A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains

Arbeitspapier

This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and tends to outperform the existing methods for approximating multivariate processes over a wide range of the parameter space, especially for highly persistent vector autoregressions with roots near the unit circle.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2013-5

Classification and Topics

• Classification: Wirtschaft ; Statistical Simulation Methods: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Mathematical Methods; Programming Models; Mathematical and Simulation Modeling: General ; General Aggregative Models: Neoclassical ; Business Fluctuations; Cycles ; Fiscal Policy
• Subject: Markov chain ; vector autoregressive processes ; numerical methods ; moment matching ; non-linear stochastic dynamic models state space discretization ; stochastic growth model ; fiscal policy

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Gospodinov, Nikolay ; Lkhagvasuren, Damba
• Event: Veröffentlichung
• (who): Federal Reserve Bank of Atlanta
• (where): Atlanta, GA
• (when): 2013

Further information

• Handle: http://hdl.handle.net/10419/101039
• Last update: 10.03.2025, 11:42 AM CET