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Introduction to Markov-switching regression models using the mswitch command Gustavo Snchez StataCorp May 18, 2016 Aguascalientes, Mxico (StataCorp) Markov-switching regression in Stata May 18 1 / 1 Introduction to Markov-switching

  1. Introduction to Markov-switching regression models using the mswitch command Gustavo Sánchez StataCorp May 18, 2016 Aguascalientes, México (StataCorp) Markov-switching regression in Stata May 18 1 / 1

  2. Introduction to Markov-switching regression models using the mswitch command Gustavo Sánchez StataCorp May 18, 2016 Aguascalientes, México (StataCorp) Markov-switching regression in Stata May 18 2 / 1

  3. Outline When we use Markov-Switching Regression Models 1 Introductory concepts 2 Markov-Switching Dynamic Regression 3 Predictions State probabilities predictions Level predictions State expected durations Transition probabilities Markov-Switching AR Models 4 (StataCorp) Markov-switching regression in Stata May 18 3 / 1

  4. When we use Markov-Switching Regression Models The parameters of the data generating process (DGP) vary over a set of different unobserved states. We do not know the current state of the DGP , but we can estimate the probability of each possible state. (StataCorp) Markov-switching regression in Stata May 18 4 / 1

  5. Markov switching dynamic regression examples In Psychology: Manic depressive states (Hamaker et al. 2010). (StataCorp) Markov-switching regression in Stata May 18 5 / 1

  6. Markov switching dynamic regression examples In Economics: Asymmetrical behavior over GDP expansions and recessions (Hamilton 1989). Exchange rates (Engel and Hamilton 1990). Interest rates (García and Perron 1996). Stock returns (Kim et al. 1998). (StataCorp) Markov-switching regression in Stata May 18 6 / 1

  7. Markov switching dynamic regression examples In Epidemiology: Incidence rates of infectious disease in epidemic and nonepidemic states (Lu et al. 2010). Source: http://www.slideshare.net/meningitis/1620-mrf-marie-pierre-preziosi-06-nov (StataCorp) Markov-switching regression in Stata May 18 7 / 1

  8. Markov switching dynamic regression examples In Political Science: Democratic and Republican partisan states in the US congress (Jones et al 2010). State 1: Republicans are the dominant national party State 2: Democrats are the dominant national party (StataCorp) Markov-switching regression in Stata May 18 8 / 1

  9. When we use Markov-Switching Regression Models The time series in all those examples are characterized by DGPs with dynamics that are state dependent. States may be recessions and expansions, high/low volatility, depressive/non-depressive, epidemic/non-epidemic states, etc. Any of the parameters (beta estimates, sigma, AR components) may be different for each state. (StataCorp) Markov-switching regression in Stata May 18 9 / 1

  10. Different volatilities Mexican peso to Us dollar (StataCorp) Markov-switching regression in Stata May 18 10 / 1

  11. Different levels, volatilities and slopes- West Texas Oil Price (StataCorp) Markov-switching regression in Stata May 18 11 / 1

  12. Different AR structure - Interbank interest rate for Spain (StataCorp) Markov-switching regression in Stata May 18 12 / 1

  13. Introductory Concepts (StataCorp) Markov-switching regression in Stata May 18 13 / 1

  14. Markov-Switching Regression Models Models for time series that transition over a set of finite unobserved states. The time of transition between states and the duration in a particular state are both random. The transitions follow a Markov process. We can estimate state-dependent and state-independent parameters. (StataCorp) Markov-switching regression in Stata May 18 14 / 1

  15. Markov-Switching Regression Models Let’s then define a (first order) Markov Chain: Assume the states are defined by a random variable S t that takes the integer values 1, 2, ..., N. Then, the probability of the current state, j, only depends on the previous state: P ( S t = j | S t − 1 = i , S t − 2 = k , S t − 3 = w ... ) = P ( S t = j | S t − 1 = i ) = p ij (StataCorp) Markov-switching regression in Stata May 18 15 / 1

  16. Markov-Switching Regression Models Let’s define a simple constant only model with three states: y t = µ s t + ε t Where: µ s t = µ 1 s t = 1 if µ s t = µ 2 if s t = 2 µ s t = µ 3 if s t = 3 We do not know with certainty the current state, but we can estimate the probability of being in each state. We can also estimate the transition probabilities: p ij : probability of being in state j in the current period given that the process was in state i in the previous period. (StataCorp) Markov-switching regression in Stata May 18 16 / 1

  17. Transition probabilities, expected duration, tests We will then be interested in obtaining the matrix with the transition probabilities:   p 11 p 12 p 13 p 21 p 22 p 23   p 31 p 32 p 33 Where: p 11 + p 12 + p 13 = 1 p 21 + p 22 + p 23 = 1 p 31 + p 32 + p 33 = 1 We will also be interested in the expected duration for each state. We can perform tests for comparing parameters across states (StataCorp) Markov-switching regression in Stata May 18 17 / 1

  18. Markov-switching dynamic regression (StataCorp) Markov-switching regression in Stata May 18 18 / 1

  19. Markov-switching dynamic regression Allow states to switch according to a Markov process Allow for quick adjustments after a change of state. Often applied to high frequency data (monthly,weekly,etc.) (StataCorp) Markov-switching regression in Stata May 18 19 / 1

  20. Markov-switching dynamic regression The model can be written as: y t = µ s t + x t α + z t β s t + ǫ t Where: y t : Dependent variable x t : Vector of exog. variables with state invariant coefficients α z t : Vector of exog. variables with state-dependent coefficients β s ǫ st ~iid N ( 0 , σ 2 s ) We can also include lags of the dependent variable among the regressors (StataCorp) Markov-switching regression in Stata May 18 20 / 1

  21. Markov switching dynamic regression Example 1: Mexican peso to US dollar Period: 1989m1 - 2015m12 Source: Banco de México (StataCorp) Markov-switching regression in Stata May 18 21 / 1

  22. Markov switching dynamic regression with two states . mswitch dr D.tc,states(2) varswitch switch(,noconstant) constant nolog Performing EM optimization: Performing gradient-based optimization: Markov-switching dynamic regression Sample: 1989m2 - 2016m1 No. of obs = 324 Number of states = 2 AIC = -0.3199 Unconditional probabilities: transition HQIC = -0.2966 SBIC = -0.2615 Log likelihood = 56.82268 D.tc Coef. Std. Err. z P>|z| [95% Conf. Interval] D.tc _cons .0135642 .0020066 6.76 0.000 .0096313 .0174971 sigma1 .0160312 .0014595 .0134113 .0191628 sigma2 .3603955 .0158708 .330594 .3928835 p11 .9772127 .0196357 .883934 .9958759 p21 .0075981 .0057055 .0017346 .0326345 (StataCorp) Markov-switching regression in Stata May 18 22 / 1

  23. Markov switching dynamic regression with two states Probabilities of being in a given state . predict pr_state1 pr_state2, pr (StataCorp) Markov-switching regression in Stata May 18 23 / 1

  24. MSDR - Example 1: Probability of being in State 1 (StataCorp) Markov-switching regression in Stata May 18 24 / 1

  25. Markov switching dynamic regression with two states Performing EM optimization: Performing gradient-based optimization: Markov-switching dynamic regression Sample: 1989m2 - 2016m1 No. of obs = 324 Number of states = 2 AIC = -0.3199 Unconditional probabilities: transition HQIC = -0.2966 SBIC = -0.2615 Log likelihood = 56.82268 D.tc Coef. Std. Err. z P>|z| [95% Conf. Interval] D.tc _cons .0135642 .0020066 6.76 0.000 .0096313 .0174971 sigma1 .0160312 .0014595 .0134113 .0191628 sigma2 .3603955 .0158708 .330594 .3928835 p11 .9772127 .0196357 .883934 .9958759 p21 .0075981 .0057055 .0017346 .0326345 (StataCorp) Markov-switching regression in Stata May 18 25 / 1

  26. MSDR - Example 1: Probability of being in State 2 (StataCorp) Markov-switching regression in Stata May 18 26 / 1

  27. Markov switching dynamic regression with two states Performing EM optimization: Performing gradient-based optimization: Markov-switching dynamic regression Sample: 1989m2 - 2016m1 No. of obs = 324 Number of states = 2 AIC = -0.3199 Unconditional probabilities: transition HQIC = -0.2966 SBIC = -0.2615 Log likelihood = 56.82268 D.tc Coef. Std. Err. z P>|z| [95% Conf. Interval] D.tc _cons .0135642 .0020066 6.76 0.000 .0096313 .0174971 sigma1 .0160312 .0014595 .0134113 .0191628 sigma2 .3603955 .0158708 .330594 .3928835 p11 .9772127 .0196357 .883934 .9958759 p21 .0075981 .0057055 .0017346 .0326345 (StataCorp) Markov-switching regression in Stata May 18 27 / 1

  28. Markov switching dynamic regression Example 2: West Texas Oil Price Period: 1974q1 - 2016q1 Source: Federal Reserve Bank of St. Louis (StataCorp) Markov-switching regression in Stata May 18 28 / 1

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