Computer Programs and Data to Accompany
State-Space Models with Regime-Switching:
Classical and Gibbs-Sampling Approaches with Applications
All programs are written and Copyright ©1998 by Chang-Jin
Kim.
Please refer to http://econ.korea.ac.kr/~cjkim/SSMARKOV.htm
for conditions of use.
If you have any questions, please e-mail Chang-Jin Kim either at
cjkim@korea.ac.kr or changjin@u.washington.edu.
Chapter 3 - State-Space Models and
the Kalman Filter
Chapter 4 - Markov-Switching Models
Chapter 5 - State-Space Models with Markov-Switching
Chapter 6 - State-Space Models with Heteroskedastic
Disturbances
Chapter 7 - An Introduction to Bayesian Inference
and Gibbs Sampling
Chapter 8 - State-Space Models and Gibbs Sampling
Chapter 9 - Markov-Switching Models and Gibbs
Sampling
Chapter 10 - State-Space Models with Markov-Switching
and Gibbs Sampling
Chapter 11 - Gibbs Sampling and Parameter
Uncertainty: Testing for Mean Reversion in Heteroskedastic Data
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Chapter 3 - State-Space Models and the
Kalman Filter
Application #1: A Decomposition of Real GDP and the Unemployment
Rate into Stochastic Trend and Transitory Components
Application #2: An Application of the Time-Varying Parameter Model
to Modeling Changing Conditional Variance
Application #3: Stock & Watson's (1991) Dynamic Factor Model
of the Coincident Economic Indicators
Programs:
TVP.OPT - A Time-Varying
Parameter Model of U.S. Monetary Growth Function: Based on Kim and
Nelson (1989)
UC_UNI.OPT - A Univariate
Unobserved Components Model of U.S. Real GDP: Based on Clark (1987)
UC_BI.OPT - A Bivariate
Unobserved Components Model of U.S. Real GDP and the Unemployment
Rate: Based on Clark (1989)
S&W.OPT - A
Dynamic Factor Model of Four Coincident Economic Indicators: An
Experimental Coincident Index: Based on Stock and Watson (1991)
Data:
TVP.PRN - see program for
details
GDP4795.PRN - Real
GDP
LHUR.PRN - Unemployment
Rate
S&W.PRN - see program
for details
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Chapter 4 - Markov-Switching Models
Application #1: Hamilton's (1989) Markov-Switching Model of Business
Fluctuations
Application #2: A Unit Root in a Three-State Markov-Switching Model
of the Real Interest Rate
Application #3: A Three-State Markov-Switching Model of Stock Returns
Programs:
HMT4_KIM.OPT -
An AR(4) Model with a Markov-Switching Mean (2-state): Based on
Hamilton's (1989) Filter and Kim's (1994) Smoothing
HMT4_DMY.OPT -
An AR(4) Model with a Markov-Switching Mean (2-state): Based on
Hamilton's (1989) Filter and Kim's (1994) Smoothing (Dummy variables
are incorporated for the mean growth rates)
INTR_S3.OPT - A
Three-State Markov-Switching Mean-Variance Model of the Real Interst
Rate: Based on Garcia and Perron (1996)
STCK_V3.OPT - A
Three-State Markov-Switching Variance Model of Stock Returns: Based
on Kim, Nelson, and Startz (1997)
HMT_TVP.OPT - An
AR(4) Model with a Markov-Switching Mean (2-state) and Time-Varying
Transition Probabilities: Based on Filardo (1994)
Data:
GDP4795.PRN - Real
GDP
INT_CPUQ.PRN - see
program for details
EW_EXCS.PRN - Equal-Weighted
Excess Returns
FILARDO.PRN -see program
for details
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Chapter 5 - State-Space Models with Markov-Switching
Application #1: Sources of Monetary Growth Uncertainty and Economic
Activity
Application #2: Friedman's Plucking Model of Business Fluctuations
and Implied Business Cycle Asymmetry
Application #3: A Dynamic Factor Model with Markov-Switching: Business
Cycle Turning Points and a New Coincident Index
Programs:
KIM_JE0.OPT - not
available at this time
KIM_JE1.OPT - A
State-Space Representation of Lam's (1990) Gerneralized Hamilton
Model and Kim's (1994) Filter(easier version)
TVPMRKF.OPT - Time-Varying-Parameter
Model with Markov-Switching Heteroskedasticity: Based on Kim (1993)
TVPM_JNT.OPT -
Time-Varying-Parameter Model with Markov-Switching Heteroskedasticity:
Based on Kim (1993); A Joint Estimation of the Output Equation and
the Monetary Growth Equation
SW_MS.OPT - Dynamic
Factor Model with Markov-Switching (A New Coicident Index): An Application
of Kim's (1994) Algorithm by Kim and Yoo (1995)
Data:
KIM_JE.PRN - see program
for details
TVPMRKF.PRN - see program
for details
SW_MS.PRN - see program
for details
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Chapter 6 - State-Space Models with Heteroskedastic
Disturbances
Application #1: The Link Between the Inflation Rate and Inflation
Uncertainty
Application #2: Transient Fads and the Crash of '87 in the U.S.
Stock Market
Programs:
TVPGRCH.OPT - A
Time-Varying-Parameter Model with GARCH(1,1) Disturbances: Based
on Harvey et. al. (1992)
INF_FNL.OPT - A
State-Space Model with Markov-Switching Heteroskedasticity: A Model
of the Inflation Rate (Large and Infrequent Permanent Shocks to
Inflation): Based on Kim (1993)
STCK.OPT - A Fad Model
of Stock Returns(Transient Fads): based on Kim and M. Kim (1996)
GARCH.OPT - GARCH(1,1)
Model of Stock Returns
STCK_V2.OPT - A
Two-State Markov-Switching Model of Stock Returns
Data:
TVPGRCH.PRN - see program
for details
GD4795.PRN - GDP Deflator
RSP2692.PRN - see
program for details
TURNER3.PRN - see
program for details
<Return to chapter list
Chapter 7 - An Introduction to Bayesian Inference
and Gibbs Sampling
Programs:
GBS_AR4.PRG - A
Gibbs-Sampling Approach to a Linear AR(4) Model
MLE_AR4.PRG - A
Maximum Likelihood Estiamtion of a Linear AR(4) Model
GBS_ATO.PRG - A
Gibbs-Sampling Approach to a Regression Model with AR(1) Disturbances
MLE_ATO.PRG - A
Maximum Likelihood Estiamtion of a Regression Model with AR(1) Disturbances
Data:
GDP4795.PRN - Real
GDP
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Chapter 8 - State-Space Models and Gibbs
Sampling
Application #1: A Gibbs-Sampling Approach to a Linear Dynamic Factor
Model and a New Coincident Index
Program:
SW_GIBS.PRG - A
Gibbs-Sampling Approach to Stock and Watson's Dynamic Factor Model
of 4 Coincident Economic Indicators: A New Experimental Coincident
Index
Data:
FULLDTA.PRN - see program
for details
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Chapter 9 - Markov-Switching Models and Gibbs
Sampling
Application #1: A Three-State Markov-Switching Variance Model of
Stock Returns
Application #2: A Three-State Markov-Switching Mean-Variance Model
of the Real Interest Rate
Program:
GIBS_MS0.PRG -
A Gibbs-Sampling Approach to an AR(0) Model with a Two-State Markov-Switching
Mean, Homoskedastic Disturbances: Multi-Move Gibbs Sampling [For
Real GDP Data]
GIBS_S3.PRG - A
Gibbs-Sampling Approach to a Three-State Markov-Switching Variance
Model of Stock Returns: Multimove Gibbs-Sampling
G_INT_S3.PRG -
A Gibbs-Sampling Approach to a Three-State Markov-Switching Mean-Variance
AR(2) Model of the Real Interest Rate: Multimove Gibbs-Sampling
Data:
GDP4795.PRN - Real
GDP
CRSPD.PRN - see program
for details
INT_CPUQ.PRN - see
program for details
Return to chapter list
Chapter 10 - State-Space Models with Markov-Switching
and Gibbs Sampling
Application #1: Business Cycle Turning Points and a New Coincident
Index
Application #2: Business Cycle Duration Dependence within a Dynamic
Factor Model
Application #3: An Unobserved Component Model of the Long-Run U.S./U.K.
Real Exchange Rate with Heteroskedasticity
Programs:
SWMSGIBS.PRG -
A Gibbs-Sampling Approach to a Dynamic Factor Model with Markov-Switching:
Based on Kim and Nelson (1998)
Data:
FULLDTA.PRN - see program
for details
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Chapter 11 -Gibbs Sampling and Parameter
Uncertainty: Testing for Mean Reversion in Heteroskedastic Data
VR_HT_OR.PRG -
VR Test Based on Gibbs-Sampling-Augmented Randomization, original
returns: Based on Kim, Nelson, and Startz (1998)
VR_HT_ST.PRG -
VR Test Based on Gibbs-Sampling-Augmented Randomization, standardized
returns: Based on Kim, Nelson, and Startz (1998)
Data:
CRSPD.PRN - see program
for details
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