Tag Archives: Quantile Regression.

Panel Quantile Regression with Penalized Fixed Effects and Correlated Random Effects (Published)

The crucial difficult in estimating covariates effects in panel analysis, is when there is correlation of the unobserved heterogeneity with the covariates and the fact that estimation of conditional mean effects seems potentially limited. Much consideration has not really been given to curb this difficulty especially in the context of quantile regression. In this work Panel Quantile regression was applied in other to investigate the correlated random effects (i.e. effects of the correlation between the covariates and the unobserved heterogeneity) and the penalized fixed effect (i.e. effects after eliminating the unobserved heterogeneity). We employed the use of real data and simulated data sets at different sample sizes. The results showed significant correlated random effect for both covariates in the real data only at the low level (0.25 quantile), but when the unobserved heterogeneity was eliminated both variables were seen to significantly affect the response at the 0.25, 0.5 and 0.75 quantiles of its distribution. The simulation study also confirmed it. We also noticed that as the sample size increases in the simulation study the correlated random effects become insignificant, while the penalized fixed effect and quantile regression effects were evidently significant at all quantiles considered. Comparison of these methods showed that the penalized fixed effect had the least value for both MSE and RMSE. This analysis was done in R environment using the quantreg package.

Keywords: Panel Data, Quantile Regression., correlated random effect, penalized fixed effect., unobserved heterogeneity

Improving Welfare: Foreign Aid versus Government Social Spending, Evidence from African Countries Using a Quantile Regression (Published)

The study uses a quantile regression to investigate the role of government actions to enhance welfare. Instead of using the Human Development index as a broad indicator of welfare, the analysis focuses on life expectancy at birth, which is more specific and pertinent for the case of less advanced economies. In addition to life expectancy, infant mortality rate is used as additional indicator. To avoid a bias in the estimates generated by a double count of the variable aid, the residual from the regression of social spending on aid is used, instead of the variable social spending itself, as some portions of government social spending are financed by aid. Results reveal that aid does not directly affect welfare. On the opposite, government social spending contributes to increase life expectancy, reduces infant mortality, and therefore plays an important role in the improvement of welfare. In addition, the impact of social spending on welfare appears stronger in the countries with poor welfare indicators, than the countries with relatively better welfare indicators.

Keywords: Aid, Quantile Regression., Social Spending, Welfare