The Art and Politics of Ecology in India

This roundtable discussion with artist and activist Ravi Agarwal and film-maker and photographer Sanjay Kak, moderated by T J Demos, explores the politics of ecology in the Indian context. The conversation considers, among other works, Kak's film Words on Water (2002), which looks at the issue of big dams and their negative social-economic effects in the Narmada valley; and Agarwal's photographic installation Extinction, which examines the disappearance of vultures on the subcontinent owing to the development of animal pharmaceuticals used to maximize milk production. The conversation critically examines the introduction of neoliberalism in the Indian economy and political context, and the anti-democratic activity of multinational corporations, in relation to the destruction of the natural environment, the growth of economic inequality, and the dispossession of tribal peoples via the governmental-corporate development of mega-dams and industrial mining projects. The discussion revolves around the aesthetic approaches artists have used in addressing such ecological emergencies

Bias Correction of ML and QML Estimators in the EGARCH(1,1) Model

n this paper we derive the bias approximations of the Maximum Likelihood (ML) and Quasi-Maximum Likelihood (QML) Estimators of the EGARCH(1,1) parameters and we check our theoretical results through simulations. With the approximate bias expressions up to O(1/T), we are then able to correct the bias of all estimators. To this end, a Monte Carlo exercise is conducted and the results are presented and discussed. We conclude that, for given sets of parameters values, the bias correction works satisfactory for all parameters. The results for the bias expressions can be used in order to formulate the approximate Edgeworth distribution of the estimators.

Edgeworth and Moment Approximations: The Case of MM and QML Estimators for the MA(1) Models

Extending the results in Sargan (1976) and Tanaka (1984), we derive the asymptotic expansions, of the Edgeworth and Nagar type, of the MM and QML estimators of the 1^{st} order autocorrelation and the MA parameter for the MA(1) model. It turns out that the asymptotic properties of the estimators depend on whether the mean of the process is known or estimated. A comparison of the Nagar expansions, either in terms of bias or MSE, reveals that there is not uniform superiority of neither of the estimators, when the mean of the process is estimated. This is also confirmed by simulations. In the zero-mean case, and on theoretical grounds, the QMLEs are superior to the MM ones in both bias and MSE terms. The results presented here are important for deciding on the estimation method we choose, as well as for bias reduction and increasing the efficiency of the estimators.Edgeworth expansion, moving average process, method of moments, Quasi Maximum Likelihood, autocorrelation, asymptotic properties.

Stochastic Expansions and Moment Approximations for Three Indirect Estimators

This paper deals with properties of three indirect estimators that are known to be (first order) asymptotically equivalent. Specifically, we examine a) the issue of validity of the formal Edgeworth expansion of an arbitrary order. b) Given a), we are concerned with valid moment approximations and employ them to characterize the second order bias structure of the estimators. Our motivation resides on the fact that one of the three is reported by the relevant literature to be second order unbiased. However, this result was derived without any establishment of validity. We provide this establishment, but we are also able to massively generalize the conditions under which this second order property remains true. In this way, we essentially prove their higher order inequivalence. We generalize indirect estimators by introducing recursive ones, emerging from multistep optimization procedures. We are able to establish higher order unbiaseness for estimators of this sort.Asymptotic Approximation, Second Order Bias Structure, Binding Function, Local Canonical Representation, Convex Variational Distance, Recursive Indirect Estimators, Higher order Bias.

AGN Winds and Blazar Phenomenology

The launch of {\em Fermi} produced a significant number of AGN detections to allow statistical treatment of their properties. One of the first such systematics was the "Blazar Divide" in FSRQs and BL Lacs according to their gamma-ray spectral index and luminosity. Further data accumulation indicated this separation to be less clear than thought before. An MHD wind model which can model successfully the Seyfert X-ray absorber properties provides the vestiges of an account of the observed blazar classification. We propose to employ this model to model in detail the broad band blazar spectra and their statistical properties in terms of the physical parameters of these MHD winds