Zivot Wang Koopman_200305_State Space Modeling in Macroeconomics Finance using SsfPack S FinMetrics.pdf

State Space Modeling in Macroeconomics and Finance
Using SsfPack for S+FinMetrics
Eric Zivot, Jiahui Wang and Siem Jan Koopman ∗
University of Washington, Seattle
Ronin Capital LLC, Chicago
Free University, Amsterdam
August 4, 2002
This version: May 29, 2003
Abstract
This paper surveys mon state space models used in macroeconomics and fi-
nance and shows how to specify and estimate these models using the SsfPack algorithms
implemented in the S-PLUS module S+FinMetrics. Examples include recursive regres-
sion models, time varying parameter models, exact autoregressive moving average models
and calculation of the Beveridge-Nelson position, ponents models,
stochastic volatility models, and term structure models.
1 Introduction
The first version of SsfPack1 appeared in 1998 and was developed further during the years that
the last author was working with Jim Durbin on their 2001 textbook on state space methods.
The fact that SsfPack functions are now a part of the S-PLUS software is partly due to Jim
Durbin. He convinced Doug Martin that SsfPack would be very beneficial to S-PLUS. Indeed
the persuasive arguments of Jim Durbin has initiated the development of SsfPack functions
for S-PLUS as part of the S+FinMetrics module. It is therefore an honor for us, the developers
of SsfPack for S+FinMetrics, to contribute to this volume with the presentation of various
applications in economics and finance that require the use of SsfPack for S+FinMetrics in
empirical research.
State space modeling in macroeconomics and finance has e widespread over the
last decade. Textbook treatments of state space models are given in Harvey (1989), Har-
vey (1993), Hamilton (1994), West & Harrison (1997), Kim & Nelson (1999), Shumway &
∗Financial support from the herlands Academy of Arts and Sciences, and from the Gary Waterman
Distinguished Scholar Fund at the University of Washington is gratefully acknowledged.
Emails: ******@, jwang@, ko