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Model Selection Methods in Autoregressive Distributed Lag Models with Missing Data / Fatimah Ali Ahmed Alteer ; Supervised Mohamed Reda Abonazel , Ahmed Amin Elsheikh

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Fatimah Ali Ahmed Alteer, 2022Description: 117 P . : charts ; 35cmOther title:
  • طرق اختيار النموذج في نماذج الانحدار الذاتي لفترات الإبطاء الموزعة في حالة وجود قيم مفقود ة [Added title page title]
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Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Statistics and Econometrics Summary: 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
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.04.Ph.D.2022.Fa.M (Browse shelf(Opens below)) Not for loan 01010110085638000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.04.Ph.D.2022.Fa.M (Browse shelf(Opens below)) 85638.CD Not for loan 01020110085638000

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

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