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1

Krolzig, Hans-Martin. Markov-Switching Vector Autoregressions. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-51684-9.

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2

Hamilton, James D., and Baldev Raj, eds. Advances in Markov-Switching Models. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-642-51182-0.

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3

Timmermann, Allan. Moments of Markov switching models. London: London School of Economics, Financial Markets Group, 1999.

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4

Gable, Jeff. Analytical derivatives of Markov switching models. Ottawa, Ont: Bank of Canada, 1995.

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5

Gable, Jeff. Analytical derivatives for Markov switching models. Ottawa, Ont: Publications Distribution, Bank of Canada, 1995.

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6

Chang-Jin, Kim. Dynamic linear models with Markov-switching. Toronto, Ont: York University, Dept. of Economics, 1991.

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7

Chauvet, Marcelle. Markov switching in disaggregate unemployment rates. [New York, N.Y.]: Federal Reserve Bank of New York, 2001.

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8

Canada, Bank of. Switching between chartists and fundamentalists: A Markov Regime-Switching approach. Ottawa: Bank of Canada, 1996.

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9

Vigfusson, Robert. Switching between chartists and fundamentalists: A Markov regime-switching approach. Ottawa: Bank of Canada, 1996.

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10

Chang-Jin, Kim. Estimation of Markov regime-switching regression models with endogenous switching. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2003.

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11

Engel, Charles. Can the Markov switching model forecast exchange rates? Cambridge, MA: National Bureau of Economic Research, 1992.

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12

Rubio-Ramírez, Juan Francisco. Markov-Switching structural vector autoregressions: Theory and application. [Atlanta, Ga.]: Federal Reserve Bank of Atlanta, 2005.

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13

Owyang, Michael T. The economic performance of cities: A Markov-switching approach. St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2006.

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14

Dueker, Michael. Can markov switching models predict excess foreign exchange returns? [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2001.

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15

Gregoriou, Greg N., and Razvan Pascalau, eds. Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration. London: Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230295216.

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16

Sims, Christopher A. Methods for inference in large multiple-equation Markov-switching models. Atlanta, Ga.]: Federal Reserve Bank of Atlanta, 2006.

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17

Celso, Brunetti, and Board of Governors of the Federal Reserve System (U.S.), eds. Markov switching GARCH models of currency turmoil in Southeast Asia. Washington, D.C: Federal Reserve Board, 2007.

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18

Clements, Michael P. Business cycles asymmetries: Characterisation and testing based on Markov-switching autoregressions. Coventry: University of Warwick, Department of Economics, 1998.

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19

Kontolemis, Zenon G. Analysis of the U.S. business cycle with a vector-Markov-switching model. [Washington, D.C.]: International Monetary Fund, European I Department, 1999.

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20

Yoshioka, Shinji. Is Indonesia a high inflation country?: A Markov regime switching model approach. Jakarta, Indonesia: National Development Planning Agency, 2003.

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21

Farmer, Roger E. A. Indeterminacy in a forward looking regime switching model. Cambridge, Mass: National Bureau of Economic Research, 2006.

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22

Farmer, Roger E. A. Indeterminacy in a forward-looking regime-switching model. Atlanta, Ga.]: Federal Reserve Bank of Atlanta, 2006.

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23

Guidolin, Massimo. Who tames the celtic tiger?: Portfolio implications from a multivariate Markov switching model. St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2006.

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24

Dueker, Michael. Multivariate Markov switching with weighted regime determination: Giving France more weight than Finland. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2008.

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25

Clements, Michael P. A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP. Coventry: University of Warwick Department of Economics, 1997.

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26

Abel, Andrew B. Exact solutions for expected rates of return under Markov regime switching: Implications for the equity premium puzzle. Cambridge, MA: National Bureau of Economic Research, 1992.

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27

Francis, Neville. Monetary policy in a Markov-switching VECM: Implications for the cost of disinflation and the price puzzle. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2003.

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28

French, Mark W. A nonlinear look at trend mfp grwoth and the business cycle: Result from a hybrid kalman/markov switching model. Washington, D.C: Federal Reserve Board, 2005.

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29

Chang-Jin, Kim. Unobserved-component time-series models with Markov-switching heteroskedasticity: Changes in regime and the link between inflation rates and inflation uncertainty. Toronto: York University, 1992.

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30

Gong, Fangxiong. Testing under non-standard conditions in frequency domain: With applications to Markov regime switching models of exchange rates and the federal funds rate. [New York, N.Y.]: Federal Reserve Bank of New York, 1997.

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31

Gong, Fangxiong. Testing under non-standard conditions in frequency domain: With applications to Markov regime switching models of exchange rates and the federal funds rate. New York, N.Y: Federal Reserve Bank of New York, 1997.

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32

United States. National Aeronautics and Space Administration., ed. MMPP Traffic Generator for the testing of the SCAR 2 Fast Packet Switch. [Washington, DC: National Aeronautics and Space Administration, 1995.

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33

R, Nelson Charles, ed. State-space models with regime switching: Classical and Gibbs-sampling approaches with applications. Cambridge, Mass: MIT Press, 1999.

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34

Communications/Information Systems Planning and Critical Path Planning Service., ed. Network switching. Boston, MA (89 Broad St., Boston 02110): Yankee Group, 1986.

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35

inc, International Resource Development, ed. Store & forward voice switching. Norwalk, Conn., U.S.A. (6 Prowitt St., Norwalk 06855): IRD, 1985.

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36

Rickwood, Sarah. European pharmaceuticals: Switching to OTC status. London: Financial Times Business Information, 1994.

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37

inc, International Resource Development, ed. Packet switching services & equipment. Norwalk, Conn., U.S.A. (6 Prowitt St., Norwalk 06855): International Resource Development, 1985.

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38

inc, International Resource Development, ed. Computers & switching equipment for value-added carriers, worldwide. New Canaan, Conn., U.S.A. (P.O. Box 1716, New Canaan 06840): International Resource Development, 1991.

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39

Rush, Thomas. Telephone bypass. Norwalk, Conn: Business Communications Co., 1989.

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40

Frost & Sullivan., ed. European Rx to OTC switching markets: Achievements, challenges, and the future. Mountain View, CA: Frost & Sullivan, 1994.

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41

United States. International Trade Administration. Office of Telecommunications., ed. A Competitive assessment of the U.S. digital central office switch industry. Washington, D.C: Dept. of Commerce, International Trade Administration, 1986.

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42

inc, International Resource Development, ed. X.25 interface hardware markets. Norwalk, Conn., U.S.A (6 Prowitt St., Norwalk 06855): International Resource Development, 1985.

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43

inc, International Resource Development, ed. Secure LANs & red switches. New Canaan, Conn., U.S.A. (21 Locust Ave., Suite 1C, New Canaan 06840): International Resource Development, 1988.

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44

Brauer, Samuel. Markets and technologies for switchable ferroelectric, electrochromic, and optical materials. Norwalk, CT: Business Communications Co., 2002.

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45

Chiappa, Silvia. Explicit-Duration Markov Switching Models. Now Publishers, 2014.

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46

Frühwirth-Schnatter, Sylvia. Finite Mixture and Markov Switching Models. Springer New York, 2010.

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47

Finite Mixture and Markov Switching Models. Springer New York, 2006. http://dx.doi.org/10.1007/978-0-387-35768-3.

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48

Finite Mixture and Markov Switching Models. Springer, 2006.

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49

Finite Mixture and Markov Switching Models. Springer London, Limited, 2006.

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50

Bao, Yun, Carl Chiarella, and Boda Kang. Particle Filters for Markov-Switching Stochastic Volatility Models. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.9.

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Abstract:
This chapter proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. It proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method that demonstrates the ability to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemented in order to analyze a real-time series, namely, the foreign exchange rate between the Australian dollar and the South Korean won.
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