A-1A Supplemental Security Income Program, October 2005

A-1A - Supplemental Security Income Program - October 2005

A-1A Supplemental Security Income Program, November 2005

A-A1 - Supplemental Security Income Program - November 2005

Wavelet analysis residual kriging vs. neural network residual kriging

This paper deals with the problem of spatial data mapping. A new method based on wavelet interpolation and geostatistical prediction (kriging) is proposed. The method - wavelet analysis residual kriging (WARK) - is developed in order to assess the problems rising for highly variable data in presence of spatial trends. In these cases stationary prediction models have very limited application. Wavelet analysis is used to model large-scale structures and kriging of the remaining residuals focuses on small-scale peculiarities. WARK is able to model spatial pattern which features multiscale structure. In the present work WARK is applied to the rainfall data and the results of validation are compared with the ones obtained from neural network residual kriging (NNRK). NNRK is also a residual-based method, which uses artificial neural network to model large-scale non-linear trends. The comparison of the results demonstrates the high quality performance of WARK in predicting hot spots, reproducing global statistical characteristics of the distribution and spatial correlation structure.

A-1A Supplemental Security Income Program, January 2006

Supplemental Security Income Program January 2006

A-1A Supplemental Security Income Program, December 2005

Supplemental Security Income Program December 2005

A-1A Supplemental Security Income Program, February 2006

Supplemental Security Income Program, February 2006

A-1A Supplemental Security Income Program, April 2006

Supplemental Security Income Program, April 2006

A-1A Supplemental Security Income Program, March 2006

A-1A Supplemental Security Income Program, March 2006

Predictable Life-Cycle Shocks, Income Risk and Consumption Inequality

Was the increase in income inequality in the US due to permanent shocks or merely to an increase in the variance of transitory shocks? The implications for consumption and welfare depend crucially on the answer to this question. We use CEX repeated cross-section data on consumption and income to decompose idiosyncratic changes in income into predictable life-cycle changes, transitory and permanent shocks and estimate the contribution of each to total inequality. Our model fits the joint evolution of consumption and income inequality well and delivers two main results. First, we find that permanent changes in income explain all of the increase in inequality in the 1980s and 90s. Second, we reconcile this finding with the fact that consumption inequality did not increase much over this period. Our results support the view that many permanent changes in income are predictable for consumers, even if they look unpredictable to the econometrician, consistent with models of heterogeneous income profiles.

A-1A Supplemental Security Income Program, July 2006

Supplemental Security Income Program, May 2006

A-1A Supplemental Security Income Program, May 2006

Supplemental Security Income Program, May 2006

A-1A Supplemental Security Income Program, June 2006

Supplemental Security Income Program, May 2006

A-1A Supplemental Security Income Program, August 2006

A-1A Supplemental Security Income Program, August 2006

A-1A Supplemental Security Income Program, September 2006

A-1A Supplemental Security Income Program produced by the Department of Human Services

A-1A Supplemental Security Income Program, June 2002

Monthly report from Iowa Department of Human Services on income.