Adaptive state space models with applications to the business cycle and financial stress

Ivan Petrella, Fabrizio Venditti and Davide Delle Monache

No 11599, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: In this paper we develop a new theoretical framework for the analysis of state space models with time-varying parameters. We let the driver of the time variation be the score of the predictive likelihood and derive a new filter that allows us to estimate simultaneously the state vector and the time-varying parameters. In this setup the model remains Gaussian, the likelihood function can be evaluated using the Kalman filter and the model parameters can be estimated via maximum likelihood, without requiring the use of computationally intensive methods. Using a Monte Carlo exercise we show that the proposed method works well for a number of different data generating processes. We also present two empirical applications. In the former we improve the measurement of GDP growth by combining alternative noisy measures, in the latter we construct an index of financial stress and evaluate its usefulness in nowcasting GDP growth in real time. Given that a variety of time series models have a state space representation, the proposed methodology is of wide interest in econometrics and statistics.

Keywords: State space models; Time-varying parameters; Score-driven models; Business cycle; Financial stress (search for similar items in EconPapers)
JEL-codes: C22 C32 C51 C53 E31 (search for similar items in EconPapers)
Date: 2016-11
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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