Partially Adaptive Estimation via 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.

Testing for Shape Invariance of Semiparametric Equivalence Scales

Within a semiparametric framework we propose a test of shape invariance of Engel curves, which is a necessary condition for base independence. Using Canadian family expenditure data for 1996 we reject shape invariance for the fuel and clothing share equations.

The absolute health income hypothesis revisited: A Semiparametric Quantile Regression Approach.

This paper uses the 1998-99 Canadian National Population Health Survey (NPHS) data to examine the health-income relationship that underlies the absolute income hypothesis. To allow for nonlinearity and data heterogeneity, we use a partially linear semiparametric quantile regression model. The “absolute income hypothesis” is partially true; the negative aging effects appear more pronounced for the illhealthy population than for the healthy population and when annual income is below 40,000 Canadian dollars.Absolute income hypothesis · Partially linear quantile

The Absolute Health Income Hypothesis Revisited : A Semiparametric Quantile Regression Approach

This paper uses the 1998-99 Canadian National Population Health Survey (NPHS) data to examine the health-income relationship that underlies the absolute income hypothesis. To allow for nonlinearity and data heterogeneity, we use a partially linear semiparametric quantile regression model. Among more than dozen of socioeconomic variables, we find that family income, age and the food security status are the most important factors in explaining an individual's overall functional health. The "absolute income hypothesis" is partially true; the negative aging effects appear more pronounced for the ill-healthy population than for the healthy population and when annual income is below 40,000 Canadian dollars.

Nominal Wage Rigidity: Non-Parametric Tests Based on Union Data for Canada

We study the wage-change distributions in union contractsreached in Canada between 1976-1999. We use non-parametric tests to check for nominal wage rigidity and find that it is present during low inflation periods.Nominal wage rigidity, non-parametric tests

An algorithm for censored quantile regressions

In this paper, we present an algorithm for Censored Quantile Regression (CQR) estimation problems. Our method permits CQR estimation problems to be solved more efficiently and reliably than was hitherto possible. It guarantees to find a high quality estimator in O(k×n²) operations with k regressors and n observations, which is much less than the existing algorithms for CQR problems.Cencored Quantile Regression

Nonparametric Econometric Methods and Application

The present Special Issue collects a number of new contributions both at the theoretical level and in terms of applications in the areas of nonparametric and semiparametric econometric methods. In particular, this collection of papers that cover areas such as developments in local smoothing techniques, splines, series estimators, and wavelets will add to the existing rich literature on these subjects and enhance our ability to use data to test economic hypotheses in a variety of fields, such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth, to name a few

Love thy Neighbor, Love thy Kin: Voting Biases in the Eurovision Song Contest

The Eurovision Song Contest provides a setting where Europeans can express their sentiments about other countries without regard to political sensitivities. Analyzing voting data from the 25 contests between 1981-2005, we find strong evidence for the existence of clusters of countries that systematically exchange votes regardless of the quality of their entries. Cultural, geographic, economic and political factors are important determinants of points awarded from one country to another. Other non-quality related factors such as order of appearance, the language of the song and the gender of the performing artist, are also important. There is also a substantial host country effectEurovision, Social Network, Games of Trust

Economic Policies and the Impact of Natural Disasters on Economic Growth: A Threshold Regression Approach

This paper investigates the impact of natural disasters on the long-term macroeconomic performance of a country. More specifically, we want to see whether the impact of natural disasters on economic growth is uniform across countries or it is differentiated according to the macroeconomic policy environment and other structural characteristics of the countries at hand. In order to test this empirically we use the Threshold Regression (TR) approach of Hansen (2000) and data from 90 countries over the period of 1970 to 2001. Our analysis reveals several interesting patterns such as: countries with higher per capita income, higher government spending, higher degree of openness to trade, less fiscal imbalances and greater financial stability are better able to withstand the disaster shock and further prevent its impact on long-term economic growth.Natural Disasters, Economic Policies, Economic Growth, Threshold Regression

FOREIGN DIRECT INVESTMENT, HUMAN CAPITAL AND NONLINEARITIES IN ECONOMIC GROWTH

This paper examines the effect of FDI on the process of economic growth by allowing the impact to differ both across each country and also across each time period. We apply non-parametric techniques taking into account the previously documented nonlinear effects of initial income and human capital on economic growth. We use a wide range of countries, both developed and developing in order to be able to distinguish potential differential effects between the two groups. Our findings suggest that FDI inflows have a moderately nonlinear effect on growth and that the human capital nonlinear effect in the presence of FDI inflows is similar to the one found elsewhere in the relevant literature. Classification-JEL: O47cross country growth regressions, FDI, human capital, semi-parametric additive model