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Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Endogenous regime switching model.'
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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.
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.
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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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.
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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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.
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.
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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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.
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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Hayashi, Fumio, and Junko Koeda. "Exiting from quantitative easing." Quantitative Economics 10, no. 3 (2019): 1069–107. http://dx.doi.org/10.3982/qe1058.
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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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.
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.
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.
Dissertations / Theses on the topic "Endogenous regime switching model":
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Devilliers, Esther. "Modélisation micro-économétrique des choix de pratiques de production et des utilisations d'intrants chimiques des agriculteurs : une approche par les fonctions de production latentes." Thesis, Rennes, Agrocampus Ouest, 2021. http://www.theses.fr/2021NSARE058.
La notion d’itinéraire technique est une notion agronomique qui nous permet d’appréhender l’imbrication entre les rendements objectifs et les niveaux d’utilisation d’intrants associés. Dès lors, on peut admettre qu’à différents types d’itinéraires techniques correspondent différentes fonctions de production. Modéliser ces différentes fonctions est une des clés pour mieux comprendre la dépendance de certaines pratiques culturales aux pesticides et de ce fait constitue un enjeu majeur pour concevoir les futures politiques publiques.Intégrer cette notion d’itinéraire technique nécessite de tenir compte de l’interdépendance entre les choix de ces pratiques, leur rendement et les utilisations associées. Pour ce faire, on considère des modèles de changement de régime endogène qui permettent de contrôler des biais de sélection. Lorsque ces pratiques sont inobservées, on définit la séquence de choix comme processus Markovien.Le modèle résultant nous permet de recouvrir les pratiques culturales, leurs niveaux de rendement et d’utilisation d’intrants ainsi que la dynamique de choix des dites pratiques. Lorsque ces pratiques sont observées, on décide de considérer un modèle primal afin de pouvoir vérifier le rôle différencié des pesticides et évaluer l’effet des politiques publiques conjointement sur les rendements et les niveaux d’utilisation d’intrants chimiques.En bref, cette thèse vise à donner des outils pour évaluer au mieux les effets des politiques agro-environnementales sur les utilisations de pesticides, les rendements et mes choix de pratiques culturales des agriculteurs Cropping management practices is an agronomic notion grasping the interdependence between targeted yield and input use levels. Subsequently, one can legitimately assume that different cropping management practices are associated to different production functions. To better understand pesticide dependence – a key point to encourage more sustainable practices – one have to consider modelling cropping management practices specific production functions.Because of the inherent interdependence between those practices and their associeted yield and input use levels, we need to consider endogenous regime switching models.When unobserved, the sequence of cropping management practices choices is considered as a Markovian process. From this modelling framework we can derive the cropping management choices, their dynamics, their associated yield and input use levels. When observed, we consider primal production functions to see how yield responds differently to input uses based on the different cropping management practices. Thus, we can assess jointly the effect of a public policy on input use and yield levels.In a nutshell, in this PhD we are aiming at giving some tools to evaluate the differentiated effect of agri-environmental public policies on production choies and on the associated yield and input use levels
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Koutchade, Obafèmi-Philippe. "Hétérogénéité inobservée et solutions en coin dans les modèles micro-économétriques de choix de production multiculture." Thesis, Rennes, Agrocampus Ouest, 2018. http://www.theses.fr/2018NSARE048/document.
Dans cette thèse, nous nous intéressons aux questions de l’hétérogénéité inobservée et des solutions en coin dans les modèles de choix d’assolements. Pour répondre à ces questions, nous nous appuyons sur un modèle de choix de production multicultures avec choix d’assolement de forme NMNL, dont nous proposons des extensions. Ces extensions conduisent à des problèmes spécifiques d’estimation, auxquels nous apportons des solutions. La question de l’hétérogénéité inobservée est traitée en considérant une spécification à paramètres aléatoires. Ceci nous permet de tenir compte des effets de l’hétérogénéité inobservée sur l’ensemble des paramètres du modèle. Nous montrons que les versions stochastiques de l’algorithme EM sont particulièrement adaptées pour estimer ce type de modèle.Nos résultats d’estimation et de simulation montrent que les agriculteurs réagissent de façon hétérogène aux incitations économiques et que ne pas tenir compte de cette hétérogénéité peut conduire à des effets simulés de politiques publique biaisés.Pour tenir compte des solutions en coin dans les choix d’assolement, nous proposons une modélisation basée sur les modèles à changement de régime endogène avec coûts fixes associés aux régimes. Contrairement aux approches basées sur des systèmes de régression censurées, notre modèle est cohérent d’un point de vue micro-économique. Nos résultats montrent que les coûts fixes associés aux régimes jouent un rôle important dans le choix des agriculteurs de produire ou non certaines cultures et qu’ils constituent, à court terme, un déterminant important des c In this thesis, we are interested in questions of unobserved heterogeneity and corner solutions in acreage choice models. To answer these questions, we rely on a NMNL acreage share multi-crop models, of which we propose extensions. These extensions lead to specific estimation problems, to which we provide solutions.The question of unobserved heterogeneity is dealt with by considering a random parameter specification. This allows us to take into account the effects of the unobserved heterogeneity on all the parameters of the model. We show that the stochastic versions of the EM algorithm are particularly suitable for estimating this type of modelOur estimation and simulation results show that farmers react heterogeneously to economic incentives and that ignoring this heterogeneity can lead to biased simulated effects of public policies.In order to take account of the corner solutions in acreage choices, we propose modelling based on endogenous regime switching models with regime fixed costs. Unlike approaches based on censored regression systems, our model is “fully” consistent from a micro-economic viewpoint. Our results show that the regime fixed costs play an important role in farmers’ choice to produce or not some crops and they are, in the short term, an important determinant of acreage choices
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Check, Adam. "REGIME SWITCHING AND THE MONETARY ECONOMY." Thesis, University of Oregon, 2016. http://hdl.handle.net/1794/20531.
For the empirical macroeconomist, accounting for nonlinearities in data series by using regime switching techniques has a long history. Over the past 25 years, there have been tremendous advances in both the estimation of regime switching and the incorporation of regime switching into macroeconomic models. In this dissertation, I apply techniques from this literature to study two topics that are of particular relevance to the conduct of monetary policy: asset bubbles and the Federal Reserve’s policy reaction function. My first chapter utilizes a recently developed Markov-Switching model in order to test for asset bubbles in simulated data. I find that this flexible model is able to detect asset bubbles in about 75% of simulations. In my second and third chapters, I focus on the Federal Reserve’s policy reaction function. My second chapter advances the literature in two important directions. First, it uses meeting- based timing to more properly account for the target Federal Funds rate; second, it allows for the inclusion of up to 14 economic variables. I find that the long-run inflation response coefficient is larger than had been found in previous studies, and that increasing the number of economic variables that can enter the model improves both in-sample fit and out-of-sample forecasting ability. In my third chapter, I introduce a new econometric model that allows for Markov-Switching, but can also remove variables from the model, or enforce a restriction that there is no regime switching. My findings indicate that the majority of coefficients in the Federal Reserve’s policy reaction function have not changed over time.
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Shami, Roland G. (Roland George) 1960. "Bayesian analysis of a structural model with regime switching." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/9277.
Sola, Martin. "Essays on speculative attacks on fixed exchange rate regimes, speculative bubbles and endogenous switching regime estimation." Thesis, University of Southampton, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316309.
Cheung, Ka-chun. "Optimal asset allocation problems under the discrete-time regime-switching model." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31311234.
Yang, Zijian. "Application of Regime Switching Model to Equity Market and Portfolio Selection." Thesis, University of Essex, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517406.
Cheung, Ka-chun, and 張家俊. "Optimal asset allocation problems under the discrete-time regime-switching model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31311234.
Grimm, Stefanie [Verfasser]. "An Interest-Rate Model with Regime-Switching Mean-Reversion Level / Stefanie Grimm." München : Verlag Dr. Hut, 2017. http://d-nb.info/1135596794/34.
Stockel, Jakob, and Niklas Skantz. "Regime shifts in the Swedish housing market - A Markov-switching model analysis." Thesis, KTH, Fastigheter och byggande, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190178.
Problem statement: Accurate and reliable forecasts of trends in the housing market can be useful information for market participants as well as policy makers. This information may be useful to minimize risk related to market uncertainty. Since the burst of the housing bubble in the early 1990s the price level of single-family houses has risen sharply in Sweden. The Swedish housing market has experienced an unusually long period of high growth rates in transaction prices which has opened up for discussions about the risk of another housing bubble. Business and property cycles have shown to contain asymmetries, which linear models are unable to pick up and therefore inappropriate to analyze cycles. Approach: Therefore, this study uses non-linear models which are able to pick up the asymmetries. The estimated models are variations of the Markov-switching regression model, i.e. the Markov-switching autoregressive (MS-AR) model and the Markov-switching dynamic regression (MS-DR) model. Results: Our ndings show that the MS-AR(4) model allowing for varying variance across regimes estimated using the growth rate of FASTPI produce superior forecasts over other MSAR models as well as variations of the MS-DR model. The average expected duration to remain in a positive growth regime is between 6.3 and 7.3 years and the average expected duration to remain in a negative growth regime is between 1.2 to 2.5 years. Conclusion: The next regime shift in the Swedish housing market is projected to occur between 2018 and 2019, counting the contraction period in 2012 as the most recent negative regime. Our ndings support other studies ndings which indicate that the longer the market has remained in one state, the greater is the risk for a regime shift. Problemformulering: Noggranna och tillforlitliga prognoser om utvecklingen pa bostadsmarknaden kan vara anvandbar information for marknadsaktorer samt beslutsfattare. Denna information kan vara anvandbar for att minimera risken relaterad till osakerheten pa marknaden. Sen bostadsbubblan sprack i borjan av 1990-talet har prisnivan for smahus okat kraftigt i Sverige. Den svenska bostadsmarknaden har upplevt en ovanligt lang period av hog tillvaxt i transaktionspriser som har oppnat upp for diskussioner om risken for en ny bostadsbubbla. Konjunkturoch fastighetscykler har visat sig innehalla asymmetrier som linjara modeller inte kan uppfanga och darfor visat sig vara olampliga for att analysera cykler. Tillvagagangssatt: Darfor anvander den har studien icke-linjara modeller som kan uppfanga dessa asymmetrier. De skattade modellerna ar variationer av Hamiltons Markov-switchingmodell, dvs. en autoregressiv Markov-switchingmodell (MS-AR) och en dynamisk Markov-switchingmodell (MS-DR). Resultat: Resultatet visar att MS-AR(4)-modellen som tar hansyn till varierande varians over regimerna estimerad med tillvaxten av FASTPI producerar overlagsna prognoser jamfort med andra MS-AR-modeller samt variationer av MS-DR-modellen. Den genomsnittliga forvantade varaktigheten att benna sig i en positiv regim ar mellan 6,3 och 7,3 ar och den genomsnittliga forvantade varaktigheten att benna sig i en negativ regim ar mellan 1,2 till 2,5 ar. Slutsats: Nasta regimskifte pa den svenska bostadsmarknaden beraknas ske mellan 2018 och 2019, antaget att nedgangen under 2012 ar den senaste negativa regimen. Resultatet stodjer tidigare studier, som tyder pa att ju langre marknaden har varit i ett tillstand, desto storre ar risken for ett regimskifte.
Books on the topic "Endogenous regime switching model":
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Chang-Jin, Kim. Estimation of Markov regime-switching regression models with endogenous switching. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2003.
Chib, Siddhartha. Non-Markovian regime switching with endogenous states and time-varying state strengths. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2004.
Yoshioka, Shinji. Is Indonesia a high inflation country?: A Markov regime switching model approach. Jakarta, Indonesia: National Development Planning Agency, 2003.
Akpalu, Wisdom, and Xu Zhang. Fast-food consumption and child body mass index in China: Application of an endogenous switching regression model. UNU-WIDER, 2014. http://dx.doi.org/10.35188/unu-wider/2014/860-5.
Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.
Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.
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Woldu, Gabriel Temesgen. Do fiscal regimes matter for fiscal sustainability in South Africa? A Markov-switching approach. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/920-4.
This paper empirically examines South Africa’s fiscal sustainability through a Markov-switching 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.
Book chapters on the topic "Endogenous regime switching model":
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Ferri, Piero, and Edward Greenberg. "A Regime Switching Model." In Lecture Notes in Economics and Mathematical Systems, 120–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-662-00831-7_8.
Valdez, Adrian Roy L., and Tiziano Vargiolu. "Optimal Portfolio in a Regime-switching Model." In Seminar on Stochastic Analysis, Random Fields and Applications VII, 435–49. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0545-2_22.
Yamaka, Woraphon, Payap Tarkhamtham, Paravee Maneejuk, and Songsak Sriboonchitta. "A Regime Switching Skew-Distribution Model of Contagion." In Structural Changes and their Econometric Modeling, 439–50. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04263-9_34.
Schlösser, Anna. "Regime-Switching Extension of the NIG Factor Copula Model." In Lecture Notes in Economics and Mathematical Systems, 185–226. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15609-0_8.
Liu, Xuanhui, Li-Ai Cui, and Fangguo Ren. "Conditional Ruin Probability with a Markov Regime Switching Model." In Advances in Intelligent and Soft Computing, 295–300. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22833-9_35.
Chevallier, Julien, and Stéphane Goutte. "Statistical Method to Estimate a Regime-Switching Lévy Model." In Springer Proceedings in Mathematics & Statistics, 381–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13881-7_42.
Savku, E., and G. W. Weber. "A Regime-Switching Model with Applications to Finance: Markovian and Non-Markovian Cases." In Dynamic Economic Problems with Regime Switches, 287–309. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54576-5_13.
Jalen, Luka, and Rogemar S. Mamon. "Parameter Estimation in a Regime-Switching Model with Non-normal Noise." In Hidden Markov Models in Finance, 241–61. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7442-6_11.
Florescu, Ionut, and Forrest Levin. "Estimation Procedure for Regime Switching Stochastic Volatility Model and Its Applications." In Handbook of High-Frequency Trading and Modeling in Finance, 107–36. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118593486.ch5.
Conference papers on the topic "Endogenous regime switching model":
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Hoesli, Martin, and Jean-Christophe Delfim. "A Robust Regime-Switching Desmoothing Model." In 25th Annual European Real Estate Society Conference. European Real Estate Society, 2018. http://dx.doi.org/10.15396/eres2018_123.
Wan, Shuping. "Stochastic Differential Portfolio Games with Regime Switching Model." In 2006 Sixth International Conference on Hybrid Intelligent Systems. IEEE, 2006. http://dx.doi.org/10.1109/his.2006.264893.
Gomes, Adam D., and Andrew J. Heunis. "Unconstrained Hedging within a Regime-Switching Market Model." In 2019 Sixth Indian Control Conference (ICC). IEEE, 2019. http://dx.doi.org/10.1109/icc47138.2019.9123156.
Chen, Zhiying, Xuanhua Peng, and Yongkui Li. "Optimal Portfolio Choice under Hidden Regime Switching Model." In Fifth Symposium of Risk Analysis and Risk Management in Western China (WRARM 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/wrarm-17.2017.43.
Siu, Chi Chung. "Option Pricing with a Regime-Switching Lévy Model." In Proceedings of the KIER–TMU International Workshop on Financial Engineering 2010. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814366038_0008.
Wan, Shuping. "Stochastic Control in Optimal Portfolio with Regime Switching Model." In 2006 9th International Conference on Control, Automation, Robotics and Vision. IEEE, 2006. http://dx.doi.org/10.1109/icarcv.2006.345087.
YANG, H., and G. YIN. "RUIN PROBABILITY FOR A MODEL UNDER MARKOVIAN SWITCHING REGIME." In Proceedings of a Workshop. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702715_0013.
Li, Ping, and Yin Libo. "A Copula-based Regime-switching Model for Rainbow Option Pricing." In 2012 Fifth International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2012. http://dx.doi.org/10.1109/bife.2012.148.
Lenhard, Gregor, and Dietmar Maringer. "State-ANFIS: A Generalized Regime-Switching Model for Financial Modeling." In 2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr). IEEE, 2022. http://dx.doi.org/10.1109/cifer52523.2022.9776208.
Yuan, Quan, Baojun Bian, and Guiqiu Yuan. "Binomial Tree Method for American Options in a Regime Switching Model." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.2312.
Reports on the topic "Endogenous regime switching model":
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Kim, Chang-Jin, Jeremy M. Piger, and Richard Startz. Estimation of Markov Regime-Switching Regression Models with Endogenous Switching. Federal Reserve Bank of St. Louis, 2003. http://dx.doi.org/10.20955/wp.2003.015.
Benigno, Gianluca, Andrew Foerster, Christopher Otrok, and Alessandro Rebucci. Estimating Macroeconomic Models of Financial Crises: An Endogenous Regime-Switching Approach. Cambridge, MA: National Bureau of Economic Research, April 2020. http://dx.doi.org/10.3386/w26935.
Chib, Siddhartha, and Michael J. Dueker. Non-Markovian Regime Switching with Endogenous States and Time-Varying State Strengths. Federal Reserve Bank of St. Louis, 2004. http://dx.doi.org/10.20955/wp.2004.030.
Farmer, Roger E., Daniel Waggoner, and Tao Zha. Indeterminacy in a Forward Looking Regime Switching Model. Cambridge, MA: National Bureau of Economic Research, September 2006. http://dx.doi.org/10.3386/w12540.
Misas A., Martha, and María Teresa Ramírez-Giraldo. Depressions in the colombian economic growth during the XX century: a Markov switching regime model. Bogotá, Colombia: Banco de la República, June 2005. http://dx.doi.org/10.32468/be.340.
Lo, Ming Chien, and Jeremy M. Piger. Is the Response of Output to Monetary Policy Asymmetric? Evidence from a Regime-Switching Coefficients Model. Federal Reserve Bank of St. Louis, 2001. http://dx.doi.org/10.20955/wp.2001.022.
Carrasco, Alex, and David Florián Hoyle. External Shocks and FX Intervention Policy in Emerging Economies. Inter-American Development Bank, August 2021. http://dx.doi.org/10.18235/0003457.
This paper discusses the role of sterilized foreign exchange (FX) interventions as a monetary policy instrument for emerging market economies in response to external shocks. We develop a model for a commodity-exporting small open economy in which FX intervention is considered as a balance sheet policy induced by a financial friction in the form of an agency problem between banks and their creditors. The severity of banks agency problem depends directly on a bank-level measure of currency mismatch. Endogenous deviations from the standard UIP condition arise at equilibrium. In this context, FX interventions moderate the response of financial and macroeconomic variables to external shocks by leaning against the wind with respect to real exchange rate pressures. Our quantitative results indicate that, conditional on external shocks, the FX intervention policy successfully reduces credit, investment, and output volatility, along with substantial welfare gains when compared to a free-floating exchange rate regime. Finally, we explore distinct generalizations of the model that eliminate the presence of endogenous UIP deviations. In those cases, FX intervention operations are considerably less effective for the aggregate equilibrium.
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Méndez-Vizcaíno, Juan C., and Nicolás Moreno-Arias. A Global Shock with Idiosyncratic Pains: State-Dependent Debt Limits for LATAM during the COVID-19 pandemic. Banco de la República, October 2021. http://dx.doi.org/10.32468/be.1175.
Fiscal sustainability in five of the largest Latin American economies is examined before and after the COVID-19 pandemic. For this purpose, the DSGE model in Bi(2012) and Hürtgen (2020) is used to estimate the Fiscal Limits and Fiscal Spaces for Peru, Chile, Mexico, Colombia, and Brazil. These estimates advance the empirical literature for Latin America on fiscal sustainability by offering new calculations stemming from a structural framework with alluring novel features: government default on the intensive margin; dynamic Laffer curves; utility-based stochastic discount factor; and a Markov-Switching process for public transfers with an explosive regime. The most notable additions to the existing literature for Latin America are the estimations of entire distributions of public debt limits for various default probabilities and that said limits critically hinge on both current and future states. Results obtained indicate notorious contractions of Fiscal Spaces among all countries during the pandemic, but the sizes of these were very heterogeneous. Countries that in 2019 had positive spaces and got closer to negative spaces in 2020, have since seen deterioration of their sovereign debt ratings or outlooks. Colombia was the only country to lose its positive Fiscal Space and investment grade, thereby joining Brazil, the previously sole member of both groups
