Ximing Wu

Calculation of maximum entropy densities with application to income distribution

A horizontal synchronizing signal is applied as a reference signal and a voltage-controlled oscillator outputs a display clock signal on the basis of the frequency of the horizontal synchronizing signal. The frequency of the display clock signal is frequency-divided in accordance with a frequency-dividing value selected from among a plurality of frequency-dividing signals stored in advance, the difference in frequency between the frequency-divided display clock signal and the horizontal synchronizing signal and the phase difference between them are obtained by a phase comparator, and the frequency of the signal outputted by the voltage-controlled oscillator is decided in dependence upon the frequency difference. In an interval in which a vertical synchronizing signal turns off and the frequency of the horizontal synchronizing signal fluctuates, the reference signal and the horizontal synchronizing signal input to the phase comparator are held fixed to prevent a fluctuation in the outputted display clock.

CHINA'S INCOME DISTRIBUTION, 1985-2001

We employ a new method to estimate Chinas income distribution using publicly available interval summary statistics. We examine rural, urban, and overall income distributions from 1985 to 2001. We show how the distributions change directly, and we examine trends in inequality. Using an intertemporal decomposition of aggregate inequality, we determine that increases in inequality within rural and urban sectors and the growing rural-urban income gap have been equally responsible for the growth in overall inequality over the last two decades. However, the rural-urban gap has played an increasingly important role in recent years. We also show that the urban consumption inequality rose considerably.

Comparing Open-Ended Choice Experiments and Experimental Auctions: An Application to Golden Rice

We use two experimental valuation methods to estimate consumer demand for genetically modified golden rice. The first is an open-ended choice experiment (OECE) where participants name the quantities of golden rice and conventional rice demanded at each of several price combinations, one of which will be randomly chosen as binding. This allows us to estimate market demand by aggregating demand across participants. This estimate of market demand also allows us to estimate own-price elasticity and consumer surplus for golden rice. Comparing willingness-to-pay (WTP) estimates from the OECE with those from a uniform-price auction, we find that OECE WTP estimates exhibit less affiliation across rounds, and the effects of positive and negative information under the OECE are more consistent with prior expectations and existing studies. We also find that, while auction WTP estimates more than double across five rounds, OECE WTP estimates are stable across rounds and are always roughly equal to those from the final auction round.

China's Income Distribution over Time: Reasons for Rising Inequality

We estimate Chinas rural, urban and overall income distributions using grouped data from 1985-2001. We show how the distributions evolve as well as examine trends in welfare indices. We find the growing rural-urban income gap and increases in inequality within either sector have been equally responsible for overall inequality growth.

Partially Adaptive Estimation via the Maximum Entropy Densities

We propose a partially adaptive estimator based on information theoretic maximum entropy estimates of the error distribution. The maximum entropy (maxent) densities have simple yet flexible functional forms to nest most of the mathematical distributions. Unlike the nonparametric fully adaptive estimators, our parametric estimators do not involve choosing a bandwidth or trimming, and only require estimating a small number of nuisance parameters, which is desirable when the sample size is small. Monte Carlo simulations suggest that the proposed estimators fare well with non-normal error distributions. When the errors are normal, the efficiency loss due to redundant nuisance parameters is negligible as the proposed error densities nest the normal. The proposed partially adaptive estimator compares favorably with existing methods, especially when the sample size is small. We apply the estimator to a bio-pharmaceutical example and a stochastic frontier model.

Climate Change and Vector-borne Diseases: An Economic Impact Analysis of Malaria in Africa

A semi-parametric econometric model is used to study the relationship between malaria cases and climatic factors in 25 African countries. Results show that a marginal change in temperature and precipitation levels would lead to a significant change in the number of malaria cases for most countries by the end of the century. Consistent with the existing biophysical malaria model results, the projected effects of climate change are mixed. Our model projects that some countries will see an increase in malaria cases but others will see a decrease. We estimate projected malaria inpatient and outpatient treatment costs as a proportion of annual 2000 health expenditures per 1,000 people. We found that even under minimal climate change scenario, some countries may see their inpatient treatment cost of malaria increase more than 20%.

Effects of Government Policies on Urban and Rural Income Inequality

We use three conventional inequality indices-the Gini, the coefficient of variation of income, and the relative mean deviation of income-and the Atkinson index to examine the effect of income tax rates, the minimum wage, and all the major government welfare and transfer programs on the evolution of income inequality for rural and urban areas by state from 1981 to 1997. We find that these programs have qualitatively similar but quantitatively different effects on urban and rural areas. Most importantly, taxes are more effective in redistributing income in urban than in rural areas, while welfare and other government transfer programs play a larger role in rural areas. Copyright

Exponential Series Estimator of Multivariate Densities

We present an Exponential Series Estimator (ESE) of multivariate densities, which has an appealing information-theoretic interpretation. For a d dimensional random variable with density p0, the ESE takes the form , where are some real-valued, linearly independent functions defined on the support of p0. We derive the convergence rate of the ESE in terms of the Kullback-Leibler Information Criterion, the integrated squared error and some other metrics. We also derive its almost sure uniform convergence rate. We then establish the asymptotic normality of . We undertake two sets of Monte Carlo experiments. The first experiment examines the ESE performance using mixtures of multivariate normal densities. The second estimates copula density functions. The results demonstrate the efficacy of the ESE. An empirical application on the joint distributions of stock returns is presented.

Exponential series estimation of empirical copulas with application to financial returns

Knowledge of the dependence structure between financial assets is crucial to improve the performance in financial risk management. It is known that the copula completely summarizes the dependence structure among multiple variables. We propose a multivariate exponential series estimator (ESE) to estimate copula densities nonparametrically. The ESE has an appealing information-theoretic interpretation and attains the optimal rate of convergence for nonparametric density estimations in Stone (1982). More importantly, it overcomes the boundary bias of conventional nonparametric copula estimators. Our extensive Monte Carlo studies show the proposed estimator outperforms the kernel and the log-spline estimators in copula estimation. It also demonstrates that two-step density estimation through an ESE copula often outperforms direct estimation of joint densities. Finally, the ESE copula provides superior estimates of tail dependence compared to the empirical tail index coefficient. An empirical examination of the Asian financial markets using the proposed method is provided.

The Effect of Food-Away-from-Home and Food-at-Home Expenditures on Obesity Rates: A State-Level Analysis

Using state-level data from the Behavioral Risk Factor Surveillance System, we investigate the effects of household food-away-from-home and food-at-home expenditures on overweight rates, obesity rates, and combined rates. Our random effects model estimates suggest that food-away-from-home expenditures are positively related to obesity and combined rates, while food-at-home expenditures are negatively related to obesity and combined rates. However, the magnitudes of these effects, while statistically significant, are relatively small. Both food-at-home and food-away-from-home expenditures do not significantly influence overweight rates.