Thesis (Ph.D.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Statistics and Econometrics

In the autoregressive distributed lag (ARDL) model, selection criteria are considering as an important issue. Based on these criteria, one determines the optimal order of the ARDL model. Moreover, if the dataset contains missing values, this will of course affect the optimal order of the ARDL model and also the estimation efficiency. Therefore, in this study, we propose to use three imputation methods (K. Nearest Neighbors (KNN), Predictive Mean Matching (PMM), Expectation Maximization (EM)) for handling the missing values and then get more efficient estimation of the model with the optimal order of lags depend on two model selection criteria which Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).In addition, we using combination methods that includes: Minimum, Maximum, Simple Average, Weighted Mean and Median methods to combine results of imputation methods and we demonstrate that the combination is preferable to improve the imputation data instead of using them individually through fitting ARDL model of all possible combinations. Finally, we compare all output of methods by MAE, MSE and RMSE criteria.Practically, we study and compare the performance of these methods based on a real dataset application and Mote Carlo simulation study to making comparison between the behavior of estimation different methods at different sample size and different percentage of missing was held based on two model selection AIC and BIC to compare between estimation methods. Also, we evaluate this model selection to choose the correct order of all ARDL models for all imputation and combination methods after handling missing data

Issued also as CD