Ridge Type Shrinkage Estimation of Seemingly Unrelated Regressions And Analytics of Economic and Financial Data from “Fragile Five” Countries

摘要

In this paper, we suggest improved estimation strategies based on preliminarily testand shrinkage principles in a seemingly unrelated regression model when explanatory variablesare affected by multicollinearity. To that end, we split the vector regression coefficient of eachequation into two parts: one includes the coefficient vector for the main effects, and the other isa vector for nuisance effects, which could be close to zero. Therefore, two competing models perequation of the system regression model are obtained: one includes all the regression of coefficients(full model); the other (sub model) includes only the coefficients of the main effects based on theauxiliary information. The preliminarily test estimation improves the estimation procedure if there isevidence that the vector of nuisance parameters does not provide a useful contribution to the model.The shrinkage estimation method shrinks the full model estimator in the direction of the sub-modelestimator. We conduct a Monte Carlo simulation study in order to examine the relative performanceof the suggested estimation strategies. More importantly, we apply our methodology based onthe preliminarily test and the shrinkage estimations to analyse economic data by investigating therelationship between foreign direct investment and several economic variables in the “Fragile Five”countries between 1983 and 2018.