Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86022
Authors: 
Koopman, Siem Jan
Bos, Charles S.
Year of Publication: 
2002
Series/Report no.: 
Tinbergen Institute Discussion Paper 02-113/4
Abstract: 
The linear Gaussian state space model for which the common variance istreated as a stochastic time-varying variable is considered for themodelling of economic time series. The focus of this paper is on thesimultaneous estimation of parameters related to the stochasticprocesses of the mean part and the variance part of the model. Theestimation method is based on maximum likelihood and it requires thesubsequent uses of the Kalman filter to treat the mean part andsampling techniques to treat the variance part. This approach leads tothe evaluation of the exact likelihood function of the model subject tosimulation error. The standard asymptotic properties of maximumlikelihood estimators apply as a result. A Monte Carlo study is carriedout to investigate the small-sample properties of the estimationprocedure. We present two illustrations which are concerned with themodelling and forecasting of two U.S. macroeconomic time series:inflation and industrial production.
Subjects: 
Autoregressive integrated moving average
Importance sampling
Industrial production
Inflation
Kalman filer
Monte Carlo simulation
Simulation smoothing
State space
Stochastic volatility
Unobserved components time series.
JEL: 
C15
C32
C51
E23
E31
Document Type: 
Working Paper

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