You survive teletransportation

Suppose that it was possible to teletransport. The teletransporter would destroy your old brain and body and construct an identical brain and body at a new location. Would you survive teletransportation? Many people think that teletransportation would kill you. On their view, the person that emerges from the teletransporter would be a replica of you, but it wouldn't be you. In contrast, I argue that there's no relevant difference between teletransportation and ordinary survival. So, if you survive ordinary life, then you survive teletransportation. Yet my argument may also show that we have little prudential reason to care about our survival in general

The Duty to Disobey Immigration Law

Many political theorists argue that immigration restrictions are unjust and defend broadly open borders. In this paper, I examine the implications of this view for individual conduct. In particular, I argue that the citizens of states that enforce unjust immigration restrictions have duties to disobey certain immigration laws. States conscript their citizens to help enforce immigration law by imposing legal duties on these citizens to monitor, report, and refrain from interacting with unauthorized migrants. If an ideal of open borders is true, these laws are unjust. Furthermore, if citizens comply with their legal duties, they contribute to violating the rights of migrants. We are obligated to refrain from contributing to rights-violations. So, citizens are obligated to disobey immigration laws. I defend the moral requirement to disobey immigration laws against the objection that disobedience to the law is excessively risky and the objection that citizens have political obligations to obey the law

A Dilemma for Buddhist Reductionism

This article develops a dilemma for Buddhist Reductionism that centers on the nature of normative reasons. This dilemma suggests that Buddhist Reductionism lacks the resources to make sense of normative reasons and, furthermore, that this failure may cast doubt on the plausibility of Buddhist Reductionism as a whole

Consistent Order Selection with Strongly Dependent Data and its Application to Efficient Estimation

Order selection based on criteria by Akaike (1974), AIC, Schwarz (1978), BIC or Hannan and Quinn (1979) HIC is often applied in empirical examples. They have been used in the context of order selection of weakly dependent ARMA models, AR models with unit or explosive roots and in the context of regression or distributed lag regression models for weakly dependent data. On the other hand, it has been observed that data exhibits the so-called strong dependence in many areas. Because of the interest in this type of data, our main objective in this paper is to examine order selection for a distributed lag regression model that covers in a unified form weak and strong dependence. To that end, and because of the possible adverse properties of the aforementioned criteria, we propose a criterion function based on the decomposition of the variance of the innovations of the model in terms of their frequency components. Assuming that the order of the model is finite, say po , we show that the proposed criterion consistently estimates, po. In addition, we show that adaptive estimation for the parameters of the model is possible without knowledge of po . Finally, a small Monte-Carlo experiment is included to illustrate the finite sample performance of the proposed criterion.Order selection, distributed lag models, strong dependence.

Semiparametric Estimation for Stationary Processes whose Spectra have an Unknown Pole

We consider the estimation of the location of the pole and memory parameter, ?0 and a respectively, of covariance stationary linear processes whose spectral density function f(?) satisfies f(?) ~ C|? - ?0|-a in a neighbourhood of ?0. We define a consistent estimator of ?0 and derive its limit distribution Z?0 . As in related optimization problems, when the true parameter value can lie on the boundary of the parameter space, we show that Z?0 is distributed as a normal random variable when ?0 ? (0, p), whereas for ?0 = 0 or p, Z?0 is a mixture of discrete and continuous random variables with weights equal to 1/2. More specifically, when ?0 = 0, Z?0 is distributed as a normal random variable truncated at zero. Moreover, we describe and examine a two-step estimator of the memory parameter a, showing that neither its limit distribution nor its rate of convergence is affected by the estimation of ?0. Thus, we reinforce and extend previous results with respect to the estimation of a when ?0 is assumed to be known a priori. A small Monte Carlo study is included to illustrate the finite sample performance of our estimators.spectral density estimation, long memory processes, Gaussian processes

SPECIFICATION TESTING FORREGRESSION MODELS WITHDEPENDENT DATA

We describe and examine a consistent test for the correct specification of aregression function with dependent data. The test is based on the supremum of thedifference between the parametric and nonparametric estimates of the regressionmodel. Rather surprisingly, the behaviour of the test depends on whether theregressors are deterministic or stochastic. In the former situation, the normalizationconstants necessary to obtain the limiting Gumbel distribution are data dependentand difficult to estimate, so to obtain valid critical values may be difficult, whereasin the latter, the asymptotic distribution may not be even known. Because of that,under very mild regularity conditions we describe a bootstrap analogue for the test,showing its asymptotic validity and finite sample behaviour in a small Monte Carloexperiment.Functional specification. Variable selection. Nonparametric kernelregression. Frequency domain bootstrap.

A Bootstrap Causality Test for Covariance Stationary Processes

This paper examines a nonparametric test for Granger-causality for a vector covariance stationary linear process under, possibly, the presence of long-range dependence. We show that the test converges to a non-distribution free multivariate Gaussian process, say vec (B(µ)) indexed by µ ? [0,1]. Because, contrary to the scalar situation, it is not possible, except in very specific cases, to find a time transformation g(µ) such that vec (B(g(µ))) is a vector with independent Brownian motion components, it implies that inferences based on vec (B(µ)) will be difficult to implement. To circumvent this problem, we propose bootstrapping the test by two alternative, although similar, algorithms showing their validity and consistency.Causality tests, long range, bootstrap tests.

Bootstrap goodness-of-fit tests for farima models

This paper proposes goodness-of-fit tests for the class of covariance stationary FARIMA processes. They are based on functionals of weighted empirical processes, say Sn C.), where the weights are the relative error between the periodogram and the fitted spectral density function under the null specification of the data. Two examples of such functionals are the Tp - Barlett and the Cramer-Von Mises standardized ro - statistics. We show that the tests are able to detect contiguous alternatives converging to the null at the rate n-JI2 • However, because the cumbersome covariance structure of the limiting process of Sn C.), tests based on its asymptotic distribution are difficult to implement in practice_ To circumvent this problem, we propose a bootstrap test, showing its consistency, and studying its small sample performance by a Monte Carlo experiment. _________________________________________________

Nonparametric estimation of structural breakpoints

This paper proposes point and interval estimates of location and size of jumps in multiple regression curves or its derivatives. We are mainly concerned with time series models where structural breaks occur at a given period of time or they are explained by the value taken by some predictor (e.g. threshold models). No previous knowledge of the underlying regression function is required. Left and right limits of the function, with respect to the regressor explaining the break, are estimated at each data point using multivariate multiplicative kernels. The univariate kernel corresponding to the regressor explaining the break is one-sided, with all its mass at the right or left of zero. Since left and right limits are the same, except at the break point, the location of the jump is estimated as the observed regressor value maximizing the difference between left and right limit estimates. This difference, evaluated at the estimated location point, is the estimation of the jump size. A small Monte Carlo study and an empirical application to USA macroecomic data illustrates the performance of the procedure in small samples. The paper also discusses some extensions, in particular the identification of the coordinate explaining the break, the application of the procedure to the estimation of parametric models, and robustification of the method for the influence of outliers