全球智库信息集成服务系统(G2TT): DP11599 Adaptive state space models with applications to the business cycle and financial stress

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来源类型	Discussion paper
规范类型	论文
来源ID	DP11599
	DP11599 Adaptive state space models with applications to the business cycle and financial stress
	Ivan Petrella; Fabrizio Venditti; Davide Delle Monache
发表日期	2016-11-03
出版年	2016
语种	英语
摘要	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.
主题	Monetary Economics and Fluctuations
关键词	State space models Time-varying parameters Score-driven models Business cycle Financial stress
URL	https://cepr.org/publications/dp11599
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/540413
推荐引用方式 GB/T 7714	Ivan Petrella,Fabrizio Venditti,Davide Delle Monache. DP11599 Adaptive state space models with applications to the business cycle and financial stress. 2016.