Monetary Policy and the Political Support for a Labor Market Reform

Lagged benefits relative to costs can politically block an efficiency-enhancing labor market reform, lending support to the two-handed approach. An accommodating monetary policy, conducted alongside the reform, could help bringing the positive effects of the reform to the fore. In order to identify the mechanisms through which monetary policy may affect the political sustainability of a reform, we add stylized features of the labor market to a standard New-Keynesian model for monetary policy analysis. A labor market reform is modeled as a structural change inducing a permanent shift in the flexible-price unemployment and output levels. In addition to the permanent gains, the impact of the timing and magnitude of the reform-induced adjustments on the welfare of workers - employed and unemployed - is crucial to the political feasibility of the reform. Since the adjustments depend, on one hand, on the macroeconomic structure and, on the other hand, can be influenced by monetary policy, we simulate various degrees of output persistence across different policy rules. We find that, if inertias are present, monetary policy, even when conducted by an independent central bank, affects the political support for the reform. In general, the more expansionary (or the less contractionary) the policy is, the faster is the recovery to the new steady-state equilibrium and, thus, the stronger is the political support.Monetary policy rules; Labor market reforms; Unemployment benefit; Political economy; New-Keynesian models

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

Testing for cointegration using induced-order statistics.

In this paper we explore the usefulness of induced-order statistics in the characterization of integrated series and of cointegration relationships. We propose a non-parametric test statistic for testing the null hypothesis of two independent random walks against wide cointegrating alternatives including monotonic nonlinearities and certain types of level shifts in the cointegration relationship. We call our testing device the induced-order Kolmogorov?Smirnov cointegration test (KS), since it is constructed from the induced-order statistics of the series, and we derive its limiting distribution. This non-parametric statistic endows the test with a number of desirable properties: invariance to monotonic transformations of the series, and robustness for the presence of important parameter shifts. By Monte Carlo simulations we analyze the small sample properties of this test. Our simulation results show the robustness of the induced order cointegration test against departures from linear and constant parameter models.Unit root tests; Cointegration tests; Nonlinearity; Robustness; Induced order statistics; Engle and Granger test;

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.

Risk management in megaprojects.

Despite its high relevance to the success of megaprojects, risk management remains one of the least developed research issues. Risk management is a process composed of several phases. This paper is focused on the first of these phases: risk identification. Our purpose is to establish the state of the art in risk management in megaprojects, systematize the risks studied in the literature, as well as to identify potential areas of further research. To this end, a systematic review is carried out. Academic journals and conference papers published from 2000 onwards in main databases (WoK, Scopus and ABI) have been examined. A qualitative analysis has been performed by using ATLAS.ti together with a checklist. To the best of the authors’ knowledge, no previous systematic revision of papers on risk management in megaprojects has ever been carried out, although certain authors have emphasized its importance. The contribution of this research includes: a bibliometric analysis of the papers that focus on risk management in megaprojects; a systematization and classification of the risks; tw†o matrices comprised of the proposed risk categorization, first in relation to the sector studied, and second related with the different stakeholders; and an identification of gaps in the research in risk management in megaprojects. The systematization of the risks helps managers towards their identification within the megaproject, and to follow the subsequent steps in the risk management process. Moreover, the matrix developed on the transfer of risks can enable managers to analyse who would be the best partner to support each risk. Furthermore, from an academic point of view, potential areas for future lines of research are presented