General--to--Specific Reductions of Vector Autoregressive Processes
Hans-Martin Krolzig ()
No 164, Computing in Economics and Finance 2001 from Society for Computational Economics
Unrestricted reduced form vector autoregressive (VAR) models have become a dominant research strategy in empirical macroeconomics since Sims (1980) critique of traditional macroeconometric modeling. They are however subjected to the curse of dimensionality. In this paper we propose general-to-specific reductions of VAR models and consider computer-automated model selection algorithms embodied in PcGets (see Krolzig and Hendry, 2000) for doing so. Starting from the unrestricted VAR, standard testing procedures eliminate statistically-insignificant variables, with diagnostic tests checking the validity of reductions, ensuring a congruent final selection. Since jointly selecting and diagnostic testing eludes theoretical analysis, we evaluate the proposed strategy by simulation. The Monte Carlo experiments show that PcGets recovers the DGP specification from a large unrestricted VAR model with size and power close to commencing from the DGP itself. The application of the proposed reduction strategy to a US monetary system demonstrates the feasibility of PcGets for the analysis of large macroeconomic data sets.
Keywords: Econometric methodology; Model selection; Vector autoregression; Data mining. (search for similar items in EconPapers)
JEL-codes: C51 C32 E52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
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