Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
Likelihood Evaluation of Models with Occasionally Binding Constraints
Applied researchers interested in estimating key parameters of DSGE models face an array of choices regarding numerical solution and estimation methods. We focus on the likelihood evaluation of models with occasionally binding constraints. We document how solution approximation errors and likelihood misspecification, related to the treatment of measurement errors, can interact and compound each other.
Cite this item
Pablo Cuba-Borda & Luca Guerrieri & Matteo Iacoviello & Molin Zhong, Likelihood Evaluation of Models with Occasionally Binding Constraints, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2019-028, 19 Apr 2019.
- 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
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
Keywords: Measurement error ; Solution error ; Occasionally binding constraints ; Particle filter
This item with handle RePEc:fip:fedgfe:2019-28
is also listed on EconPapers
For corrections, contact Ryan Wolfslayer ()