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FRB Atlanta Working Paper
A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains
AbstractThis 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.
Cite this item
Nikolay Gospodinov & Damba Lkhagvasuren, A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains, Federal Reserve Bank of Atlanta, FRB Atlanta Working Paper 2013-05, 01 Sep 2013.
JEL Classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- 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
- C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
- E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy
Keywords: Markov chain; vector autoregressive processes; numerical methods; moment matching; non-linear stochastic dynamic models state space discretization; stochastic growth model; fiscal policy
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