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

Author

Listed:
  • Delle Monache, Davide
  • Petrella, Ivan
  • Venditti, Fabrizio

Abstract

In this paper we develop a new 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 applied macroeconomics.

Suggested Citation

  • Delle Monache, Davide & Petrella, Ivan & Venditti, Fabrizio, 2016. "Adaptive state space models with applications to the business cycle and financial stress," CEPR Discussion Papers 11599, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:11599
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Simone Auer, 2017. "A Financial Conditions Index for the CEE economies," Temi di discussione (Economic working papers) 1145, Bank of Italy, Economic Research and International Relations Area.
    2. Giovanni Angelini & Paolo Gorgi, 2018. "DSGE Models with Observation-Driven Time-Varying parameters," Tinbergen Institute Discussion Papers 18-030/III, Tinbergen Institute.
    3. repec:eee:ecolet:v:171:y:2018:i:c:p:169-171 is not listed on IDEAS
    4. Giuseppe Buccheri & Giacomo Bormetti & Fulvio Corsi & Fabrizio Lillo, 2018. "A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics," Papers 1803.04894, arXiv.org, revised Mar 2019.
    5. Francisco (F.) Blasques & Paolo Gorgi & Siem Jan (S.J.) Koopman, 2018. "Missing Observations in Observation-Driven Time Series Models," Tinbergen Institute Discussion Papers 18-013/III, Tinbergen Institute.

    More about this item

    Keywords

    Business cycle; financial stress.; score-driven models; State space models; time-varying parameters;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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