"Racial Preferences in a Small Urban Housing Market: A Spatial Econometric Analysis of Microneighborhoods in Kingston, New York"

This paper use spatial econometric models to test for racial preferences in a small urban housing market. Identifying racial preferences is difficult when unobserved neighborhood amenities vary systematically with racial composition. We adopt three strategies to redress this problem: (1) we focus on housing price differences across microneighborhoods in the small and relatively homogenous city of Kingston, New York; (2) we introduce GIS-based spatial amenity variables as controls in the hedonic regressions; and (3) we use spatial error and lag models to explicitly account for the spatial dependence of unobserved neighborhood amenities. Our simple OLS estimates agree with the consensus in the literature that black neighborhoods have lower housing prices. However, racial price discounts are no longer significant when we account for the spatial dependence of errors. Our results suggest that price discounts in black neighborhoods are caused not by racial preferences but by the demand for amenities that are typically not found in black neighborhoods.Housing; Race; Neighborhood Amenities; Spatial Econometrics Commonwealth of Independent States

Earnings mobility and measurement error : a pseudo-panel approach

The degree of mobility in incomes is often seen as an important measure of the equality of opportunity in a society and of the flexibility and freedom of its labor market. But estimation of mobility using panel data is biased by the presence of measurement error and non-random attrition from the panel. This paper shows that dynamic pseudo-panel methods can be used to consistently estimate measures of absolute and conditional mobility in the presence of non-classical measurement errors. These methods are applied to data on earnings from a Mexican quarterly rotating panel. Absolute mobility in earnings is found to be very low in Mexico, suggesting that the high level of inequality found in the cross-section will persist over time. However, the paper finds conditional mobility to be high, so that households are able to recover quickly from earnings shocks. These findings suggest a role for policies which address underlying inequalities in earnings opportunities.Inequality,Housing&Human Habitats,Roads&Highways,Economic Theory&Research,Rural Poverty Reduction

Image fusion techniqes for remote sensing applications

Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts. The aim of this survey paper is to describe three typical applications of data fusion in remote sensing. The first study case considers the problem of the Synthetic Aperture Radar (SAR) Interferometry, where a pair of antennas are used to obtain an elevation map of the observed scene; the second one refers to the fusion of multisensor and multitemporal (Landsat Thematic Mapper and SAR) images of the same site acquired at different times, by using neural networks; the third one presents a processor to fuse multifrequency, multipolarization and mutiresolution SAR images, based on wavelet transform and multiscale Kalman filter. Each study case presents also results achieved by the proposed techniques applied to real data

Cyberattacks: Does Physical Boundry Matter?

Information security issues are characterized with interdependence. Particularly, cyber criminals can easily cross national boundaries and exploit jurisdictional limitations between countries. Thus, whether cyber attacks are spatially autocorrelated is a strategic issue for government authorities and a tactic issue for insurance companies. Through an empirical study of cyber attacks across 62 countries during the period 2003-2007, we find little evidence on the spatial autocorrelation of cyber attacks at any week. However, after considering economic opportunity, IT infrastructure, international collaboration in enforcement and conventional crimes, we find strong evidence that cyber attacks were indeed spatially autocorrelated as they moved over time. The policy and managerial implication is that physical boundary should be an important factor in addressing strategic cyber attacks and their potential risks

Testing for Predictability in a Noninvertible ARMA Model

We develop likelihood-based tests for autocorrelation and predictability in a first order non- Gaussian and noninvertible ARMA model. Tests based on a special case of the general model, referred to as an all-pass model, are also obtained. Data generated by an all-pass process are uncorrelated but, in the non-Gaussian case, dependent and nonlinearly predictable. Therefore, in addition to autocorrelation the proposed tests can also be used to test for nonlinear predictability. This makes our tests different from their previous counterparts based on conventional invertible ARMA models. Unlike in the invertible case, our tests can also be derived by standard methods that lead to chi-squared or standard normal limiting distributions. A further convenience of the noninvertible ARMA model is that, to some extent, it can allow for conditional heteroskedasticity in the data which is useful when testing for predictability in economic and financial data. This is also illustrated by our empirical application to U.S. stock returns, where our tests indicate the presence of nonlinear predictability