Constructive Data Mining: Modeling Argentine Broad Money Demand
Year: 2008
Type: article
Abstract: This paper assesses the empirical merits of PcGets and Autometrics--two recent algorithms for computer-automated model selection--using them to improve upon Kamin and Ericsson's (1993) model of Argentine broad money demand. The selected model is an economically sensible and statistically satisfactory error correction model, in which cointegration between money, inflation, the interest rate, and exchange rate depreciation depends on the inclusion of a 'ratchet' variable that captures irreversible effects of inflation. Short-run dynamics differ markedly from the long run. Algorithmically based model selection complements opportunities for the researcher to contribute value added in the empirical analysis.
Authors: Neil R. Ericsson, Steven Kamin
Institutions: Federal Reserve, Federal Reserve Board of Governors
Language: English
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W1528418244