. monetary policies from 1973 to 2014 with the Taylor rule as a benchmark by utilizing a k-state Markov regime-switching model in which the number and the periods of the regimes are endogenously determined. The model relates the federal funds rate to real time output gaps and inflation forecast. It endogenously identifies the periods of Taylor rule regime and discretionary regimes, consistent with the U.S. experience. The Taylor rule regime also coincides with periods of lower variability in inflation and in real GDP growth.

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11

Choi, Yongok. "Volatility Analysis of Korean Stock Market Using Endogenous Regime Switching Model with Multiple States." Journal of Korean Economics Studies 37, no. 4 (December 31, 2019): 61–79. http://dx.doi.org/10.46665/jkes.2019.12.37.4.61.

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12

Bocher, Temesgen Fitamo, Bamlaku Alamirew Alemu, and Zerihun Getachew Kelbore. "Does access to credit improve household welfare? Evidence from Ethiopia using endogenous regime switching regression." African Journal of Economic and Management Studies 8, no. 1 (March 13, 2017): 51–65. http://dx.doi.org/10.1108/ajems-03-2017-145.

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Purpose The purpose of this paper is to investigate how credit access affects the welfare of households and sheds light on how household characteristics influence the decision to take credit and the efficiency in credit use. Design/methodology/approach This study uses data from the fourth round of the Ethiopian Rural Household Survey conducted in 2009, and examines factors that determine the decision to take credit and the effect of such decision on household welfare. The household welfare variable is measured by the food security indicator and total food expenditure. The study employs endogenous Regime Switching model to account for endogeneity in access to credit and self-selection bias in the decision to participate in credit. Findings The result from the kernel distribution shows households with access to credit have more consumption expenditure than those without access to credit. The ordinary least square regression shows that access to credit increases total consumption by 12 percent without considering self-selection bias. Participation in non-farm activity increases the demand for credit by 17 percent. Land holding, household size, and participation in saving associations increase the probability of getting credit by 5, 11, and 20 percent, respectively. Access to credit appears to have a positive impact on food security in both actual and counterfactual cases for the current credit receivers. Originality/value This study provides a thorough analysis of the impacts of access to credit on household welfare in Ethiopia. The study contributes to the debate on the link between access to credit and household welfare and provides valuable input for policy makers.

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13

Xu, Wan, and Dayton M. Lambert. "Business Establishment Growth in the Appalachian Region, 2000-2007: An Application of Smooth Transition Spatial Process Models." Journal of Agricultural and Applied Economics 43, no. 3 (August 2011): 309–24. http://dx.doi.org/10.1017/s1074070800004314.

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Business establishment growth in the Appalachian region (2000-2007) was regressed on industry sector composition controlling for demographic, physical, and economic determinants. We test the hypothesis that local response to growth determinants is geographically heterogeneous using Smooth Transition spatial process models. This class of models exhibiting endogenous regime switching behavior provides another tool for exploring the spatially heterogeneous effects of local determinants on economic growth.

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14

Hussain, Hafezali Iqbal, Sebastian Kot, Hassanudin Mohd Thas Thaker, and Jason J. Turner. "Environmental Reporting and Speed of Adjustment to Target Leverage: Evidence from a Dynamic Regime Switching Model." Organizacija 53, no. 1 (February 1, 2020): 21–35. http://dx.doi.org/10.2478/orga-2020-0002.

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AbstractBackground and Purpose: This study investigates the impact of environmental reporting on speed of adjustment and adjustment costs which is evaluated based on the ability of firms to adjust to target leverage level for non-financial firms listed in the Malaysian Stock Exchange (Bursa Malaysia).Design/Methodology/ Approach: The study selects Malaysian firms based on the contracting and political cost of the economy which is seen as a relationship-based economy. This in turn influences a firm’s ability to obtain external financing and thus has an important impact on capital structure decisions. In addition, the method employed allows for a direct measure on adjustment cost for firms. The current study utilises a dynamic regime switching model based on the DPF estimator to estimate rate of adjustment to optimal target levels based on the distinction of environmental reporting of public listed firms. The approach allows statistical inferences to control for potential serial correlation, endogeneity and heterogeneity concerns which accounts for firm specific characteristics.Results: The empirical findings suggest voluntary disclosure on environmental reporting increases a firm’s ability to access external financing at a cheaper cost as evidenced by a more rapid rate of adjustment. The findings are consistent across differing endogenous and exogenous factors indicating that these firms tend to face lower adjustment costs.Conclusion: The current study provides a direct measure on the ability of firms to adjust to target levels via security issues and repurchases in the capital markets. This in turn is a reflection of perceived riskiness and value from the investors’ point of view in an emerging market. Prior studies have focused on environmental reporting and equity risk premiums and have not evaluated the direct impact on firm value given that the trade-off theory of capital structure predicts that firm value is maximised at target i.e. optimal levels of leverage. This study addresses the current gap in the literature by evaluating the impact on firms’ value, based on the adjustment cost.

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15

Silva, Marta, Luis Filipe Martins, and Helena Lopes. "Asymmetric Labor Market Reforms: Effects on Wage Growth and Conversion Probability of Fixed-Term Contracts." ILR Review 71, no. 3 (October 23, 2017): 760–88. http://dx.doi.org/10.1177/0019793917737506.

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The authors investigate the impact of a change in employment protection laws in Portugal that increased the maximum legal duration of fixed-term contracts. They find that this reform led to a reduction in the probability that a worker on a fixed-term contract would be converted to a permanent contract. In addition, those workers who had their contracts converted experienced a significantly higher hourly wage growth at the time of conversion and faced a lower reduction in wage growth during the years in which the changed legislation was in force. Consequently, the implementation of this law led to a 27% increase in the wage-growth differential between the two contracts. The findings are based on an endogenous regime-switching model using rich administrative linked employer–employee data.

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16

Chiang, Gengnan, and Ming-Yi Wu. "The Richer the Greener: Evidence from G7 Countries." International Journal of Economics and Finance 9, no. 10 (August 28, 2017): 11. http://dx.doi.org/10.5539/ijef.v9n10p11.

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This research applies a recently-developed nonlinear panel smooth transition regression (PSTR) model and takes into account the potential endogeneity biases to examine whether Environmental Kuznets Curve (EKC) exists in G7 countries over the period 1991-2008. This research makes three contributions to the CO2 emissions literature. First, we apply the panel smooth transition regression (PSTR) model of González et al. (2005) to investigate the relationship among CO2 emissions per capita, energy use per capita, real gross fixed capital formation, real GDP per capita, and labor participation rate for G7 countries. Second, we complement the existing literature by simultaneously examining the impacts of energy use, real gross fixed capital formation, real GDP, and labor participation rate on CO2 emissions and take into account endogenous determination of real GDP on the PSTR model for CO2 emissions. Third, based on the characteristics of the PSTR model, we can consider the elasticity of CO2 emissions changes with country and time to analyze the elasticity of heterogeneous countries and the potential impacts of structural breaks on the CO2 emissions elasticity in the panel framework. Based on the elasticity of the CO2 emissions with respect to real income per capita, the environmental quality is a necessary good in Japan, the UK, and the USA, but a luxury good in the rest of G7 countries. Thus, there exists an inverted U-shaped relationship between CO2 emissions and real income per capita with the threshold value of US$20,488, which is endogenously determined. This finding supports the existence of EKC in G7 countries. In other words, our results confirm there exists the regime-switching effect of real income on CO2 emissions in G7 countries.

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17

Dale, David, and Andrei Sirchenko. "Estimation of nested and zero-inflated ordered probit models." Stata Journal: Promoting communications on statistics and Stata 21, no. 1 (March 2021): 3–38. http://dx.doi.org/10.1177/1536867x211000002.

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We introduce three new commands—nop, ziop2, and ziop3—for the estimation of a three-part nested ordered probit model, the two-part zero-inflated ordered probit models of Harris and Zhao (2007, Journal of Econometrics 141: 1073–1099) and Brooks, Harris, and Spencer (2012, Economics Letters 117: 683–686), and a three-part zero-inflated ordered probit model of Sirchenko (2020, Studies in Nonlinear Dynamics and Econometrics 24: 1) for ordinal outcomes, with both exogenous and endogenous switching. The three-part models allow the probabilities of positive, neutral (zero), and negative outcomes to be generated by distinct processes. The zero-inflated models address a preponderance of zeros and allow them to emerge in different latent regimes. We provide postestimation commands to compute probabilistic predictions and various measures of their accuracy, to assess the goodness of fit, and to perform model comparison using the Vuong test (Vuong, 1989, Econometrica 57: 307–333) with the corrections based on the Akaike and Schwarz information criteria. We investigate the finite-sample performance of the maximum likelihood estimators by Monte Carlo simulations, discuss the relations among the models, and illustrate the new commands with an empirical application to the U.S. federal funds rate target.

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18

Chen, Shun, Shiyuan Zheng, and Hilde Meersman. "Testing for the burst of bubbles in dry bulk shipping market using log periodic power law model." Maritime Business Review 3, no. 2 (June 18, 2018): 128–44. http://dx.doi.org/10.1108/mabr-12-2017-0033.

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Purpose The occurrence and unpredictability of speculative bubbles on financial markets, and their accompanying crashes, have confounded economists and economic historians worldwide. The purpose of this paper is to diagnose and detect the bursting of shipping bubbles ex ante, and to qualify the patterns of shipping price dynamics and the bubble mechanics, so that appropriate counter measures can be taken in advance to reduce side effects arising from bubbles. Design/methodology/approach Log periodic power law (LPPL) model, developed in the past decade, is used to detect large market falls or “crashes” through modeling of the shipping price dynamics on a selection of three historical shipping bubbles over the period of 1985 to 2016. The method is based on a nonlinear least squares estimation that yields predictions of the most probable time of the regime switching. Findings It could be concluded that predictions by the LPPL model are quite dependent on the time at which they are conducted. Interestingly, the LPPL model could have predicted the substantial fall in the Baltic Dry Index during the recent global downturn, but not all crashes in the past. It is also found that the key ingredient that sets off an unsustainable growth process for shipping prices is the positive feedback. When the positive feedback starts, the burst of bubbles in shipping would be influenced by both endogenous and exogenous factors, which are crucial for the advanced warning of the market conversion. Originality/value The LPPL model has been first applied into the dry bulk shipping market to test a couple of shipping bubbles. The authors not only assess the predictability and robustness of the LPPL model but also expand the understanding of the model and explain patterns of shipping price dynamics and bubble mechanics.

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19

Yulisti, Maharani, Tenny Apriliani, Risna Yusuf, and Rismutia Hayu Deswati. "FAKTOR PENENTU ADOPSI STANDAR ORGANIK DAN DAMPAKNYA TERHADAP KINERJA BUDIDAYA UDANG WINDU." Jurnal Sosial Ekonomi Kelautan dan Perikanan 14, no. 1 (June 30, 2019): 73. http://dx.doi.org/10.15578/jsekp.v14i1.7700.

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Standar keamanan pangan di Indonesia telah diusulkan untuk menghadapi tantangan pasar ikan global seperti peningkatan produksi budidaya dan perjanjian perdagangan bebas. Namun, manfaat sertifikasi keamanan pangan bagi pembudidaya ikan sering diperdebatkan. Dampaknya sangat kontekstual, yang sebenarnya sangat relevan dengan sektor perikanan skala kecil yang memiliki tingkat keragaman agro ekologi dan kondisi sosial ekonomi. Ini tidak selalu dipertimbangkan dalam penelitian sebelumnya. Oleh karena itu, kajian ini menganalisis dampak adopsi organic standard terhadap produktivitas petambak udang dengan mengambil studi kasus di Kabupaten Sidoarjo. Analisis yang digunakan untuk mengetahui faktor penentu adopsi menggunakan model probit; sedangkan untuk mengukur dampak terhadap outcome budidaya udang digunakan model endogenous switching regression. Hasil analisis probit menunjukkan bahwa standar organik tampaknya lebih banyak diterapkan pada pembudidaya yang memiliki pekerjaan di luar tambak udang dan lebih banyak memiliki pengalaman, tetapi kurang diadopsi oleh petani yang menyewa tambak, memiliki hubungan pasar dan hubungan kredit dengan pembeli mereka. Hasil analisis dampak menunjukkan bahwa rata-rata dari hasil budidaya udang tidak ditemukan perbedaan yang signifikan antara petambak yang mengadopsi standar dan yang tidak mengadopsi, sedangkan terdapat perbedaan signifikan dalam keuntungan bersih antara dua rezim. Namun, hasil analisis menunjukkan bahwa adopter memiliki hasil lebih besar pada produksi udang jika mereka tidak mengadopsi, begitu pula sebaliknya terhadap nonadopter menghasilkan produksi udang lebih kecil apabila mereka mengadopsi standard. Di sisi lain, adopter memiliki profit lebih kecil pada produksi udang jika mereka tidak mengadopsi, begitu pula sebaliknya terhadap non-adopter menghasilkan profit lebih kecil apabila mereka tidak mengadopsi standard. Determinants for Adopting Organic Standard and Their Impact on Performance of Black Tiger Shrimp FarmingFood safety standard in Indonesia has been proposed to face global fish market challenges such as increasing aquaculture production and free trade agreements. Yet, the benefits of food safety certification for farmers has often been debated. It has context-specific impact and closely relevant to small farm sector with its large degree of agroecological and socio-economic heterogenity. This idea was not always get into consideration in previous researches. Therefore, this paper analyzes the impact of organic standard adoption on productivity of small-scale shrimp farming in Indonesia. The study used a probit model to determine the determinants of adoption, while endogenous switching regression model was used to measure the impact on the outcome of shrimp farming. Heterogeneity is accounted for an endogenous switching regression framework. The analytical result of probit showed that organic standard is more applied to farmers who have off-farm job and experiences, but is less adopted by farmers who rent ponds, have market and credit relationship with their buyers. The result of impact analysis showed that there were no significant differences on shrimp production between those adopted the standard and those who did not, the average yield of shrimp farming was not found to be a significant between farmers who adopted and those who did not adopt the standard, while there were significant differences on net profit between the two regimes. However, the analysis found that adopters had higher results on shrimp production if they do not adopt standard, and nonadopters had less shrimp production if they adopt the standard. On the other hand, adopters have smaller profit on shrimp production if they do not adopt the standard, and non-adopters made smaller profits if they do not adopt the standard.

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20

Sethi, Rajiv. "Endogenous regime switching in speculative markets." Structural Change and Economic Dynamics 7, no. 1 (March 1996): 99–118. http://dx.doi.org/10.1016/0954-349x(95)00040-t.

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21

Tran, Minh Chau, Christopher E. C. Gan, and Baiding Hu. "Credit constraints and their impact on farm household welfare." International Journal of Social Economics 43, no. 8 (August 8, 2016): 782–803. http://dx.doi.org/10.1108/ijse-11-2014-0243.

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Purpose – The purpose of this paper is to identify factors affecting formal credit constraint status of rural farm households in Vietnam’s North Central Coast (NCC) region. Design/methodology/approach – Using the direct elicitation method (DEM), the authors consider both internal and external credit rationing. Findings – Empirical evidences confirm the importance of household head’s age, gender and education to household’s likelihood of being credit constrained. In addition, households who have advantages in farm land size, labour resources and non-farm income are less likely to be credit constrained. Poor households are observed to remain restricted by formal credit institutions. Results from the endogenous switching regression model suggest that credit constraints negatively impact household’s consumption per capita and informal credit can act as a substitute to mitigate the negative influence of formal credit constraints. Research limitations/implications – One limitation arises from the usage of the DEM to identify credit constrained households. The method cannot detect effective and ineffective constraints. Another limitation is the inability of cross-section data to capture long-term impacts of credit constraints on household welfare. Finally, causes of credit constraints from the lender’s view cannot be observed. Practical implications – The results suggest that it is necessary to enhance the credit allocation regime to reduce the transaction cost and provide target households with sufficient credit. It should be emphasized that high transaction cost and the mismatch between credit demand and supply stemming from information asymmetry. The government can help formal financial institutions to reduce information cost by encouraging the active role of social organizations such as Women Unions, Youth Unions and Veteran Unions in bridging rural farm households with formal lenders. Originality/value – There are limited studies focusing on determinants of credit constraints and their impacts on rural farm households. To the best of the knowledge, there is no study evaluating the impact of credit constraints on rural farm household welfare particularly in Vietnam. In addition, the studies related to credit constraints only considered full quantity rationing (households applied for the loan but were rejected), omitting the case of partly quantity rationing (loan obtained by the borrowers is less than their demand) and self-rationing.

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22

Alfeus, Mesias, Ludger Overbeck, and Erik Schlögl. "Regime switching rough Heston model." Journal of Futures Markets 39, no. 5 (January 16, 2019): 538–52. http://dx.doi.org/10.1002/fut.21993.

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23

Leccadito, Arturo, and Stefania Veltri. "A regime switching Ohlson model." Quality & Quantity 49, no. 5 (August 19, 2014): 2015–35. http://dx.doi.org/10.1007/s11135-014-0088-6.

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24

Han, Zhixia, and Jiandong Zhao. "Stochastic SIRS model under regime switching." Nonlinear Analysis: Real World Applications 14, no. 1 (February 2013): 352–64. http://dx.doi.org/10.1016/j.nonrwa.2012.06.008.

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25

Ferri, Piero, and Edward Greenberg. "A wage-price regime switching model." Journal of Economic Behavior & Organization 13, no. 1 (January 1990): 77–95. http://dx.doi.org/10.1016/0167-2681(90)90054-h.

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26

Lv, Guangying, and Beibei Zhang. "Permanence and extinction of stochastic regime-switching mutualism model." International Journal of Biomathematics 13, no. 04 (April 28, 2020): 2050028. http://dx.doi.org/10.1142/s179352452050028x.

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This paper is concerned with the permanence and extinction of a stochastic regime-switching mutualism model. We aim to find the difference between the stochastic mutualism model with regime-switching and without regime-switching. By studying ergodicity of regime-switching diffusion processes, we establish the sufficient conditions to estimate the permanence and extinction of a species in a random switching environment. Moreover, compared with the system without switching, the advantages of the stochastic regime-switching mutualism model are given.

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27

Caminal, Ramon, and Carmen Matutes. "Endogenous switching costs in a duopoly model." International Journal of Industrial Organization 8, no. 3 (September 1990): 353–73. http://dx.doi.org/10.1016/0167-7187(90)90002-i.

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28

Cai, Jun. "A Markov Model of Switching-Regime ARCH." Journal of Business & Economic Statistics 12, no. 3 (July 1994): 309. http://dx.doi.org/10.2307/1392087.

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Cai, Jun. "A Markov Model of Switching-Regime ARCH." Journal of Business & Economic Statistics 12, no. 3 (July 1994): 309–16. http://dx.doi.org/10.1080/07350015.1994.10524546.

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30

Wu, Zheng, Hao Huang, and Lianglong Wang. "Stochastic Delay Logistic Model under Regime Switching." Abstract and Applied Analysis 2012 (2012): 1–26. http://dx.doi.org/10.1155/2012/241702.

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This paper is concerned with a delay logistical model under regime switching diffusion in random environment. By using generalized Itô formula, Gronwall's inequality, and Young's inequality, some sufficient conditions for existence of global positive solutions and stochastically ultimate boundedness are obtained, respectively. Also, the relationships between the stochastic permanence and extinction as well as asymptotic estimations of solutions are investigated by virtue ofV-function technique,M-matrix method, and Chebyshev's inequality. Finally, an example is given to illustrate the main results.

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31

Chow, Sy-Miin, Kevin J. Grimm, Guillaume Filteau, Conor V. Dolan, and John J. McArdle. "Regime-Switching Bivariate Dual Change Score Model." Multivariate Behavioral Research 48, no. 4 (July 2013): 463–502. http://dx.doi.org/10.1080/00273171.2013.787870.

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32

Elliott, Robert J., and Reza Bradrania. "Estimating a regime switching pairs trading model." Quantitative Finance 18, no. 5 (December 19, 2017): 877–83. http://dx.doi.org/10.1080/14697688.2017.1403035.

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33

Kim, Jaehee, and Sooyoung Cheon. "A Bayesian regime-switching time-series model." Journal of Time Series Analysis 31, no. 5 (June 23, 2010): 365–78. http://dx.doi.org/10.1111/j.1467-9892.2010.00670.x.

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34

朱, 萱. "Stock Loan Model under Volatility Regime Switching." Advances in Applied Mathematics 07, no. 05 (2018): 495–500. http://dx.doi.org/10.12677/aam.2018.75059.

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35

Evarest, Emmanuel, Fredrik Berntsson, Martin Singull, and Xiangfeng Yang. "Weather derivatives pricing using regime switching model." Monte Carlo Methods and Applications 24, no. 1 (March 1, 2018): 13–27. http://dx.doi.org/10.1515/mcma-2018-0002.

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Abstract In this study we discuss the pricing of weather derivatives whose underlying weather variable is temperature. The dynamics of temperature in this study follows a two state regime switching model with a heteroskedastic mean reverting process as the base regime and a shifted regime defined by Brownian motion with nonzero drift. We develop mathematical formulas for pricing futures and option contracts on heating degree days (HDDs), cooling degree days (CDDs) and cumulative average temperature (CAT) indices. The local volatility nature of the model in the base regime captures very well the dynamics of the underlying process, thus leading to a better pricing processes for temperature derivatives contracts written on various index variables. We use the Monte Carlo simulation method for pricing weather derivatives call option contracts.

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36

Chang, Yoosoon, Yongok Choi, and Joon Y. Park. "A new approach to model regime switching." Journal of Econometrics 196, no. 1 (January 2017): 127–43. http://dx.doi.org/10.1016/j.jeconom.2016.09.005.

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37

Hansen, Asbjørn T., and Rolf Poulsen. "A simple regime switching term structure model." Finance and Stochastics 4, no. 4 (August 2000): 409–29. http://dx.doi.org/10.1007/pl00013523.

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38

Shi, Yanlin, and Kin-Yip Ho. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model." Journal of Banking & Finance 61 (December 2015): S189—S204. http://dx.doi.org/10.1016/j.jbankfin.2015.08.025.

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39

Bojanic, Antonio N. "A Markov-Switching Model of Inflation in Bolivia." Economies 9, no. 1 (March 11, 2021): 37. http://dx.doi.org/10.3390/economies9010037.

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The Bolivian inflation process is analyzed utilizing a time-varying univariate and multivariate Markov-switching model (TMS). With monthly data and, beginning in the late 1930s, inflation is accurately described by a univariate TMS. The intercept for the high-inflation regime is significantly higher than for the low-inflation regime and the actual inflation rate mirrors the smoothing probabilities of the Markov process. Additionally, the predicted duration of each regime closely fits the periods when the country experienced low and inordinate high inflation rates. From a long-run perspective and utilizing a multivariate TMS, the results generally fall in line with what the quantity theory of money predicts. In the high-inflation regime, money growth increases inflation (almost) one-for-one, as classical economics contends. From a short-run perspective and in the high-inflation regime, inflation is almost exclusively explained by a negative output gap. In the low-inflation regime, lagged inflation is the most important determinant of inflation, in line with price stickiness expectations. Partitioning the sources of inflation demonstrate that, from a long-run perspective and in the high inflation regime, differences in inflation are mostly explained by GDP growth; in the low-inflation regime, money growth and velocity growth are the principal factors explaining the variance of inflation. From a short-run perspective, the output gap explains almost all regression variance in the high-inflation regime, and past inflation does the same during times of low inflation, though in both cases the R2 is low which precludes making definite statements about the sources of variability in inflation.

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40

Chen, Zhuo. "Development and inequality: Evidence from an endogenous switching regression without regime separation." Economics Letters 96, no. 2 (August 2007): 269–74. http://dx.doi.org/10.1016/j.econlet.2007.01.013.

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41

Osiewalski, Jacek, and Aleksander Welfe. "The price-wage mechanism: An endogenous switching model." European Economic Review 42, no. 2 (February 1998): 365–74. http://dx.doi.org/10.1016/s0014-2921(97)00083-4.

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42

FUTAMI, HIDENORI. "REGIME SWITCHING TERM STRUCTURE MODEL UNDER PARTIAL INFORMATION." International Journal of Theoretical and Applied Finance 14, no. 02 (March 2011): 265–94. http://dx.doi.org/10.1142/s0219024911006358.

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In this study, we attempt to calculate the term structure of the interest rate under partial information using a model in which the mean reversion level of the short rate changes in accordance with a regime shift in the economy. Under partial information, an investor observes the history of only the short rate and not a regime shift; hence, calculating the term structure of the interest rate is reduced to the problem of filtering the current regime from observable short rates. Therefore, we calculate it using the filtering theory that estimates a stochastic process from noisy observations, and investigate the effects of the regime shift under partial information on the market price of risk and the volatility of a bond price compared with those under full information, in which the regime is assumed to be observable. We find that, under partial information, the regime-shift risk converts into the diffusion risk. As a result, we find that both the market price of diffusion risk and the volatility of a bond price under partial information become stochastic, even though these under full information are constant.

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43

Deryugina, E., and A. Ponomarenko. "Money-based Inflation Risk Indicator:a regime Switching Model." Voprosy Ekonomiki, no. 9 (September 20, 2013): 119–27. http://dx.doi.org/10.32609/0042-8736-2013-9-119-127.

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We examine the relationship between probability of large fluctuations of inflation rate and monetary developments. With this purpose we identify the periods of high inflation regime for the cross-section of 15 transition economies. The obtained results are used to estimate the panel probit models that capture the relationship between probability of transition to a high inflation regime and money growth. We calculate the conditional probability of transition to a high inflation regime that may be regarded as a money-based inflation risk indicator for Russia.

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44

White, Richard, and Riccardo Rebonato. "A swaption volatility model using Markov regime switching." Journal of Computational Finance 12, no. 1 (September 2008): 79–114. http://dx.doi.org/10.21314/jcf.2008.182.

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45

Farmer, Roger E. A., Daniel F. Waggoner, and Tao Zha. "Indeterminacy in a forward-looking regime switching model." International Journal of Economic Theory 5, no. 1 (March 2009): 69–84. http://dx.doi.org/10.1111/j.1742-7363.2008.00094.x.

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46

Henriksen, Pål Nicolai. "Pricing barrier options by a regime switching model." Quantitative Finance 11, no. 8 (August 2011): 1221–31. http://dx.doi.org/10.1080/14697680903567160.

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47

Dai, M., Q. Zhang, and Q. J. Zhu. "Trend Following Trading under a Regime Switching Model." SIAM Journal on Financial Mathematics 1, no. 1 (January 2010): 780–810. http://dx.doi.org/10.1137/090770552.

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48

Lin, X. Sheldon, Ken Seng Tan, and Hailiang Yang. "Pricing Annuity Guarantees Under a Regime-Switching Model." North American Actuarial Journal 13, no. 3 (July 2009): 316–32. http://dx.doi.org/10.1080/10920277.2009.10597557.

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49

Ghysels, Eric. "TIME-SERIES MODEL WITH PERIODIC STOCHASTIC REGIME SWITCHING." Macroeconomic Dynamics 4, no. 4 (December 2000): 467–86. http://dx.doi.org/10.1017/s136510050001703x.

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We present a class of stochastic regime-switching models. The time-series models may have periodic transition probabilities and the drifts may be seasonal. In the latter case, the model exhibits seasonal dummy variation that may change with the regime. The processes entail nontrivial interactions between so-called business and seasonal cycles. We discuss the stochastic properties as well as their relationship with periodic ARMA processes. Estimation and testing are also discussed in detail.

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50

Bac, Catherine, Jean-MicheI Chevet, and Eric Ghysels. "TIME-SERIES MODEL WITH PERIODIC STOCHASTIC REGIME SWITCHING." Macroeconomic Dynamics 5, no. 1 (February 2001): 32–55. http://dx.doi.org/10.1017/s1365100501018028.

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This paper provides a historical chronology of economic activity in 16th- and 17th-century France that is based on wheat price series in Paris and Toulouse. A stochastic regime-switching model enables us to benchmark eras and summarize the salient features of a development difficult to appraise in all its complexity. A new class of Markov regime-switching time-series models is introduced to allow for nontrivial interdependencies between different types of cycles that make the economy grow at an unsteady rate. With a predominantly agricultural cycle, we uncover a strongly periodic Markov switching scheme for recorded wheat prices from the grain markets of Paris and Toulouse. Besides the periodic nature of the Markov chain, we also study whether a common factor determined the state of the economy in Paris and Toulouse or whether each series moved independently.

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