Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-22T20:10:03.363Z Has data issue: false hasContentIssue false


Published online by Cambridge University Press:  09 June 2017

David Ubilava*
University of Sydney
Address correspondence to: David Ubilava, School of Economics, The University of Sydney, Merewether Building, NSW 2006, Australia; e-mail:


The recent global financial crisis and the subsequent recession have revitalized the discussion on causal interactions between financial and economic sectors. In this study, I apply the financial stress and the national activity indices–respectively developed by Federal Reserve Banks of Kansas City and Chicago–to investigate the impact of financial uncertainty on an overall economic performance. I examine nonlinear dynamics in a vector smooth transition autoregressive framework, and illustrate regime-dependent asymmetries in the financial and economic indices using the generalized impulse-response functions. The results reveal more amplified dynamics during the stressed conditions. I further evaluate benefits of nonlinear modeling in an out-of-sample setting. The forecasting exercise brings out the important advantages that nonlinear modeling provides in the identification of the causal effect of financial instability on overall economic performance.

Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


This paper greatly benefited from many useful comments and suggestions from Edward Nelson, as well as those of two anonymous referees.



Anderson, H. M., Athanasopoulos, G. and Vahid, F. (2007) Nonlinear autoregressive leading indicator models of output in G-7 countries. Journal of Applied Econometrics 22 (1), 6387.Google Scholar
Anderson, H. M. and Vahid, F. (1998) Testing multiple equation systems for common nonlinear components. Journal of Econometrics 84 (1), 136.Google Scholar
Arestis, P., Demetriades, P. O. and Luintel, K. B. (2001) Financial development and economic growth: The role of stock markets. Journal of Money Credit and Banking 33 (1), 1641.Google Scholar
Ashley, R., Granger, C. W. and Schmalensee, R. (1980) Advertising and aggregate consumption: An analysis of causality. Econometrica 48 (5), 11491167.Google Scholar
Bacon, D. and Watts, D. (1971) Estimating the transition between two intersecting straight lines. Biometrika 58 (3), 525534.Google Scholar
Brunnermeier, M. K. and Sannikov, Y. (2014) A macroeconomic model with a financial sector. American Economic Review 104 (2), 379421.Google Scholar
Burns, A. F. and Mitchell, W. C. (1946) Measuring Business Cycles. National Bureau of Economic Research.Google Scholar
Calderón, C. and Liu, L. (2003) The direction of causality between financial development and economic growth. Journal of Development Economics 72 (1), 321334.Google Scholar
Camacho, M. (2004) Vector smooth transition regression models for US GDP and the composite index of leading indicators. Journal of Forecasting 23 (3), 173196.Google Scholar
Chan, K. and Tong, H. (1986) On estimating thresholds in autoregressive models. Journal of Time Series Analysis 7 (3), 179190.Google Scholar
Clark, T. and McCracken, M. (2001) Tests of equal forecast accuracy and encompassing for nested models. Journal of Econometrics 105 (1), 85110.Google Scholar
Clark, T. and West, K. (2007) Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics 138 (1), 291311.Google Scholar
Davies, R. (1977) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 64 (2), 247254.Google Scholar
Davies, R. (1987) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 74 (1), 3343.Google Scholar
Davig, T. and Hakkio, C. S. (2010) What is the effect of financial stress on economic activity?. Federal Reserve Bank of Kansas City, Economic Review 95 (2), 3562.Google Scholar
De Gregorio, J. and Guidotti, P. E. (1995) Financial development and economic growth. World Development 23 (3), 433448.Google Scholar
Diebold, F. and Mariano, R. (1995) Comparing predictive accuracy. Journal of Business & Economic Statistics 13 (3), 253263.Google Scholar
Diebold, F. X. and Rudebusch, G. D. (1991) Forecasting output with the composite leading index: A real-time analysis. Journal of the American Statistical Association 86 (415), 603610.Google Scholar
Diebold, F. X. and Rudebusch, G. D. (1996) Measuring business cycles: A modern perspective. Review of Economics and Statistics 78 (1), 6777.Google Scholar
Ferrara, L., Marcellino, M. and Mogliani, M. (2015) Macroeconomic forecasting during the great recession: The return of non-linearity?. International Journal of Forecasting 31 (3), 664679.Google Scholar
Fisher, I. (1933) The debt-deflation theory of great depressions. Econometrica 1 (4), 337357.Google Scholar
Franses, P. H. and van Dijk, D. (2005) The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production. International Journal of Forecasting 21 (1), 87102.Google Scholar
Gertler, M. and Kiyotaki, N. (2010) Financial intermediation and credit policy in business cycle analysis. In Friedman, B. M. and Woodford, M. (eds.), Handbook of Monetary Economics, vol. 3, pp. 547599. Amsterdam, The Netherlands: Elsevier.Google Scholar
Giacomini, R. and Rossi, B. (2013) Forecasting in macroeconomics. In Hashimzade, N. and Thornton, M. A. (eds.), Handbook of Research Methods and Applications on Empirical Macroeconomics. Cheltenham, UK: Edward Elgar Publishing.Google Scholar
Gilchrist, S., Yankov, V., and Zakrajşek, E. (2009) Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets. Journal of Monetary Economics 56 (4), 471493.Google Scholar
Granger, C. and Teräsvirta, T. (1993) Modelling Nonlinear Economic Relationships. New York, USA: Oxford University Press.Google Scholar
Granger, C. W. (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37 (3), 424438.Google Scholar
Hakkio, C. S. and Keeton, W. R. (2009) Financial stress: What is it, how can it be measured, and why does it matter?. Federal Reserve Bank of Kansas City, Economic Review 94 (2), 550.Google Scholar
Hubrich, K. and Teräsvirta, T. (2013) Thresholds and Smooth Transitions in Vector Autoregressive Models. Research paper 13, CREATES.Google Scholar
Hubrich, K. and Tetlow, R. (2015) Financial stress and economic dynamics: The transmission of crises. Journal of Monetary Economics 70, 100115.Google Scholar
Hyndman, R. J. (1995) Highest-density forecast regions for nonlinear and non-normal time series models. Journal of Forecasting 4 (5), 431441.Google Scholar
Hyndman, R. J. (1996) Computing and graphing highest density regions. American Statistician 50 (2), 120126.Google Scholar
Jermann, U. and Quadrini, V. (2012). Macroeconomic effects of financial shocks. American Economic Review 102 (1), 238271.Google Scholar
Jones, P. M. and Enders, W. (2016) The asymmetric effects of uncertainty on macroeconomic activity. Macroeconomic Dynamics forthcoming 1–28.Google Scholar
Keynes, J. M. (1936) The General Theory of Employment, Interest and Money. London: Macmillan.Google Scholar
King, R. G. and Levine, R. (1993) Finance and growth: Schumpeter might be right. Quarterly Journal of Economics 108 (3), 717737.Google Scholar
Koop, G., Pesaran, M. and Potter, S. (1996) Impulse response analysis in nonlinear multivariate models. Journal of Econometrics 74 (1), 119147.Google Scholar
Lahiri, K. and Wang, J. G. (1994) Predicting cyclical turning points with leading index in a markov switching model. Journal of Forecasting 13 (3), 245263.Google Scholar
Levine, R. (1997) Financial development and economic growth: Views and agenda. Journal of Economic Literature 35 (2), 688726.Google Scholar
Liu, Z., Waggoner, D. F. and Zha, T. (2011) Sources of macroeconomic fluctuations: A regime-switching DSGE approach. Quantitative Economics 2 (2), 251301.Google Scholar
Luukkonen, R., Saikkonen, P. and Teräsvirta, T. (1988) Testing linearity against smooth transition autoregressive models. Biometrika 75 (3), 491499.Google Scholar
Marcellino, M., Stock, J. H. and Watson, M. W. (2006) A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. Journal of Econometrics 135 (1), 499526.Google Scholar
McCracken, M. W. (2007) Asymptotics for out of sample tests of Granger causality. Journal of Econometrics 140 (2), 719752.Google Scholar
Mittnik, S. and Semmler, W. (2013) The real consequences of financial stress. Journal of Economic Dynamics and Control 37 (8), 14791499.Google Scholar
Rothman, P., van Dijk, D. and Franses, P. H. (2001) Multivariate STAR analysis of money–output relationship. Macroeconomic Dynamics 5 (4), 506532.Google Scholar
Samuelson, P. A. (1966) Science and stocks. Newsweek, September 19, 1992.Google Scholar
Schleer, F. and Semmler, W. (2015) Financial sector and output dynamics in the euro area: Non-linearities reconsidered. Journal of Macroeconomics 46, 235263.Google Scholar
Schumpeter, J. A. (1934) The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. Cambridge, MA: Harvard University Press. Translated by Redvers Opie.Google Scholar
Skalin, J. and Teräsvirta, T. (2002) Modeling asymmetries and moving equilibria in unemployment rates. Macroeconomic Dynamics 6 (2), 202241.Google Scholar
Stock, J. H. and Watson, M. W. (1989) New Indexes of Coincident and Leading Economic Indicators. NBER macroeconomics annual 4, 351409.Google Scholar
Stock, J. H. and Watson, M. W. (1993) A procedure for predicting recessions with leading indicators: Econometric issues and recent experience. In Stock, J. H. and Watson, M. W. (eds.), Business Cycles, Indicators and Forecasting, pp. 95156. Chicago, IL: University of Chicago Press.Google Scholar
Stock, J. H. and Watson, M. W. (1999a) Business cycle fluctuations in US macroeconomic time series. In Taylor, J. B. and Woodford, M. (eds.), Handbook of Macroeconomics, vol. 1, pp. 364. Amsterdam, The Netherlands: Elsevier.Google Scholar
Stock, J. H. and Watson, M. W. (1999b) Forecasting inflation. Journal of Monetary Economics 44 (2), 293335.Google Scholar
Stock, J. H. and Watson, M. W. (2003) Forecasting output and inflation: The role of asset prices. Journal of Economic Literature 41 (3), 788829.Google Scholar
Teräsvirta, T. (1994) Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association 89 (425), 208218.Google Scholar
Teräsvirta, T. (1995) Modelling nonlinearity in US gross national product 1889–1987. Empirical Economics 20 (4), 577597.Google Scholar
Teräsvirta, T. and Anderson, H. (1992) Characterizing nonlinearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics 7 (S1), S119S136.Google Scholar
Teräsvirta, T., Tjøstheim, D. and Granger, C. W. J. (2010). Modelling Nonlinear Economic Time Series. Advanced Texts in Econometrics. New York: Oxford University Press.Google Scholar
Teräsvirta, T. and Yang, Y. (2014a) Linearity and misspecification tests for vector smooth transition regression models. Research paper 4, CREATES.Google Scholar
Teräsvirta, T. and Yang, Y. (2014b) Specification, estimation and evaluation of vector smooth transition autoregressive models with applications. Research paper 8, CREATES.Google Scholar
Tong, H. and Lim, K. S. (1980) Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological) 42 (3), 245292.Google Scholar
van Dijk, D. and Franses, P. (1999) Modeling multiple regimes in the business cycle. Macroeconomic Dynamics 3 (3), 311340.Google Scholar
Weise, C. (1999). The asymmetric effects of monetary policy: A nonlinear vector autoregression approach. Journal of Money, Credit and Banking 31 (1), 85108.Google Scholar