Heteroscedasticity and multicollinearity are serious problems when they exist in econometrics data. These problems exist as a result of violating the assumptions of equal variance between the error terms and that of i...Heteroscedasticity and multicollinearity are serious problems when they exist in econometrics data. These problems exist as a result of violating the assumptions of equal variance between the error terms and that of independence between the explanatory variables of the model. With these assumption violations, Ordinary Least Square Estimator</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">(OLS) will not give best linear unbiased, efficient and consistent estimator. In practice, there are several structures of heteroscedasticity and several methods of heteroscedasticity detection. For better estimation result, best heteroscedasticity detection methods must be determined for any structure of heteroscedasticity in the presence of multicollinearity between the explanatory variables of the model. In this paper we examine the effects of multicollinearity on type I error rates of some methods of heteroscedasticity detection in linear regression model in other to determine the best method of heteroscedasticity detection to use when both problems exist in the model. Nine heteroscedasticity detection methods were considered with seven heteroscedasticity structures. Simulation study was done via a Monte Carlo experiment on a multiple linear regression model with 3 explanatory variables. This experiment was conducted 1000 times with linear model parameters of </span><span style="white-space:nowrap;"><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">0</span></sub><span style="font-family:Verdana;"> = 4 , </span><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">1</span></sub><span style="font-family:Verdana;"> = 0.4 , </span><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">2</span></sub><span style="font-family:Verdana;">= 1.5</span></span></span><span style="font-family:""><span style="font-family:Verdana;"> and </span><em style="font-family:""><span style="font-family:Verdana;">β</span><span style="font-family:Verdana;"><sub>3 </sub></span></em><span style="font-family:Verdana;">= 3.6</span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">Five (5) </span><span style="font-family:Verdana;"></span><span style="font-family:Verdana;">levels of</span><span style="white-space:nowrap;font-family:Verdana;"> </span><span style="font-family:Verdana;"></span><span style="font-family:Verdana;">mulicollinearity </span></span><span style="font-family:Verdana;">are </span><span style="font-family:Verdana;">with seven</span><span style="font-family:""> </span><span style="font-family:Verdana;">(7) different sample sizes. The method’s performances were compared with the aids of set confidence interval (C.I</span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;">) criterion. Results showed that whenever multicollinearity exists in the model with any forms of heteroscedasticity structures, Breusch-Godfrey (BG) test is the best method to determine the existence of heteroscedasticity at all chosen levels of significance.展开更多
The development of many estimators of parameters of linear regression model is traceable to non-validity of the assumptions under which the model is formulated, especially when applied to real life situation. This not...The development of many estimators of parameters of linear regression model is traceable to non-validity of the assumptions under which the model is formulated, especially when applied to real life situation. This notwithstanding, regression analysis may aim at prediction. Consequently, this paper examines the performances of the Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis in prediction of linear regression model under the joint violations of the assumption of non-stochastic regressors, independent regressors and error terms. With correlated stochastic normal variables as regressors and autocorrelated error terms, Monte-Carlo experiments were conducted and the study further identifies the best estimator that can be used for prediction purpose by adopting the goodness of fit statistics of the estimators. From the results, it is observed that the performances of COR at each level of correlation (multicollinearity) and that of ML, especially when the sample size is large, over the levels of autocorrelation have a convex-like pattern while that of OLS and PC are concave-like. Also, as the levels of multicollinearity increase, the estimators, except the PC estimators when multicollinearity is negative, rapidly perform better over the levels autocorrelation. The COR and ML estimators are generally best for prediction in the presence of multicollinearity and autocorrelated error terms. However, at low levels of autocorrelation, the OLS estimator is either best or competes consistently with the best estimator, while the PC estimator is either best or competes with the best when multicollinearity level is high(λ>0.8 or λ-0.49).展开更多
In regression analysis, data sets often contain unusual observations called outliers. Detecting these unusual observations is an important aspect of model building in that they have to be diagnosed so as to ascertain ...In regression analysis, data sets often contain unusual observations called outliers. Detecting these unusual observations is an important aspect of model building in that they have to be diagnosed so as to ascertain whether they are influential or not. Different influential statistics including Cook’s Distance, Welsch-Kuh distance and DFBETAS have been proposed. Based on these influential statistics, the use of some robust estimators MM, Least trimmed square (LTS) and S is proposed and considered as alternative to influential statistics based on the robust estimator M and the ordinary least square (OLS). The statistics based on these estimators were applied into three set of data and the root mean square error (RMSE) was used as a criterion to compare the estimators. Generally, influential measures are mostly efficient with M or MM robust estimators.展开更多
The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally.Recently,COVID-19 a new kind of coronavirus emerge from Wuhan city in Ch...The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally.Recently,COVID-19 a new kind of coronavirus emerge from Wuhan city in China and was declared a pandemic by the World Health Organization.There has been a reported case of about 8622985 with global death of 457,355 as of 15.05 GMT,June 19,2020.South-Africa,Egypt,Nigeria and Ghana are the most affected African countries with this outbreak.Thus,there is a need to monitor and predict COVID-19 prevalence in this region for effective control and management.Different statistical tools and time series model such as the linear regression model and autoregressive integrated moving average(ARIMA)models have been applied for disease prevalence/incidence prediction in different diseases outbreak.However,in this study,we adopted the ARIMA model to forecast the trend of COVID-19 prevalence in the aforementioned African countries.The datasets examined in this analysis spanned from February 21,2020,to June 16,2020,and was extracted from theWorld Health Organization website.ARIMA models with minimum Akaike information criterion correction(AICc)and statistically significant parameters were selected as the best models.Accordingly,the ARIMA(0,2,3),ARIMA(0,1,1),ARIMA(3,1,0)and ARIMA(0,1,2)models were chosen as the best models for SA,Nigeria,and Ghana and Egypt,respectively.Forecasting was made based on the best models.It is noteworthy to claim that the ARIMA models are appropriate for predicting the prevalence of COVID-19.We noticed a form of exponential growth in the trend of this virus in Africa in the days to come.Thus,the government and health authorities should pay attention to the pattern of COVID-19 in Africa.Necessary plans and precautions should be put in place to curb this pandemic in Africa.展开更多
文摘Heteroscedasticity and multicollinearity are serious problems when they exist in econometrics data. These problems exist as a result of violating the assumptions of equal variance between the error terms and that of independence between the explanatory variables of the model. With these assumption violations, Ordinary Least Square Estimator</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">(OLS) will not give best linear unbiased, efficient and consistent estimator. In practice, there are several structures of heteroscedasticity and several methods of heteroscedasticity detection. For better estimation result, best heteroscedasticity detection methods must be determined for any structure of heteroscedasticity in the presence of multicollinearity between the explanatory variables of the model. In this paper we examine the effects of multicollinearity on type I error rates of some methods of heteroscedasticity detection in linear regression model in other to determine the best method of heteroscedasticity detection to use when both problems exist in the model. Nine heteroscedasticity detection methods were considered with seven heteroscedasticity structures. Simulation study was done via a Monte Carlo experiment on a multiple linear regression model with 3 explanatory variables. This experiment was conducted 1000 times with linear model parameters of </span><span style="white-space:nowrap;"><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">0</span></sub><span style="font-family:Verdana;"> = 4 , </span><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">1</span></sub><span style="font-family:Verdana;"> = 0.4 , </span><em><span style="font-family:Verdana;">β</span></em><sub><span style="font-family:Verdana;">2</span></sub><span style="font-family:Verdana;">= 1.5</span></span></span><span style="font-family:""><span style="font-family:Verdana;"> and </span><em style="font-family:""><span style="font-family:Verdana;">β</span><span style="font-family:Verdana;"><sub>3 </sub></span></em><span style="font-family:Verdana;">= 3.6</span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">Five (5) </span><span style="font-family:Verdana;"></span><span style="font-family:Verdana;">levels of</span><span style="white-space:nowrap;font-family:Verdana;"> </span><span style="font-family:Verdana;"></span><span style="font-family:Verdana;">mulicollinearity </span></span><span style="font-family:Verdana;">are </span><span style="font-family:Verdana;">with seven</span><span style="font-family:""> </span><span style="font-family:Verdana;">(7) different sample sizes. The method’s performances were compared with the aids of set confidence interval (C.I</span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;">) criterion. Results showed that whenever multicollinearity exists in the model with any forms of heteroscedasticity structures, Breusch-Godfrey (BG) test is the best method to determine the existence of heteroscedasticity at all chosen levels of significance.
文摘The development of many estimators of parameters of linear regression model is traceable to non-validity of the assumptions under which the model is formulated, especially when applied to real life situation. This notwithstanding, regression analysis may aim at prediction. Consequently, this paper examines the performances of the Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis in prediction of linear regression model under the joint violations of the assumption of non-stochastic regressors, independent regressors and error terms. With correlated stochastic normal variables as regressors and autocorrelated error terms, Monte-Carlo experiments were conducted and the study further identifies the best estimator that can be used for prediction purpose by adopting the goodness of fit statistics of the estimators. From the results, it is observed that the performances of COR at each level of correlation (multicollinearity) and that of ML, especially when the sample size is large, over the levels of autocorrelation have a convex-like pattern while that of OLS and PC are concave-like. Also, as the levels of multicollinearity increase, the estimators, except the PC estimators when multicollinearity is negative, rapidly perform better over the levels autocorrelation. The COR and ML estimators are generally best for prediction in the presence of multicollinearity and autocorrelated error terms. However, at low levels of autocorrelation, the OLS estimator is either best or competes consistently with the best estimator, while the PC estimator is either best or competes with the best when multicollinearity level is high(λ>0.8 or λ-0.49).
文摘In regression analysis, data sets often contain unusual observations called outliers. Detecting these unusual observations is an important aspect of model building in that they have to be diagnosed so as to ascertain whether they are influential or not. Different influential statistics including Cook’s Distance, Welsch-Kuh distance and DFBETAS have been proposed. Based on these influential statistics, the use of some robust estimators MM, Least trimmed square (LTS) and S is proposed and considered as alternative to influential statistics based on the robust estimator M and the ordinary least square (OLS). The statistics based on these estimators were applied into three set of data and the root mean square error (RMSE) was used as a criterion to compare the estimators. Generally, influential measures are mostly efficient with M or MM robust estimators.
文摘The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally.Recently,COVID-19 a new kind of coronavirus emerge from Wuhan city in China and was declared a pandemic by the World Health Organization.There has been a reported case of about 8622985 with global death of 457,355 as of 15.05 GMT,June 19,2020.South-Africa,Egypt,Nigeria and Ghana are the most affected African countries with this outbreak.Thus,there is a need to monitor and predict COVID-19 prevalence in this region for effective control and management.Different statistical tools and time series model such as the linear regression model and autoregressive integrated moving average(ARIMA)models have been applied for disease prevalence/incidence prediction in different diseases outbreak.However,in this study,we adopted the ARIMA model to forecast the trend of COVID-19 prevalence in the aforementioned African countries.The datasets examined in this analysis spanned from February 21,2020,to June 16,2020,and was extracted from theWorld Health Organization website.ARIMA models with minimum Akaike information criterion correction(AICc)and statistically significant parameters were selected as the best models.Accordingly,the ARIMA(0,2,3),ARIMA(0,1,1),ARIMA(3,1,0)and ARIMA(0,1,2)models were chosen as the best models for SA,Nigeria,and Ghana and Egypt,respectively.Forecasting was made based on the best models.It is noteworthy to claim that the ARIMA models are appropriate for predicting the prevalence of COVID-19.We noticed a form of exponential growth in the trend of this virus in Africa in the days to come.Thus,the government and health authorities should pay attention to the pattern of COVID-19 in Africa.Necessary plans and precautions should be put in place to curb this pandemic in Africa.
