Inequality Constrained State Space Models

Qian, Hang (2015): Inequality Constrained State Space Models.

Abstract

The standard Kalman filter cannot handle inequality constraints imposed on the state variables, as state truncation induces a non-linear and non-Gaussian model. We propose a Rao-Blackwellised particle filter with the optimal importance function for forward filtering and the likelihood function evaluation. The particle filter effectively enforces the state constraints when the Kalman filter violates them. We find substantial Monte Carlo variance reduction by using the optimal importance function and Rao-Blackwellisation, in which the Gaussian linear sub-structure is exploited at both the cross-sectional and temporal levels.

• Item Type: MPRA Paper
• Language: English
• Keywords: Rao-Blackwellisation, Kalman filter, Particle filter, Sequential Monte Carlo
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods

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