1. Intestinal Parasite Infection Amongst Preschool-Age Children in te Democratic Republic o Congo: A Multilevel Analysis

    Ngianga II Kandala1,2, Ho Ming Yuen1

    1. Faculty of Medicine, Primary Care and Population Sciences, University of Southampton, UK.

    2. Faculty of Sciences, School of Health sciences & Social Work, University of Portsmouth, UK..

    Abstract: Intestinal parasite (IP) infections, such as hookworm infection, constitute a public health concern in less developed countries. Little is known about the epidemiology of IP infection in preschool-age children in the Democratic Republic of Congo (DRC). This study explored the epidemiology of IP infection in preschool-age children from the DRC and investigated whether the unobserved variations of this infection were between households or communities. Demographic Health Survey (DHS) collected data on preschool-age children with/without a record of a drug prescription for IP infection were used. Multilevel logistic regression analysis was applied due to the hierarchy nature of the data. The prevalence of IP infection was significantly different amongst the 11 regions and was higher in urban areas in the DRC. The random effect model showed that there were significant variations of IP infection due to unobserved household level factors. High prevalence of IP infection is a public health concern in the DRC and can remain a national health threat for as long as poverty persists.

    Keywords: intestinal parasite infection, ascaris lumbricoides, strongyloides stercoralis, larvae, multilevel logistic regression, random effect.

    Pages: 241 – 247 | Full PDF Paper
  2. Combining the Predictive Ability of Factorial Analysis and Transfer Functions for VAT Revenue Forecasting

    César Perez López and Camino González Vasco

    Instituto de Estudios Fiscales


    We propose a two-step methodology combining factorial analysis and a dynamic regression model to produce a valid forecast for VAT revenue. Instead of using final consumption expenditure as the only explanatory variable in a transfer function, we propose a set of indicators covering different areas of the economy (General, Construction, Labour Market and Service Activity Indicators). The idea is to enforce parsimony and to avoid multicollinearity with little information loss by performing principal component analysis and regressing not on the full set of indicators but rather on the first two principal components.

    We apply the proposed method to quarterly data beginning in 1995 and ending in 2014, providing out of sample estimations for the four quarters of 2015.

    Keywords: Principal Components Regression, VAT forecasting, forecast combination, generated regressors.

    Pages: 248 – 269 | Full PDF Paper