Bootstrapping Neural tests for conditional heteroskedasticity

We deal with bootstrapping tests for detecting conditional heteroskedasticity in the context of standard and nonstandard ARCH models. We develope parametric and nonparametric bootstrap tests based both on the LM statistic and a neural statistic. The neural tests are designed to approximate an arbitrary nonlinear form of the conditional variance by a neural function. While published tests are valid asymptotically, they are not exact in finite samples and suffer from a substantial size distortion: the finite-sample error remains non-negligible, even for several hundred observations. Here, we treat this problem using bootstrap methods, making possible a better finite-sample estimate of the distribution of the test statistic. A graphical presentation employing a size-correction principle is used to show the true power of the tests rather than the spurious nominal power typically givenBootstrap, Artificial Neural Networks, ARCH models, inference tests

Admissible Clustering of Aggregator Components: A Necessary and Sufficient Stochastic Semi-Nonparametric Test for Weak Separability.

In aggregation theory, the admissibility condition for clustering together components to be aggregated is blockwise weak separability, which also is the condition needed to separate out sectors of the economy. Although weak separability is thereby of central importance in aggregation and index number theory and in econometrics, prior attempts to produce statistical tests of weak separability have performed poorly in Monte Carlo studies. This paper deals with semi- nonparametric tests for weak separability. It introduces both a necessary and su¢ cient test, and a fully stochastic procedure allowing to take into account measurement error. Simulations show that the test performs well, even for large measurement errors.weak separability, quantity aggregation, clustering, sectors, index number theory, semi-nonparametrics

Stopping Tests in the Sequential Estimation for Multiple Structural Breaks

In this paper, we propose the use of bootstrapping methods to obtain correct critical values for dating breaks. Following the procedure proposed in Banerjee, Lazarova and Urga (1998), we consider the case of estimating a system with two or more marginal processes and a conditional process. First, the location of the breaks in marginal models is estimated. Next, the marginal models are imposed on the conditional model to form a reduced form system. The conditional model with its own breaks is then estimated. The estimation of the break dates is sequential. Break dates are estimated via two alternative procedures: including estimated break dates one by one or splitting the sample. Inclusion of additional breaks or splitting samples are repeated until a criterion for stopping is satisfied. In this paper we propose bootstrap tests as criterion for stopping sequential search. This procedure allows to improve the estimators to avoid excessive bias and prove to be stable in the case of both stationary and non-stationary series. Finally, we illustrate the methods by modelling the money demand in United KingdomStructural Breaks, Sequential Testing, Bootstrap

Tests of structural changes in conditional distributions with unknown changepoints

This paper focuses on a procedure to test for structural changes in the first two moments of a time series, when no information about the process driving the breaks is available. To approximate the process, an orthogonal Bernstein polynomial is used and testing for the null is achieved either by using an AICu information criterion, or a restriction test. The procedure covers both the pure discrete structural change and the continuous changes models. Running Monte-Carlo simulations, we show that the test has power against various alternatives.Structural changes, Bernstein polynomial, AICu.

A Bootstrap Neural Network Based Heterogeneous Panel Unit Root Test: Application to Exchange Rates

This paper proposes a bootstrap artificial neural network based panel unit root test in a dynamic heterogeneous panel context. An application to a panel of bilateral real exchange rate series with the US Dollar from the 20 major OECD countries is provided to investigate the Purchase Power Parity (PPP). The combination of neural network and bootstrapping significantly changes the findings of the economic study in favour of PPP.Artificial neural network, panel unit root test, bootstrap, Monte Carlo experiments, exchange rates.

La disputa entre Foucault y Derrida por los restos de Descartes (1ª parte)

El hilo que, siempre tortuosa y a menudo subterráneamente, guiará este texto tiene mucho que ver con las complicadas relaciones, cómplices a veces y otras veces antagónicas, que, dentro del lenguaje, se tejen y destejen entre la escritura filosófica y la literatura. Complicación que se agrava aún más cuando de lo que se trata-y ésta es la tremenda apuesta en la que se ven implicados los tres pensadores de los que aquí me voy a ocupar: Descartes, Foucault y Derrida- es de saber cuál es el lugar -si lo hay- desde el cual ese resto impensado y, al parecer, tradicionalmente impensable del pensamiento, ese exceso y desmesura de la razón, en una palabra, la locura, lo Otro del pensamiento, puede decir su nombre, hablar por sí misma, en su propio nombre y en nombre propio

A nonlinear panel unit root test under cross section dependence

We propose a nonlinear heterogeneous panel unit root test for testing the null hypothesis of unit-roots processes against the alternative that allows a proportion of units to be generated by globally stationary ESTAR processes and a remaining non-zero proportion to be generated by unit root processes. The proposed test is simple to implement and accommodates cross sectional dependence. We show that the distribution of the test statistic is free of nuisance parameters as (N, T) −∞. Monte Carlo simulation shows that our test holds correct size and under the hypothesis that data are generated by globally stationary ESTAR processes has a better power than the recent test proposed in Pesaran [2007]. Various applications are providedNonlinear panel unit root tests, cross sectional dependence.

Do the Users Have Anything to Add?

A survey of the people who frequently use homeless shelters or hot meal distribution services ended with a deliberately vague question in order to give the homeless the rare opportunity to make their opinions heard. Half of the homeless people surveyed seized this chance. One fifth of those surveyed talked about the survey itself, to rate its quality, but also to criticise it. A third focused on their difficulties in finding housing and/or work One fifth used the opportunity to give their opinion about homeless shelters. One in ten criticized the support services in general or directly criticised the support workers they had had contact with, such as social workers. The responses to this final question not only suggest new ways in which such questionnaires could be improved, but also report on the persistent problems which people without official documents, couples, families and even young people face.Homeless, Poverty, Lexicography, Interview, Discourse

Entrevista con Jacques Derrida

Sin resume

A GARCH analysis of dark-pool trades

The ability to trade in dark-pools without publicly announcing trading orders, concerns regulators and market participants alike. This paper analyzes the information contribution of dark trades to the intraday volatility process. The analysis is conducted by performing a GARCH estimation framework where errors follow the generalized error distribution (GED) and two different proxies for dark trading activity are separately included in the volatility equation. Results indicate that dark trades convey important information on the intraday volatility process. Furthermore, the results highlight the superiority of the proportion of dark trades relative to the proportion of dark volume in affecting the one-step-ahead density forecas