A RESEARCH STUDY OF NONLINEARITY EXPERIENCING IN THE RATE OF CURRENT ACCOUNT DEFICIT TO THE BULGARIAN HEALTH AND CARE NATIONAL PRODUCT
Abstract
called Regime Switching Models: TAR, SETAR, Markov
Switching Model, etc. Usually, the behavior of time series exhibit breaks is
associated with structural changes in government policy or financial crises. In
the present research it is used as an example the calculated data about the
rate of current account deficit to the Bulgarian Health and Care National
Product. The series are hard to be modeling because of the structural change of
the government policy about the Bulgarian Health and Care National Product. The
basic hypothesis that is tested in the conducted research is that when there is
a case of changes in the time series in their structure it is impossible the
principles of linearity assumption to be applied. In the traditional
econometrics as a science the linearity is an important assumption but there
are practical evidences in which most of the time series do not provide this
assumption. These cases of such time series behavior are called nonlinearity
series. It is important to test the linearity assumption because of the
differing between the ways of modeling the series in the case of linearity and
nonlinearity using the date of the rate of current account deficit to the
Bulgarian Health and Care National Product. If any series do not provide the
linearity assumption and also have change in the structure then the case can be
modeling with TAR, SETAR or Markov Switching Model. We provided the research by
questioning whether there is nonlinearity in the rate of proportion of current
account deficit to the Bulgarian Health and Care National Product and it is
experienced with four nonlinearity tests: Kaplan Test, McLeod-Li test, BDS Test
and Tzay Test.
References
- Ashley, R.A., Patterson, D.M., (1998). Nonlinear Model Specifications/Diagnostics: Insights from a Battery of Nonlinearity Tests, 1998. Barnett, W.A., Gallant, R.A., Hinich, M.J., Jugeilges, A.J., Kaplan, D.T., Jensen, M.J., (1995), Robustness of Nonlinearity and Chaos Test to Measurement Error, Inference Method and Sample Size, Journal of Economic Behavior and Organization, 27, 301-320, 1995. Barnett, W.A., Gallant, R.A., Hinich, M.J., Jugeilges, A.J., Kaplan, D.T., Jensen M.J., (1996). An Experimental Desing To Compare Tests of Nonlinearity and Chaos, In Barnett W.A Kirman,A.P. and Salmon M. (Editors) Journal of Econometrics 77, 297-302, 1996. Barnett, W.A., Gallant, R.A., Hinich, M.J., Jugeilges, A.J., Kaplan, D.T., Jensen M.J., (1997). A Single Blind Controlled Competition among Test for Nonlinearity and Chaos, Journal of Econometrics, 77, 297-302, 1997. Brock, W.A, Hsieh, D.A., LeBaron, B, (2011). Nonlinear Dynamics, Chaos and Instability: Statistical Theory and Economic Evidence, MIT Press, Cambridge, Massachusetts, 2011. Cromwell, JB, Labys, WC., Teraza, M (2014), Univariate Tests for Time Series Models Sage, Thousand Oaks, Canada p.44, 2014. Granger, C.W.J., Terasvirta, T. (2013), Modeling Nonlinear Economic Relationships, Oxford University Press, 2013. Granger, C.W.J. (2010). Overview of Nonlinear Time Series Specifications in Economics, 2010. Hinich, M., Patterson, D.M. (2015), Detecting Epochs of Transient Dependence in White Noise, University of Texas at Austin, 2015. Kaplan, D.T. (1995а), Nonlinearity and No stationary: The Use of Surrogate Data in Interpreting Fluctuations, Center of Nonlinear Dynamics and Dept of Physiology McGill University Montreal, Canada, 1995. McLeod, Li (2013а), Diagnostic Checking of ARMA time Series Models Using Squared- Residual Autocorrelation, Journal of Time Series Analysis 4, 269-273, 2013. Patterson, D.M. and Ashley, R. (2000). A Nonlinear Time Series Workshop, Kluwer, Norwell, 2000. Sotirov, B., Sakakushev, B., Koriykov, Ts., Georgiev, Ts, Zhel, Zh. F, Popesku, G. (2010a). „Menidzhmant na kachestvoto i metrologiya“ - spetsialnost ot evropeyskoto obrazovatelno prostranstvo. // Standartizatsiya, Metrologiya, Sertifikatsiya, 2010, broy I, str. 25-28, ISSN 1310-0831 (Сотиров, Б., Сакакушев, Б., Корийков, Ц., Георгиев, Ц, Жел, Ж. Ф, Попеску, Г. „Мениджмънт на качеството и метрология“ - специалност от европейското образователно пространство. // Стандартизация, Метрология, Сертификация, 2010, брой I, стр. 25-28, ISSN 1310-0831). Potter, S.M., (1999). Nonlinear Impulse Response Functions, Federal Reserve Bank of New York. http://www.newyorkfed.org/research/staff_reports/sr87.pdf, 1999. Tzay, R. S, (2016). Nonlinearity Tests for Time Series, Biometrika 73, 461-466, 2016. Walter, E., Applied Econometrics Time Series, (2004). Second Edition p. 406, 2004.
Abstract
References
- Ashley, R.A., Patterson, D.M., (1998). Nonlinear Model Specifications/Diagnostics: Insights from a Battery of Nonlinearity Tests, 1998. Barnett, W.A., Gallant, R.A., Hinich, M.J., Jugeilges, A.J., Kaplan, D.T., Jensen, M.J., (1995), Robustness of Nonlinearity and Chaos Test to Measurement Error, Inference Method and Sample Size, Journal of Economic Behavior and Organization, 27, 301-320, 1995. Barnett, W.A., Gallant, R.A., Hinich, M.J., Jugeilges, A.J., Kaplan, D.T., Jensen M.J., (1996). An Experimental Desing To Compare Tests of Nonlinearity and Chaos, In Barnett W.A Kirman,A.P. and Salmon M. (Editors) Journal of Econometrics 77, 297-302, 1996. Barnett, W.A., Gallant, R.A., Hinich, M.J., Jugeilges, A.J., Kaplan, D.T., Jensen M.J., (1997). A Single Blind Controlled Competition among Test for Nonlinearity and Chaos, Journal of Econometrics, 77, 297-302, 1997. Brock, W.A, Hsieh, D.A., LeBaron, B, (2011). Nonlinear Dynamics, Chaos and Instability: Statistical Theory and Economic Evidence, MIT Press, Cambridge, Massachusetts, 2011. Cromwell, JB, Labys, WC., Teraza, M (2014), Univariate Tests for Time Series Models Sage, Thousand Oaks, Canada p.44, 2014. Granger, C.W.J., Terasvirta, T. (2013), Modeling Nonlinear Economic Relationships, Oxford University Press, 2013. Granger, C.W.J. (2010). Overview of Nonlinear Time Series Specifications in Economics, 2010. Hinich, M., Patterson, D.M. (2015), Detecting Epochs of Transient Dependence in White Noise, University of Texas at Austin, 2015. Kaplan, D.T. (1995а), Nonlinearity and No stationary: The Use of Surrogate Data in Interpreting Fluctuations, Center of Nonlinear Dynamics and Dept of Physiology McGill University Montreal, Canada, 1995. McLeod, Li (2013а), Diagnostic Checking of ARMA time Series Models Using Squared- Residual Autocorrelation, Journal of Time Series Analysis 4, 269-273, 2013. Patterson, D.M. and Ashley, R. (2000). A Nonlinear Time Series Workshop, Kluwer, Norwell, 2000. Sotirov, B., Sakakushev, B., Koriykov, Ts., Georgiev, Ts, Zhel, Zh. F, Popesku, G. (2010a). „Menidzhmant na kachestvoto i metrologiya“ - spetsialnost ot evropeyskoto obrazovatelno prostranstvo. // Standartizatsiya, Metrologiya, Sertifikatsiya, 2010, broy I, str. 25-28, ISSN 1310-0831 (Сотиров, Б., Сакакушев, Б., Корийков, Ц., Георгиев, Ц, Жел, Ж. Ф, Попеску, Г. „Мениджмънт на качеството и метрология“ - специалност от европейското образователно пространство. // Стандартизация, Метрология, Сертификация, 2010, брой I, стр. 25-28, ISSN 1310-0831). Potter, S.M., (1999). Nonlinear Impulse Response Functions, Federal Reserve Bank of New York. http://www.newyorkfed.org/research/staff_reports/sr87.pdf, 1999. Tzay, R. S, (2016). Nonlinearity Tests for Time Series, Biometrika 73, 461-466, 2016. Walter, E., Applied Econometrics Time Series, (2004). Second Edition p. 406, 2004.
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | September 15, 2019 |
| Submission Date | July 16, 2019 |
| Published in Issue | Year 2019 |
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