The Role of Coefficient Drivers of Time-Varying Coefficients in Estimating the Total Effects of a Regressor on the Dependent Variable of an Equation

Paravastu Ananta Venkata Bhattanatha Swamy, I-Lok Chang, Peter von zur Muehlen () and Amit Achameesing
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Paravastu Ananta Venkata Bhattanatha Swamy: Federal Reserve Board, 20th & Constitution Avenue, Washington, DC 20551, USA
I-Lok Chang: Department of Mathematics and Statistics, American University, 4400 Massachusetts Avenue, Washington, DC 20016, USA
Amit Achameesing: Business School, University of Stellenbosch, Carl Cronje Dr, Bellville, Cape Town 7530, South Africa

JRFM, 2022, vol. 15, issue 8, 1-14

Abstract: Typically, the explanatory variables included in a regression model, in conjunction with the omitted relevant regressors implied by the usual error term, have both direct and indirect effects on the dependent variable. Attempts to obtain their separate estimates have been plagued with simultaneity issues. To circumvent these problems, this paper defines their sum as “total effects”, develops a time-varying coefficients methodology for their estimation without simultaneity bias, and applies these techniques to estimate the total effects of commercial bank credit per-capita on real GDP per-capita in Mauritius. An innovation is the introduction of extraneous variables that act as “coefficient drivers” chosen on the basis of best predictive performance, as measured by the smallest value of Theil’s U-statistic we were able to locate in the estimation.

Keywords: total effects; bank credit; economic growth; direct effects; indirect effects; threshold regression; real GDP; coefficient drivers; Theil’s U statistic (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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