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.

Cosmic ray transport and radiative processes in nuclei of starburst galaxies

The high rate of star formation and supernova explosions of starburst galaxies make them interesting sources of high energy radiation. Depending upon the level of turbulence present in their interstellar medium, the bulk of cosmic rays produced inside starburst galaxies may lose most of their energy before escaping, thereby making these sources behave as calorimeters, at least up to some maximum energy. Contrary to previous studies, here we investigate in detail the conditions under which cosmic ray confinement may be effective for electrons and nuclei and we study the implications of cosmic ray confinement in terms of multifrequency emission from starburst nuclei and production of high energy neutrinos. The general predictions are then specialized to three cases of active starbursts, namely M82, NGC253 and Arp220. Both primary and secondary electrons, as well as electron-positron pairs produced by gamma ray absorption inside starburst galaxies are taken into account. Electrons and positrons produced as secondary products of hadronic interactions are found to be responsible for most of the emission of leptonic origin. In particular, synchrotron emission of very high energy secondary electrons produces an extended emission of hard X-rays that represent a very interesting signature of hadronic process in starburst galaxies, potentially accessible to current and future observations in the X-ray band. A careful understanding of both the production and absorption of gamma rays in starburst galaxies is instrumental to the assessment of the role of these astrophysical sources as sources of high energy astrophysical neutrinos.Comment: Version accepted for publication in MNRA