Problem of omitted variable in regression model specification

In many non-experimental studies, the analyst may not have access to all relevant variables, and does not include these variables into the model and omits them. To omit some variables that affect the dependent variable from the model may cause omitted variables bias. In this thesis, it is aimed to investigate the omitted variable bias, its importance, reasons, and consequences and to research the methods for dealing with omitted variable bias and RESET test which is a method for detecting omitted variable(s).In this study, a simulation was performed by using the programs written in Minitab which is a statistical software package. Three types of populations with 1000 observations which varied depending on the correlations between the variables were generated and random samples were drawn from these populations. Though the true model had three independent variables, the models were estimated by omitting one and then two independent variables for each sample. 10,000 repetitions were generated for each of sample sizes. Therefore when correlations were changed and the number of omitted variables was increased, the effects of omitted variable bias were investigated. The amount of bias, the estimated coefficients, coefficients of determination and the adjusted coefficients of determination, standard deviations of the estimated coefficients were computed for every model and F statistics were also computed for applying RESET test and they were all compared for each population. Moreover, by increasing the sample size, it was investigated whether the effects of omitted variable bias were changed depending on sample size.Keywords: Regression analysis, model specification error, omitted variable bias, RESET test