Extended Switching Regression Models
Theory and Applications
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Switching regression models are models that allow parameters of the conditional distribution, such as the mean and variance, to vary according to a finite-valued stochastic process with states or regimes. The regime changes aim at capturing changes in the underlying financial and economic mechanism through the observed time series. These models have proven very useful in modeling economic and financial time series. In this book, we generalized this modeling approach. We consider models that allow occasional, recurrent and independent switches in disjoint subsets of the parameters of the condit...
Switching regression models are models that allow
parameters of the conditional distribution, such as
the mean and variance, to vary according to a finite-
valued stochastic process with states or regimes.
The regime changes aim at capturing changes in the
underlying financial and economic mechanism through
the observed time series. These models have proven
very useful in modeling economic and financial time
series.
In this book, we generalized this modeling approach.
We consider models that allow occasional, recurrent
and independent switches in disjoint subsets of the
parameters of the conditional distribution. These
are determined by the realization of several latent
state variables. The state variable probabilities
can be constant or change over time. We call these
extended switching regression models. We develop an
EM algorithm for estimation, give conditions for
consistency and asymptotic normality and apply our
models to combine conditional volatility forecasts
of several exchange rates. We also consider the
penalized likelihood method for selecting the
correct latent structure of these models.
parameters of the conditional distribution, such as
the mean and variance, to vary according to a finite-
valued stochastic process with states or regimes.
The regime changes aim at capturing changes in the
underlying financial and economic mechanism through
the observed time series. These models have proven
very useful in modeling economic and financial time
series.
In this book, we generalized this modeling approach.
We consider models that allow occasional, recurrent
and independent switches in disjoint subsets of the
parameters of the conditional distribution. These
are determined by the realization of several latent
state variables. The state variable probabilities
can be constant or change over time. We call these
extended switching regression models. We develop an
EM algorithm for estimation, give conditions for
consistency and asymptotic normality and apply our
models to combine conditional volatility forecasts
of several exchange rates. We also consider the
penalized likelihood method for selecting the
correct latent structure of these models.
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