Generalized Autoregressive Method of Moments
Siem Jan Koopman (),
Andre Lucas () and
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Marcin Zamojski: VU University Amsterdam, the Netherlands
No 15-138/III, Tinbergen Institute Discussion Papers from Tinbergen Institute
We extend the generalized method of moments to a setting where a subset of the parameters may vary over time with unknown dynamics. We approximate the true unknown dynamics by an updating scheme that is driven by the influence function of the conditional criterion function at time t. The updates ensure a local improvement of the conditional criterion function at each time in expectation. In our framework, time-varying parameters are a function of past data; it leads to a computationally efficient method since it does not require simulation-based methods for estimation. The approach can be applied to a wide range of moment conditions that are used in economics and finance. We provide an illustration for a capital asset pricing model with time-varying risk aversion.
Keywords: dynamic models; time-varying parameters; generalized method of moments; non-linearity (search for similar items in EconPapers)
JEL-codes: C10 C22 C32 C51 (search for similar items in EconPapers)
Date: 2015-12-24, Revised 2018-07-06
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20150138
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