Search

This paper considers the partially linear model when the explanatory variables are highly correlated as well as the dataset contains outliers. We propose new robust biased estimators for this model under these conditions. The proposed estimators combine least trimmed squares and ridge estimations, based on the spline partial residuals technique. The performance of the proposed estimators and the Speckman-spline estimator has been examined by a Monte Carlo simulation study. The results indicated that the proposed estimators are more efficient and reliable than the Speckman-spline estimator. Â

This paper investigated the appropriate specifications of Engel curves for non-food expenditure categories and estimated the deprivation indices of non-food needs in rural areas using a semi parametric examination of the presence of saturation points. The study used the extended partial linear model (EPLM) and adopted two estimation methods—the double residual estimator and differencing estimator—to obtain flexible shapes across different expenditure categories and estimate equivalence scales. We drew on data of the Egyptian Household Income, Expenditure, and Consumption Survey (HIEC). Our paper provides empirical evidence that the rankings of most non-food expenditure categories is of rank three at most. Rural households showed high economies of scale in non-food consumption, with child’s needs accounting for only 10% of adult’s non-food needs. Based on semi-parametrically estimated consumption behavior, the tendency of non-food expenditure categories to saturate did not emerge. While based on parametrically estimated consumption behavior, rural areas exhibited higher deprivation indices in terms of health and education expenditure categories, which indicates the need to design specific programs economically targeting such vulnerable households.

To revisit a study on the prevalence of Down Syndrome (DS) in the Fylde of Lancashire and ionizing radiation, using new birth data that allow better control for maternal age. Associations between ionizing radiation and DS prevalence have been controversial. Some studies link temporal variation in prevalence to ionizing radiation; others do not. Cases were ascertained in a prospective survey of major congenital malformations among residents in the Fylde of Lancashire between 1957 and 1991. New data on the birth maternal age distribution in the Fylde were obtained from the Office for National Statistics for most of the study period. Temporal clusters in prevalence rates were again detected using the Poisson log linear models used to measure the association between prevalence and ionizing radiation from atomic fallout. Significant effects of fallout radiation were found when maternal age was both controlled and not controlled for. Two DS prevalence peaks occurred during the study period. The first started in June 1958 and lasted for five months. The second lasted longer, starting in October 1962 and ending in August 1964. Although these peaks corresponded to peaks in fallout radiation dosage, they were not significant.

In analysing panel data in which the dependent variable is a binary choice variable taking values 1 or 0 for success or failure respectively, it is feasible to consider the conditional probabilities of the dependent variable. Under strict exogeneity, this conditional probability equals the expected value of the dependent variable. This treatment calls for a nonlinear function which will ensure that the conditional probability lies between 0 and 1, and such functions yield the probit model and the logit model. This paper discusses an estimation of nonlinear logit panel data model with fixed effects. There are two main estimators for such models: 'unconditional maximum likelihood' and 'conditional maximum likelihood'. Application study was designed to determine the most important factors affecting delayed completion of adjuvant chemotherapy among patients with breast cancer and adjuvant chemotherapy improvement outcomes of patients with breast cancer to determine the relationship between time to chemotherapy and outcome according to breast cancer. The optimal timing from beginning to the end of chemotherapy is known (three months). We hypothesized that prolonged time to chemotherapy would be associated with adverse outcomes. Delayed time to chemotherapy was defined as more three months from the first dose and the last dose of chemotherapy. The study results show that the conditional fixed effects logit estimator is efficient and better than the unconditional pooling and unconditional fixed effects logit estimators. And we find that the most important factors affecting delayed completion of adjuvant chemotherapy among patients are haemoglobin, platelets, and alanine transaminase.

This paper considers the count regression models in case of the dataset contains overdispersion and outliers. Seven robust and non-robust estimators are provided for four count regression (Poisson, negative binomial (NB), zero inflated Poisson (ZIP) and zero inflated negative binomial (ZINB)) models. The non-robust estimators were obtained by applying the maximum likelihood estimation on the four count models. While two robust estimators were obtained by applying the M-estimation on the Poisson and NB models (MP and MNB estimators), and the third robust estimator is the quantile regression of the count model (QRC estimator). Simulation study and empirical application were conducted to evaluate the performance and the efficiency for the robust and non-robust estimators of the four count regression models. The results showed that, in general, all robust estimators gave better performance than all non-robust estimators if the model contains outliers. And the QRC estimator reforms well even if the percent of the outlier values up to 25% when the sample size is large, dispersion value is small (less than or equal one). While when the dispersion value more than one, the MNB estimator is the efficient. The results of our application, which based on German health survey data in 1998, indicate that the significant variable that effect on the number of visits to doctor is the patient's condition (bad health or not in bad health), and the QRC estimator is the best for this data.

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

This paper presents a robust version of partial residuals technique to estimate parametric and nonparametric components in semiparametric partially linear model. The robust estimation of the parame...

Multicollinearity in multinomial logistic regression affects negatively on the variance of the maximum likelihood estimator. That leads to inflated confidence intervals and theoretically important variables become insignificant in testing hypotheses. In this paper, Liu-type estimator is proposed that has smaller total mean squared error than the maximum likelihood estimator. The proposed estimator is a general estimator which includes other biased estimators such as Liu estimator and ridge estimator as special cases. Simulation studies and an application are given to evaluate the performance of our estimator. The results indicate that the proposed estimator is more efficient and reliable than the conventional estimators.

This article considers first-order autoregressive panel model that is a simple model for dynamic panel data (DPD) models. The generalized method of moments (GMM) gives efficient estimators for these models. This efficiency is affected by the choice of the weighting matrix that has been used in GMM estimation. The non-optimal weighting matrices have been used in the conventional GMM estimators. This led to a loss of efficiency. Therefore, we present new GMM estimators based on optimal or suboptimal weighting matrices. Monte Carlo study indicates that the bias and efficiency of the new estimators are more reliable than the conventional estimators.

In dynamic panel data (DPD) models, thegeneralized method of moments (GMM) estimation gives efficient estimators. However, this efficiency is affected by the choice of the initial weighting matrix. In practice, the inverse of the moment matrix of the instruments has been used as an initial weighting matrix which led to a loss of efficiency. Therefore, we will present new GMM estimators based on optimal or suboptimal weighting matrices in GMM estimation. Monte Carlo study indicates that the potential efficiency gain by using these matrices. Moreover, the bias and efficiency of the new GMM estimators are more reliable than any other conventional GMM estimators

The partially linear model (PLM) is one of semiparametric regression models; since it has both parametric (more than one) and nonparametric (only one) components in the same model, so this model is more flexible than the linear regression models containing only parametric components. In the literature, there are several estimators are proposed for this model; where the main difference between these estimators is the estimation method used to estimate the nonparametric component, since the parametric component is estimated by least squares method mostly. The Speckman estimator is one of the commonly used for estimating the parameters of the PLM, this estimator based on kernel smoothing approach to estimate nonparametric component in the model. According to the papers in nonparametric regression, in general, the spline smoothing approach is more efficient than kernel smoothing approach. Therefore, we suggested, in this paper, using the basis spline (B-spline) smoothing approach to estimate nonparametric component in the model instead of the kernel smoothing approach. To study the performance of the new estimator and compare it with other estimators, we conducted a Monte Carlo simulation study. The results of our simulation study confirmed that the proposed estimator was the best, because it has the lowest mean squared error.

This paper considers panel data models when the errors are first-order serially correlated as well as with stochastic regression parameters. The generalized least squares (GLS) estimators for these models have been derived and examined in this paper. Moreover, an alternative estimator for GLS estimators in small samples has been proposed, this estimator is called simple mean group (SMG). The efficiency comparisons for GLS and SMG estimators have been carried out. The Monte Carlo studies indicate that SMG estimator is more reliable in most situations than the GLS estimators, especially when the model includes one or more non-stochastic parameter.

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.