Yaya, OlaOluwa S and Ogbonna, Ephraim A and Furuoka, Fumitaka and Gil-Alana, Luis A. (2019): A new unit root analysis for testing hysteresis in unemployment.
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Abstract
This paper proposes a nonlinear unit root test based on the artificial neural network-augmented Dickey-Fuller (ANN-ADF) test for testing hysteresis in unemployment. In this new unit root test, the linear, quadratic and cubic components of the neural network process are used to capture the nonlinearity in the time-series data. Fractional integration methods, based on linear and nonlinear trends are also used in the paper. By considering five European countries such as France, Italy, Netherland, Sweden, and the United Kingdom, the empirical findings indicate that there is still hysteresis in these countries. Among batteries of unit root tests applied, both the ARNN-ADF and fractional integration tests fail to reject the hypothesis of unemployment hysteresis in all the countries.
Item Type: | MPRA Paper |
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Original Title: | A new unit root analysis for testing hysteresis in unemployment |
Language: | English |
Keywords: | Unit root process; Nonlinearity; Neuron network: Time-series; Hysteresis; Unemployment; Europe; Labour market. |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes |
Item ID: | 96621 |
Depositing User: | Dr OlaOluwa Yaya |
Date Deposited: | 23 Oct 2019 12:28 |
Last Modified: | 23 Oct 2019 12:28 |
References: | Adya, M. & Calopy, F. (1998). How effective are neural networks at forecasting and prediction? A review and evaluation, Journal of Forecasting, 17, 481–95. Balkin, S. D. & Ord, J. K. (2000). Automatic neural network modelling for univariate time series, International Journal of Forecasting, 16, 509–15. Beran, J. (1995). Maximum likelihood estimation of the differencing parameter for invertible short and long memory ARIMA models, Journal of the Royal Statistical Society, Series B, 57, 659-672. Binner, J. M., Bissoondeeal, R. K., Elger, T., Gazely, A. M. & Mullineux, A. W. (2005). A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia, Applied Economics, 37:6, 665–680, DOI: 10.1080/0003684052000343679. Breuer, J. B., McNown, R. and Wallace, M. (2002). Series-Specific Unit Root Tests with Panel Data. Oxford Bulletin of Economics and Statistics 64 (5): 527–546. Burges A. N. & Refenes A. N. (1999). Modelling non-linear moving average processes using neural networks with error feedback: an application to implied volatility forecasting. Signal Process; 74(1), 89–99. Camarero, M., & Tamarit, C. (2004). Hysteresis vs. natural rate of unemployment: New evidence for OECD countries. Economics Letters, 84, 413– 417. Camarero, M., Carrión-i-Silvestre, J. L., & Tamarit, C. (2006). Testing for hysteresis in unemployment in OECD countries. New evidence using stationarity panel tests with breaks. Oxford Bulletin of Economics and Statistics, 68, 167–182. Caner, M. & Hansen, B. E. (2001). Threshold autoregression with a unit root. Econometrica, 69, 1555–1596. Choi, I. (2002). Combination Unit Root Tests for Cross-Sectionally Correlated Panels. Mimeo, Hong Kong University of Science and Technology. Cambridge University Press. UK. Choi, I. (2015). Almost all about Unit roots: Foundations, Developments and Applications. Connor, J. T. & Martin, R. D. (1994). Recurrent neural networks and robust time series prediction. IEEE Transactions on Neural Network; 5(2):240–253. De Gooijer, J. G. & Hyndman, R. J. (2006). 25 years of time series forecasting. Int. J. Forecast; 22(3): 443–473. Dickey, D. A & Fuller, W. A. (1979). Distributions of the Estimators for Autoregressive Time Series with a Unit Root, Journal of American Statistical Association, 74(366), 427–481. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4), 1057–1072. Enders, W. & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74, 574–599. Engle, R. F. (2003). Risk and volatility: Econometric models and financial practice. 44 West Fourth Street, New York, NY 10012-1126, USA: New York University, Department of Finance (SalomonCentre). Elliott, G., Rothenberg, T.J. & Stock, J.H. (1996). Efficient Tests for an Autoregressive Unit Root. Econometrica, 64(4), 813–836. European Commission (2019). Eurostat. https://ec.europa.eu/eurostat/data/database [accessed on 16 March 2019]. Franchi, M. & Ordonez, J. (2008). Common smooth transition trend-stationarity in European unemployment. Economics Letters, 101, 106–109. Franses, P. H. & van Dijk, D. (2003). Nonlinear Time Series Models in Empirical Finance, Cambridge: Cambridge University Press. Furuoka, F. (2017). A new approach to testing unemployment hysteresis. Empirical Economics, 53(3), 1253–1280. Gil-Alana, L. A. & Yaya, O. S. (2018). Testing Fractional Unit Roots with Non-linear Smooth Break Approximations using Fourier functions. MPRA Paper No. 90516. Gorr, W. L., Nagin, D. & Szcypula, J. (1994). Comparative study of artificial neural network and statistical models for predicting student grade point averages, International Journal of Forecasting, 10, 17–34. Granger, C. W. J. & Teräsvirta, T. (1993). Modelling Nonlinear Economic Relationships. Oxford: Oxford University Press. Chinese edition 2006: Shanghai University of Finance and Economics Press. Haggan, V. & Ozaki, T. (1981). Modeling nonlinear vibrations using an amplitude dependent autoregressive time series model, Biometrika, 68, 189-196. Hornik, K., Stinchcombe, M. & White, H. (1989). Multi-Layer Feedforward Networks are Universal Approximators. Neural Networks, 2, 359–366. Iacus, S. M. (2011). Statistical data analysis of financial time series and option pricing in R. Chicago: R/Finance, USA. Im, K. S., Pesaran, M. H. & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115, 53–74. Johnes, G. (2000). Up around the bend: linear and nonlinear models of the UK economy compared, International Review of Applied Economics, 14, 485–493. Kapetanios, G., Shin, Y. & Snell, A. (2003). Testing for a Unit Root in the Nonlinear STAR Framework. Journal of Econometrics 112: 359–379. Lee, J., & Strazicich, M. (2013). Minimum LM unit root test with two structural break. Economics Bulletin, 33, 483–2492. Levin, A., Lin, C. & Chu, C. J. (2002). Unit Root Tests in Panel Data: Asymptotic and Finite-sample Properties, Journal of Econometrics, 108, 1–24. Maddala, G. S. & Wu, S. (1999). A Comparative Study of Unit Root Tests with Panel Data and a new simple test. Oxford Bulletin of Economics and Statistics, 61, 631–652. MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11, 601–618. Moon, H. R. & Perron, P. (2004), Testing for Unit Root in Panels with Dynamic Factors. Journal of Econometrics, 122, 81–126. Moshiri, S. and Cameron, N. (2000). Neural Network versus Econometric Models in Forecasting Inflation. Journal of Forecasting 19, 201–217. Nag, A. K. & Mitra, A. (2002) Forecasting daily foreign exchange rates using genetically optimized neural networks, Journal of Forecasting, 21, 501–511. Escribano, A. and Jorda, O. (1999). Improved testing and specification of smooth transition regression models. In: Rothman P. (eds) Nonlinear Time Series Analysis of Economic and Financial Data. Dynamic Modeling and Econometrics in Economics and Finance, vol 1. Springer, Boston, MA Perron, P. (2006). Dealing with structural breaks. Palgrave Handbook of Econometrics, 1, 278–352. Perron, P. & Vogelsang, T. J. (1992). Non-stationarity and level shifts with an application to purchasing power parity. Journal of Business, Economics and Statistics, 10(3), 301– 320. Pesaran, M. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross Section Dependence. Journal of Applied Econometrics, 22, 265–312. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. Rech, G. (2002). Modelling and Forecasting Economic Time Series with Single Hidden-Layer Feed forward Autoregressive Artificial Neural Networks. PhD Thesis. Stockholm School of Economics. Robinson, P. M. (1994). Efficient tests of nonstationary hypotheses. Journal of American Statistical Association. 89, 1420–1437 Robinson P. M. (2005). Modelling memory of economic and financial time series. London School of Economics and Political Science. Salisu, A. A., Ogbonna, A. E. and Omosebi, P. A. (2018). Does the choice of estimator matter for forecasting? A revisit. Working Papers 053, Centre for Econometric and Allied Research, University of Ibadan. Shin, D. & Lee, O. (2001). Tests for asymmetry in possibly nonstationary time series data, Journal of Business and Economic Statistics 19, 233−244. Sowell, F. (1992). Maximum likelihood estimation of stationary univariate fractionally integrated time series models, Journal of Econometrics 53, 165.18 Stanca, L. (1999). Asymmetries and nonlinearities in Italian macroeconomic fluctuations, Applied Economics, 31, 483–499. Taylor, A. M. (2002). A century of purchasing power parity. Review of Economics and Statistics. 84, 139–150. Taylor, M. P. & Sarno, L. (1998). The Behavior of Real Exchange Rates during the Post-Bretton Woods Period. Journal of International Economics, 46, 281–312. Tealab, T., Hefny, H. and Badr, A. (2017). Forecasting of nonlinear time series using ANN, Future Computing and Informatics Journal, 2(1), 39–47. Teräsvirta, T. (1994). Specification, Estimation and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89, 208–218. Teräsvirta, T. (2005). Forecasting economic variables with nonlinear models. SSE/EFI working paper in economics and finance. Stockholm: Department of Economic Statistics; p. 598. Tiao, G. C. and Tsay, R. S. (1994). Some advances in non-linear and adaptive modelling in time-series, Journal of Forecasting, 13, 109–131. Tong, H. (1990). Nonlinear Time Series: A Dynamical System Approach. Oxford University Press, Oxford, UK. van Dijk, D., Franses, P. H., and Paap, R. (2002). A nonlinear long memory model, with an application to US unemployment. Journal of Econometrics, 110, 135–165. van Dijk, D., Medeiros, M. C. and Teräsvirta, T. (2004). Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: a re-examination. Department of Economics PUC-Rio, Pontifical Catholic University of Rio de Janeiro Rua Marques de Sao Vicente 225-Rio de Janeiro 22453-900, RJ. Yaya, O. S., Ogbonna, A. E. and Atoi, N. V. (2019). Are Inflation Rates in OECD countries actually Stationary during 2011-2018? Evidence based on Fourier Nonlinear Unit Root tests with breaks. MPRA paper 93937, University Library of Munich, Germany. Yaya, O. S., Ogbonna, A. E. and Mudida, R. (2019). Hysteresis of Unemployment rate in Africa: New Findings from Fourier ADF test. Quality and Quantity International Journal of Methodology. https://doi.org/10.1007/s11135-019-00894-6. Zivot, E. and Andrews, D. W. K. (1992). Further evidence on Great Crash, the oil price shock and the unit root hypothesis. Journal of Business, Economics and Statistics, 10, 251–270. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/96621 |