USING RIDGE REGRESSION TO ESTIMATE SOME VARIABLES AFFECTING THE IRAQI STOCK EXCHANGE INDEX

Muhammed Al-Kassab, Mowafaq (2021) USING RIDGE REGRESSION TO ESTIMATE SOME VARIABLES AFFECTING THE IRAQI STOCK EXCHANGE INDEX. Advances and Applications in Statistics, 69 (2). pp. 191-202.

Text (Research Article) AS0690201911.pdf - Published Version Download (163kB)

Abstract

Ridge regression method is one of the most widely used methods for solving the multicollinearity problem as an alternative to the Ordinary Least Squares (OLS) when there is a collinearity between the explanatory variables, the presence of multicollinearity will produce unreliable result in the estimates of the parameters of OLS. Due to such a reason, this study aims to use the new version of the ridge regression to estimate the coefficients of some important economic variables as a real application. A numerical example of stock market index and macroeconomic variables in Iraq is employed using both methods aimed at to investigate the relationship of the variables in the presence of multicollinearity in the data set. The variables of interest are the index of Iraqi Stock Exchange (ISE), the Prices of Crude Oil (POC) and Gold (POG), and the Global Inflation Rates (GIR). Depending on the mean squares error criterion, the results show that the new version of the ridge regression procedure is able to estimate the model and produce reliable results by reducing the effect of multicollinearity in the data set.

Actions (login required)

View Item