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A0367
Title: Adaptive state space models with applications to the business cycle and financial stress Authors:  Davide Delle Monache - Bank of Italy (Italy) [presenting]
Fabrizio Venditti - Queen Mary University of London (United Kingdom)
Ivan Petrella - Warwick Business School (United Kingdom)
Abstract: The estimation of state-space models with time-varying parameters typically implies the use of computationally intensive methods. Moreover, when volatility evolves stochastically, the model ceases to be conditionally Gaussian and requires nonlinear filtering techniques. We model parameters' variation in a Gaussian state-space model by letting their dynamics to be driven by the score of the predictive likelihood. In this setup, conditionally on past data, the model remains Gaussian and the likelihood function can be evaluated using the Kalman filter. We derive a new set of recursions running in parallel with the standard Kalman filter recursions that allows us estimate simultaneously the unobserved state vector and the time-varying parameters by maximum likelihood. Given that a variety of time series models have a state space representation, the proposed methodology is of wide interest in econometrics and applied macroeconomics. Specifically, the usefulness of the methodology is illustrated in two applications: the former aims to improve GDP measurement based on alternative noisy measures, in the letter we construct an index of financial conditions that can be used to nowcast GDP in real time.