European Journal of Statistics and Probability (EJSP)

EA Journals

Panel Data

Non-Stationarity in U.S. All-Cause Mortality Rates: Probing The Usefulness of the Idiom ‘Excess Death’ (Published)

Borrowing a bit from the author Yuval Harari − death is a chaos not particularly influenced by predictions made about it. This paper examines the non-stationarity of aggregate U.S. age standardized all cause mortality rates over the period 1968-2021. Both univariate and state-level panel unit root tests confirm that the underlying stochastic process generating U.S. mortality rates changes over time. Examining non-stationary death in the aggregate, controlling for age and population, establishes proper context to scrutinize the usefulness of the idiom ‘excess death’.

Keywords: Panel Data, Unit Root, age standardized death rates, excess death

Application of Least Square Dummy Variable (LSDV) in Estimation of Compensation of Employee (Published)

This research was conducted to estimate compensation of employee using least square dummy variable (LSDV) regression model. The data used in this work were secondary data sourced from National Bureau of Statistics (NBS) from 1981 to 2006. The variables considered were compensation of employee as the dependent variable, fixed capital, price of goods, tax and surplus as the independent variables. The data were analyzed using (STATA 13). The results obtained revealed that F-value of 3874.05 was statistically high suggesting the overall model was good fitted. The R2 -value 0.9989 was also high which indicated that 99.89% of the total variation was accounted for by the independent variables included in model while the remaining 0.11% unexplained was accounted for by the white noise. Again, all the differential intercept coefficients have negative signs. Also, several differential slope coefficients have negative signs which implied that they were negatively related to compensation. Again, the result revealed that compensation is not statistically significantly related to fixed capital, price, tax and surplus. However, none of the differential slope coefficients is statistically significant. Of all the three differential intercept coefficients only  was statistically significant. Since none of the differential slope coefficients was statistically significant, it concluded that the differential slope coefficients are not different from the slope coefficient of the base/comparison group (power sector.

Keywords: Compensation, Dummy Variable, Panel Data, fixed effect and employee

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

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