Ecosystem resilience, specialized adaptation and population decline: A modern Malthusian theory

The purpose of this article is to construct a theoretical framework characterizing the interactions among economic development, ecosystem equilibrium and possible population decline, and to discuss the population dynamics in the very long run. In our framework, economic activities bridge population and environment. On the one hand, human beings reform the environment through economic activities; on the other hand, economic activities decrease environmental resilience and increase the possibility of an environmental change in a discontinuous and irreversible pattern, as described in Arrow et al. (1995). Furthermore, a highly developed economy also causes over-specialization of human adaptation, which tends to exaggerate the impact of an environmental change on human population size. Received: 19 January 1999/Accepted: 3 July 1999     

A transnational networked public sphere of air pollution: analysis of a Twitter network of PM2.5 from the risk society perspective

ABSTRACT

Drawing on digitaltrace data, publicly accessible government documents, and journalistic reports, this research integrates Beck’s risk society theory with digital media theories to examine the mediated process of risk definition and assessment of PM2.5 (particulate matters with a diameter less than 2.5 micrometers) in a networked public sphere. Network and content analysis of a PM2.5 Twitter network shows that political and professional elite remained the most powerful producers of risk definition. Established media played a key role, yet faced challenges from a variety of actors who disseminated and filtered information. Laypersons, while peripheral, actively interacted with elite and established media. The blurring geographic boundary in the PM2.5 Twitter network revealed an emerging transnational public sphere, which, however, was segregated by language. This research advances a layered understanding of the contingent, paradoxical media impact for social changes in a risk society.     

Empirical study of robust estimation methods for PAR models with application to the air quality area

Abstract

This paper compares three estimators for periodic autoregressive (PAR) models. The first is the classical periodic Yule-Walker estimator (YWE). The second is a robust version of YWE (RYWE) which uses the robust autocovariance function in the periodic Yule-Walker equations, and the third is the robust least squares estimator (RLSE) based on iterative least squares with robust versions of the original time series. The daily mean particulate matter concentration (PM10) data is used to illustrate the methodologies in a real application, that is, in the Air Quality area.     

Do people think about absolute or relative price differences when choosing between substitute goods?

The article presents experimental evidence that shows that people often consider relative price differences in addition to absolute differences when choosing between substitute goods. Because the choice between substitute goods is a very common one, this is an important finding. The experiment uses scenarios in various consumption categories: hotel rooms, flights, and books. Subjects were either students or participants in an economics conference. The data allow to reject the hypothesis that people think only about relative price differences in favor of the hypothesis that people think about both relative and absolute price differences. Whether the price given to the subjects is that of the high-quality good or of the low-quality good makes a large difference, a result that is related to the endowment effect and the status quo bias. Implications of the results for business strategy and other areas are also discussed.     

First‐order bias correction for fractionally integrated time series

Most of the long memory estimators for stationary fractionally integrated time series models are known to experience non‐negligible bias in small and finite samples. Simple moment estimators are also vulnerable to such bias, but can easily be corrected. In this article, the authors propose bias reduction methods for a lag‐one sample autocorrelation‐based moment estimator. In order to reduce the bias of the moment estimator, the authors explicitly obtain the exact bias of lag‐one sample autocorrelation up to the order n⁻¹. An example where the exact first‐order bias can be noticeably more accurate than its asymptotic counterpart, even for large samples, is presented. The authors show via a simulation study that the proposed methods are promising and effective in reducing the bias of the moment estimator with minimal variance inflation. The proposed methods are applied to the northern hemisphere data. The Canadian Journal of Statistics 37: 476–493; 2009 © 2009 Statistical Society of Canada     

LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

ABSTRACT

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.