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Another Look at Swedish Business Cycles, 1861-1988

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  • J. SKALIN
  • T. TERÄSVIRTA

Abstract

The linearity of nine long Swedish macroeconomic time series, whose business cycle properties were discussed by Englund, Persson, and Svensson (1992), is tested and rejected for all but two. Non-linear (STAR) models are estimated, and their properties are investigated. Business cycle frequency variation does not seem to be constant over time for all series; it is difficult to find a 'Swedish business cycle'. Pairwise Granger non-causality tests are adapted to the STAR case, and non-causality is tested. The results point at strong temporal interactions and indicate that the functional form (linear or STAR) strongly affects the outcome of these tests.
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Suggested Citation

  • J. Skalin & T. Teräsvirta, 1996. "Another Look at Swedish Business Cycles, 1861-1988," SFB 373 Discussion Papers 1996,96, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  • Handle: RePEc:zbw:sfb373:199696
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    Cited by:

    1. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    2. Juan Carlos Cuestas & Estefanía Mourelle, 2009. "Inflation persistence and asymmetries: evidence for African countries," Working Papers 2009/2, Nottingham Trent University, Nottingham Business School, Economics Division.
    3. Dijk, Dick van & Franses, Philip Hans, 1999. "Modeling Multiple Regimes in the Business Cycle," Macroeconomic Dynamics, Cambridge University Press, vol. 3(03), pages 311-340, September.
    4. Pablo Mejia-Reyes & Denise Osborn & Marianne Sensier, 2010. "Modelling real exchange rate effects on output performance in Latin America," Applied Economics, Taylor & Francis Journals, vol. 42(19), pages 2491-2503.
    5. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2012. "The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US," Working Papers 201226, University of Pretoria, Department of Economics.
    6. José Cancelo & Estefanía Mourelle, 2005. "Modeling Cyclical Asymmetries in European Imports," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 11(2), pages 135-147, May.
    7. Andreas Röthig & Carl Chiarella, 2007. "Investigating nonlinear speculation in cattle, corn, and hog futures markets using logistic smooth transition regression models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(8), pages 719-737, August.
    8. Prasad Bal, Debi & Narayan Rath, Badri, 2015. "Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India," Energy Economics, Elsevier, vol. 51(C), pages 149-156.
    9. Skalin, Joakim & Ter svirta, Timo, 2002. "Modeling Asymmetries And Moving Equilibria In Unemployment Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 6(02), pages 202-241, April.
    10. M-Ali Sotoudeh & Andrew C. Worthington, 2016. "A comparative analysis of monetary responses to global oil price changes: net oil producing vs. net oil consuming countries," International Economics and Economic Policy, Springer, vol. 13(4), pages 623-640, October.
    11. Tsai, I-Chun & Peng, Chien-Wen, 2016. "Linear and nonlinear dynamic relationships between housing prices and trading volumes," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 172-184.
    12. Q. Farooq Akram & Øyvind Eitrheim & Lucio Sarno, 2005. "Non-linear dynamics in output, real exchange rates and real money balances: Norway, 1830-2003," Working Paper 2005/2, Norges Bank.
    13. Mark J.Holmes, 2002. "Are there non linearities in US: Latin American real exchange behavior," Estudios de Economia, University of Chile, Department of Economics, vol. 29(2 Year 20), pages 177-190, December.
    14. Pavlidis Efthymios G & Paya Ivan & Peel David A, 2010. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-40, May.
    15. Kim, Sei-wan & Lee, Kihoon & Nam, Kiseok, 2010. "The relationship between CO2 emissions and economic growth: The case of Korea with nonlinear evidence," Energy Policy, Elsevier, vol. 38(10), pages 5938-5946, October.
    16. Tiwari, Aviral Kumar & Dar, Arif Billah & Bhanja, Niyati, 2013. "Oil price and exchange rates: A wavelet based analysis for India," Economic Modelling, Elsevier, vol. 31(C), pages 414-422.
    17. Afsin Sahin, 2013. "Estimating Money Demand Function by a Smooth Transition Regression Model: An Evidence for Turkey," Working Papers 791, Economic Research Forum, revised Nov 2013.
    18. Cheng, Che-Hui & Wu, Po-Chin, 2013. "Nonlinear earnings persistence," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 156-168.
    19. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    20. Nektarios Aslanidis, 2002. "Smooth Transition Regression Models in UK Stock Returns," Working Papers 0201, University of Crete, Department of Economics.
    21. Nektarios Aslanidis, 2002. "Regime-switching behaviour in European," Working Papers 0202, University of Crete, Department of Economics.
    22. Louise Holm, 2016. "The Swedish business cycle, 1969-2013," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2015(2), pages 1-22.
    23. Escribano, A. & Franses, Ph.H.B.F. & van Dijk, D.J.C., 1998. "Nonlinearities and outliers: robust specification of STAR models," Econometric Institute Research Papers EI 9832, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    24. Chen, Yi-Ting, 2003. "Discriminating between competing STAR models," Economics Letters, Elsevier, vol. 79(2), pages 161-167, May.
    25. Mei-Se Chien, 2013. "The Non-linear Ripple Effect of Housing Prices in Taiwan: A Smooth Transition Regressive Model," ERES eres2013_51, European Real Estate Society (ERES).

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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