Atil, LyndaFellag, HocineSipols, Ana E.Santos Martín, M.TSimón de Blas, Clara2024-09-242024-09-242023-11-27Atil, L., Fellag, H., Sipols, A.E. et al. Non-linear Cointegration Test, Based on Record Counting Statistic. Comput Econ (2023). https://doi.org/10.1007/s10614-023-10520-10927-7099https://hdl.handle.net/10115/39778This paper proposes a new procedure for cointegration tests, as traditional tests fail to detect the presence of nonlinearities in cointegrated series. The two-step Engle and Granger (EG) test is modified by incorporating the RUR and FB-RUR tests of Aparicio et al. (2006). These non-parametric tests, based on functions of order statistics, exhibit desirable properties such as invariance to nonlinear transformations of the series and robustness to significant parameter shifts. Furthermore, no prior estimation of the cointegrating parameter is required, resulting in parameter-free asymptotic null distributions. Monte Carlo simulations are used to evaluate the test’s properties and power at different sample sizes, demonstrating their ability to detect cointegration in real exchange rate relationships where standard cointegration tests fail.Traditional tests fail to detect the presence of nonlinearities in series that are cointegrated, so in this paper a new procedure for cointegration tests is proposed by modifying the two-step Engle and Granger (EG) test (Engle and Granger in Econometrica 55:251–276, 1987), incorporating the RUR and the FB-RUR test of Aparicio et al. (J Time Ser Anal 27:545–576, 2006). The statistics of these non-parametric tests, which are constructed as functions of order statistics, endow the test with desirable properties such as invariance to non-linear transformations of the series and robustness to the presence of significant parameter shifts. As no prior estimation of the cointegrating parameter is required, the new tests lead to parameter-free asymptotic null distributions. Monte Carlo simulations are used to analyze the test properties and evaluate the power at different sample sizes. The robustness of the procedure is tested by performing a comparison of different tests of cointegration in real exchange rate relationships. These tests are able to find evidence of cointegration while standard cointegration tests fail to detect it.engCointegration testMonte CarloTime SeriesError Correction Modelinfo:eu-repo/semantics/article10.1007/s10614-023-10520-1info:eu-repo/semantics/embargoedAccess