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Testing the Asymmetric Effects of Financial Conditions in South Africa: A Nonlinear Vector Autoregression Approach

Mehmet Balcilar (), Kirsten Thompson, Rangan Gupta () and Renee van Eyden ()
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Kirsten Thompson: Department of Economics, University of Pretoria Author-Email: kirsten.thompson@lantic.net

No 15-11, Working Papers from Eastern Mediterranean University, Department of Economics

Abstract: The negative consequences of financial instability for the world economy during the recent financial crisis have highlighted the need for a better understanding of financial conditions. We use a financial conditions index (FCI) for South Africa previously constructed from 16 financial variables to test whether the South African economy responds in a nonlinear and asymmetric way to unexpected changes in financial conditions. To this end, we make use of a nonlinear logistic smooth transition vector autoregressive model (LSTVAR), which allows for a smooth evolution of the economy, governed by a chosen switching variable between periods of high and low financial volatility. We find that the South African economy responds nonlinearly to financial shocks, and that manufacturing output growth and Treasury Bill rates are more affected by financial shocks during upswings. Inflation responds significantly more to financial changes during recessions.

Keywords: Financial conditions index; nonlinear vector autoregression; LSTVAR; asymmetry (search for similar items in EconPapers)
JEL-codes: C32 G01 E44 E32 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2014
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http://repec.economics.emu.edu.tr/RePEc/emu/wpaper/15-11.pdf First version, 2014 (application/pdf)

Related works:
Journal Article: Testing the asymmetric effects of financial conditions in South Africa: A nonlinear vector autoregression approach (2016) Downloads
Working Paper: Testing the Asymmetric Effects of Financial Conditions in South Africa: A Nonlinear Vector Autoregression Approach (2014) Downloads
Working Paper: Testing the Asymmetric Effects of Financial Conditions in South Africa: A Nonlinear Vector Autoregression Approach (2014)
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