Range unit root tests

Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analyse time series with strong serial dependence, the focus being placed in the detection of eventual unit roots in an autorregresive model fitted to the series. In this paper we propose a completely different method to test for the type of "long-wave" patterns observed not only in unit root time series but also in series following more complex data generating mechanism. To this end, our testing device analyses the trend exhibit by the data, without imposing any constraint on the generating mechanism. We call our device the Range Unit Root (RUR) Test since it is constructed from running ranges of the series. These statistics allow a more general characterization of a strong serial dependence in the mean behavior, thus endowing our test with a number of desirable properties. Among these properties are the invariance to nonlinear monotonic transformations of the series and the robustness to the presence of level shifts and additive outliers. In addition, the RUR test outperforms the power of standard unit root tests on near-unit-root stationary time series

A range unit root test

Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analyse time series with strong serial dependence, the focus being placed in the detection of eventual unit roots in an autorregresive model fitted to the series. In this paper we propose a completely different method to test for the type of long-wave patterns observed not only in unit root time series but also in series following more complex data generating mechanisms. To this end, our testing device analyses the trend exhibit by the data, without imposing any constraint on the generating mechanism. We call our device the Range Unit Root (RUR) Test since it is constructed from running ranges of the series. These statistics allow a more general characterization of a strong serial dependence in the mean behavior, thus endowing our test with a number of desirable properties, among which its error-model-free asymptotic distribution, the invariance to nonlinear monotonic transformations of the series and the robustness to the presence of level shifts and additive outliers. In addition, the RUR test outperforms the power of standard unit root tests on near-unit-root stationary time series and is asymptotically immune to noise

Nonlinear Cointegration and Nonlinear Error Correction: Record Counting Cointegration Tests

In this article we propose a record counting cointegration (RCC) test that is robust to nonlinearities and certain types of structural breaks. The RCC test is based on the synchronicity property of the jumps (new records) of cointegrated series, counting the number of jumps that simultaneously occur in both series. We obtain the rate of convergence of the RCC statistics under the null and alternative hypothesis. Since the asymptotic distribution of RCC under the null hypothesis of a unit root depends on the short-run dependence of the cointegrated series, we propose a small sample correction and show by Monte Carlo simulation techniques their excellent small sample behaviour. Finally, we apply our new cointegration test statistic to several financial and macroeconomic time series that have certain structural breaks and nonlinearities.Publicad

Range Unit Root (RUR) Tests: Robust against Nonlinearities, Error Distributions, Structural Breaks and Outliers

Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analysing time series with strong serial dependence in mean behaviour, the focus being placed on the detection of eventual unit roots in an autoregressive model fitted to the series. In this paper, we propose a completely different method to test for the type of long-wave patterns observed not only in unit-root time series but also in series following more complex data-generating mechanisms. To this end, our testing device analyses the unit-root persistence exhibited by the data while imposing very few constraints on the generating mechanism. We call our device the range unit-root (RUR) test since it is constructed from the running ranges of the series from which we derive its limit distribution. These nonparametric statistics endow the test with a number of desirable properties, the invariance to monotonic transformations of the series and the robustness to the presence of important parameter shifts. Moreover, the RUR test outperforms the power of standard unit-root tests on near-unit-root stationary time series; it is invariant with respect to the innovations distribution and asymptotically immune to noise. An extension of the RUR test, called the forward?backward range unit-root (FB-RUR) improves the check in the presence of additive outliers. Finally, we illustrate the performances of both range tests and their discrepancies with the Dickey?Fuller unit-root test on exchange rate series.Publicad

A RANGE UNIT ROOT TEST

Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analyse time series with strong serial dependence, the focus being placed in the detection of eventual unit roots in an autorregresive model fitted to the series. In this paper we propose a completely different method to test for the type of“long-wave” patterns observed not only in unit root time series but also in series following more complex data generating mechanisms. To this end, our testing device analyses the trend exhibit by the data, without imposing any constraint on the generating mechanism. We call our device the Range Unit Root (RUR) Test since it is constructed from running ranges of the series. These statistics allow a more general characterization of a strong serial dependence in the mean behavior, thus endowing our test with a number of desirable properties, among which its error-model-free asymptotic distribution, the invariance to nonlinear monotonic transformations of the series and the robustness to the presence of level shifts and additive outliers. In addition, the RUR test outperforms the power of standard unit root tests on near-unit-root stationary time series and is asymptotically immune to noise.

La utilidad de la teoría de traducción en la práctica desde la perspectiva de los traductores

Treball Final de Grau en Traducció i Interpretació. Codi: TI0983. Curs 2014-2015Este Trabajo Final de Grado tiene por objetivo investigar la opinión de los traductores profesionales sobre la utilidad de la traducción en la práctica traductora, tal y como indica su título. Existe una gran disparidad de opiniones con relación a la aportación que la teoría de la traducción puede hacer a la práctica traductora. De esta misma forma, simultáneamente se ha elaborado una contextualización teórica sobre este objeto de estudio para poder conocer las diferentes opiniones de diversos teóricos sobre el tema. Para poder ofrecer datos reales sobre la opinión de los traductores profesionales acerca de esta cuestión, en este trabajo se ha realizado una encuesta, con la que se pretende reflejar la opinión de los traductores profesionales encuestados, para así poder elaborar una conclusión y confirmar una hipótesis. A grandes rasgos podemos decir que los resultados de la encuesta muestran la gran controversia que hay respecto al tema, puesto que hallamos dos grupos bien diferenciados y prácticamente igualados: los que apoyan que la teoría de la traducción tenga utilidad, es decir, que sí aporta una contribución en la práctica traductora y los que no

Nonlinear Cointegration and Nonlinear Error Correction: Record Counting Cointegration Tests.

In this article we propose a record counting cointegration (RCC) test that is robust to nonlinearities and certain types of structural breaks. The RCC test is based on the synchronicity property of the jumps (new records) of cointegrated series, counting the number of jumps that simultaneously occur in both series. We obtain the rate of convergence of the RCC statistics under the null and alternative hypothesis. Since the asymptotic distribution of RCC under the null hypothesis of a unit root depends on the short-run dependence of the cointegrated series, we propose a small sample correction and show by Monte Carlo simulation techniques their excellent small sample behaviour. Finally, we apply our new cointegration test statistic to several financial and macroeconomic time series that have certain structural breaks and nonlinearities.Cointegration; Counting statistics; Jumps; Nonlinearity; Ranges; Robustness; Small sample corrections; Structural breaks; Unit roots tests; 37M10; 62M10;

Range Unit Root (RUR) Tests: Robust against Nonlinearities, Error Distributions, Structural Breaks and Outliers.

Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analysing time series with strong serial dependence in mean behaviour, the focus being placed on the detection of eventual unit roots in an autoregressive model fitted to the series. In this paper, we propose a completely different method to test for the type of long-wave patterns observed not only in unit-root time series but also in series following more complex data-generating mechanisms. To this end, our testing device analyses the unit-root persistence exhibited by the data while imposing very few constraints on the generating mechanism. We call our device the range unit-root (RUR) test since it is constructed from the running ranges of the series from which we derive its limit distribution. These nonparametric statistics endow the test with a number of desirable properties, the invariance to monotonic transformations of the series and the robustness to the presence of important parameter shifts. Moreover, the RUR test outperforms the power of standard unit-root tests on near-unit-root stationary time series; it is invariant with respect to the innovations distribution and asymptotically immune to noise. An extension of the RUR test, called the forward?backward range unit-root (FB-RUR) improves the check in the presence of additive outliers. Finally, we illustrate the performances of both range tests and their discrepancies with the Dickey?Fuller unit-root test on exchange rate series.

Regional devolopment and solidarity in Spain: the relationship between the E.C. and the internal solidarity funds.

This paper will analyse the importance that the Regional Solidarity Funds have acquired in the financial systems of those Autonomous Communities able to benefit from them, and the influence that these Funds have had in their development. In addition, the special role that Structural Funds have had in order to bring the Autonomous Communities’ development in line with the average level of the European Member States will be referred to. Then, the different EC tools that are connected with regional development will be described and analysed; next, the Spanish regions that have benefited from these tools since Spain joined the European Union in 1986 will be studied. The Internal Solidarity Funds and their relationship with the EC Structural Funds will also be analysed. The effects of these mechanisms will be examined from three different points of view. First of all, the impact of these resources on the regional financial system will be considered; secondly, the coordination between the EC Structural Funds and the Internal Solidarity Funds, and the impact of this relationship will be analysed; and finally, whether or not these mechanisms are efficient enough to achieve the aim of regional unity will be determined, and their influence in different areas of production will also be observed. Key words: Fiscal Federalism, Spanish Autonomous Communities, Structural Funds, Inter-regional Solidarity. JEL Codes: H7, H77.