Regression Disturbances Research Articles

Application of least square dummy variable (LSDV) in the estimation of inequality adjusted HDI

The effects of inequality-adjusted indexes for education, income and life expectancy on inequality-adjusted human development index were observed using panel data for periods 2010 to 2019 in Benin, Cameroon, Ghana, Niger and Nigeria via Least squares dummy variable (LSDV) model that varies non-stochastically over time series (t) and individual level (i) observations. ADF test revealed unit root process for the series skating around their variances, centered dependent time with infinite persistence shocks. VIF test indicated moderately correlations of the variables in the LSDV model regression. Breusch-Godfrey/Wooldridge test predicted no serial correlation in idiosyncratic errors of the LSDV model. Again, Breusch-Pagan LM and Pasaran CD test indicated insignificant cross-sectional dependence in the panels. Likewise, the Breusch-Pagan test predicted that the regression disturbances of LSDV model are homoskedastic with the same variance across time and individuals. Consequently; for a given country, as inequality-adjusted indexes for education, income and life expectancy fluctuate by one unit, the inequality-adjusted human development index increases by 33.32%, 33.40% and 33.23% discretely. The average achievements of human development in education, income, and life expectancy distributed among its population by overlooking each dimension’s average value according to its level of inequality is low and needs to be enriched.

On approximation of linear regression disturbance distribution

Inference on the parameters of a linear regression usually requires the assumption that the disturbances are normally distributed. But sometimes this assumption does not correspond to the residual distribution. Thus, there is a need to infer the disturbances of the regression model from its residuals. In this article, the analysis is mainly focused on estimation of moments of the disturbances based on residuals. The main goal of this article is derivation of the unbiased estimators of the third and forth central moments of the linear regression disturbances. They are applied to estimation parameters of the random component distribution based on empirical data. In addition to the normal distribution of the random component, the gamma distribution and the class of Pearson curves are considered. An example of skewness and kurtosis coefficient estimation is shown. An example of estimating the dominant of the gamma distribution is shown. Finally, different types of point and interval forecasts of the examined variable are presented.

Application of the LINEX Loss Function with a Fundamental Derivation of Liu Estimator

For a variety of well-known approaches, optimum predictors and estimators are determined in relation to the asymmetrical LINEX loss function. The applications of an iteratively practicable lowest mean squared error estimation of the regression disturbance variation with the LINEX loss function are discussed in this research. This loss is a symmetrical generalisation of the quadratic loss function. Whenever the LINEX loss function is applied, we additionally look at the risk performance of the feasible virtually unbiased generalised Liu estimator and practicable generalised Liu estimator. Whenever the variation σ2 is specified, we get all acceptable linear estimation in the class of linear estimation techniques, and when σ2 is undetermined, we get all acceptable linear estimation in the class of linear estimation techniques. During position transformations, the proposed Liu estimators are stable. The estimators' biases and hazards are calculated and evaluated. We utilize an asymmetrical loss function, the LINEX loss function, to calculate the actual hazards of several error variation estimators. The employment of δP(σ), which is easy to use and maximin, is recommended in the conclusions.

Bayesian Inference of Multivariate Regression Models with Endogenous Markov Regime-Switching Parameters

AbstractThis study introduces a multivariate regression model with endogenous Markov regime-switching parameters, in which the regression disturbances and regime switches are allowed to be instantaneously correlated. For the estimation and model comparison, we develop a posterior sampling algorithm for the parameters, regimes, and marginal likelihood calculation. We demonstrate the reliability of the proposed method using simulation and empirical studies. The simulation study shows that neglecting the endogeneity leads to inaccurate parameter estimates, and that our marginal likelihood comparison chooses a correctly specified model. In the business cycle application, we find that the joint dynamics of the U.S. industrial production index (IPI) growth and unemployment rates are subject to three-state endogenous regime shifts. Another application to stock and bond return data suggests that negative shocks to the stock return seem to cause regime shifts from a low volatility state to a high volatility state of the financial markets. (JEL: C11, C53, E43, G12)

Agropecuária na região norte do Brasil: transbordamentos tecnológicos entre municípios

Este trabalho tem como objetivo verificar a existência do efeito transbordamento de tecnologia do setor agropecuário entre os municípios da região Norte, bem como analisar os determinantes do valor de produção do setor em 2006. Para esse fim, foram utilizados métodos derivados da econometria espacial. Os resultados obtidos mostraram que no setor agropecuário da região Norte a variável mão-de-obra possui maior impacto sobre o valor de produção e que não existe transbordamento de tecnologia entre os municípios. Este pode ser explicado pela baixa densidade rodoviária na região e pela distância existente entre os estabelecimentos que não permitem a disseminação e compartilhamento de tecnologia. Contudo, verificou-se a existência de dependência espacial no termo de erro na agropecuária nortista.

The onset of sleep disturbances and their associations with anxiety after acute high-altitude exposure at 3700\u2009m

Sleep disturbances and psychiatric repercussions pose great challenges at high altitude; however, few studies have investigated sleep disturbance and anxiety profiles and their associations after acute exposure in consecutive patients. Thus, we aimed to study the profiles of sleep disturbances in consecutive patients after high-altitude exposure and the association of such disturbances with anxiety. A total of 668 participants were recruited at sea level and 3700 m. The trials were performed at sea level (1 week prior to a 2-h flight to a high-altitude destination) and at 3700 m (24, 72, and 168 h). Sleep disturbances were assessed by self-reported sleep patterns and scores on the Athens Insomnia Scale (AIS). State anxiety was assessed using the Self-Rating Anxiety Scale (SAS). In our study, the incidence of sleep disturbances increased significantly after acute high-altitude exposure (65.3%, 434/668) and then gradually decreased after 72 h (50%, 141/282) and 168 h (44%, 124/282). The sleep assessments AIS [2.0 (4.0) vs. 4.0 (5.0)] and ESS [4.0 (4.0) vs. 5.0 (5.0)] increased significantly (p < 0.05). Also, the SAS increased significantly from 26.25 (3.75) to 28.75 (7.5). The SAS was significantly high in sleep disturbance group [31.25 (7.5) vs. 27.5 (5), p < 0.001] than in the non-sleep- disturbance group. The baseline SAS and AIS scores were significantly higher in participants with sleep disturbances than in those without (p < 0.01). Age, baseline insomnia, sleepiness, fatigue, and higher SAS were predictors of sleep disturbances in univariate regression (all p values < 0.05). However, only an older age (p = 0.045) and a higher baseline SAS (p = 0.018) remained independent predictors of sleep disturbances. Our findings indicated that acute high-altitude exposure triggers the onset of sleep disturbances, which are closely associated with anxiety. Furthermore, baseline state anxiety and age are independent predictors of sleep disturbances at high altitude.

UNIFORM CONVERGENCE RATES OVER MAXIMAL DOMAINS IN STRUCTURAL NONPARAMETRIC COINTEGRATING REGRESSION

This paper presents uniform convergence rates for kernel regression estimators, in the setting of a structural nonlinear cointegrating regression model. We generalise the existing literature in three ways. First, the domain to which these rates apply is much wider than the domains that have been considered in the existing literature, and can be chosen so as to contain as large a fraction of the sample as desired in the limit. Second, our results allow the regression disturbance to be serially correlated, and cross-correlated with the regressor; previous work on this problem (of obtaining uniform rates) having been confined entirely to the setting of an exogenous regressor. Third, we permit the bandwidth to be data-dependent, requiring it to satisfy only certain weak asymptotic shrinkage conditions. Our assumptions on the regressor process are consistent with a very broad range of departures from the standard unit root autoregressive model, allowing the regressor to be fractionally integrated, and to have an infinite variance (and even infinite lower-order moments).

Endogenous Markov Switching Regression Models for High-Frequency Data Under Microstructure Noise

We present a novel method in analyzing microstructure noise of high-frequency data as a measurement error problem within an endogenous Markov-switching regression model. In this model, the regression disturbance and the latent state variable controlling the regime are correlated. We show that under endogeneity the popular realized variance estimator is biased and no longer converges to the integrated regime dependent volatility. Exploring intraday return data on foreign exchange rates, we find significant endogeneity at high frequencies. Similar to the popular volatility signature plot suggested by Andersen, Bollerslev, Diebold, and Labys (2000b), we propose an endogeneity plot, which indicates as to which sampling frequency the assumption of exogeneity of the state variable controlling the regime remains valid.

Reducing errors-in-variables bias in linear regression using compact genetic algorithms

A new technique is devised to mitigate the errors-in-variables bias in linear regression. The procedure mimics a 2-stage least squares procedure where an auxiliary regression which generates a better behaved predictor variable is derived. The generated variable is then used as a substitute for the error-prone variable in the first-stage model. The performance of the algorithm is tested by simulation and regression analyses. Simulations suggest the algorithm efficiently captures the additive error term used to contaminate the artificial variables. Regressions provide further credit to the simulations as they clearly show that the compact genetic algorithm-based estimate of the true but unobserved regressor yields considerably better results. These conclusions are robust across different sample sizes and different variance structures imposed on both the measurement error and regression disturbances.

Heteroskedasticity and non-normality robust LM tests for spatial dependence

The standard LM tests for spatial dependence in linear and panel regressions are derived under the normality and homoskedasticity assumptions of the regression disturbances. Hence, they may not be robust against non-normality or heteroskedasticity of the disturbances. Following Born and Breitung (2011), we introduce general methods to modify the standard LM tests so that they become robust against heteroskedasticity and non-normality. The idea behind the robustification is to decompose the concentrated score function into a sum of uncorrelated terms so that the outer product of gradient (OPG) can be used to estimate its variance. We also provide methods for improving the finite sample performance of the proposed tests. These methods are then applied to several popular spatial models. Monte Carlo results show that they work well in finite sample.

Improved GMM estimation of the spatial autoregressive error model

We suggest an improved GMM estimator for the autoregressive parameter of a spatial autoregressive error model by taking into account that unobservable regression disturbances are different from observable regression residuals.

Modeling and Forecasting Currency in Circulation in Sri Lanka

Currency in circulation is typically estimated either by specifying a currency demand equation based on the theory of transaction and portfolio demand for money or univariate time series models. The first approach works well with low frequency data but faces limitations with high frequency data series. Therefore, this paper proposes an alternative approach in modeling the high frequency data series by decomposing the trend, the seasonal, and the cyclical components. Three separate models were estimated with monthly, weekly and daily time series, assembling tools for forecasting trend, seasonal patterns and cycles in individual series separately. Trend and seasonal effects were identified by regressing on trend and seasonal dummies while cyclical dynamics were captured by allowing for ARMA effect in the regression disturbances. The sample period is 1 January 2000 to 31 August 2005 and data for 1 September 2005 to 31 January 2006 were used for post validity test. All three models fit the data well and provide very close forecasts during the post sample period. All three models clearly identify both inter-month and intramonths variations of currency in circulation. The models also identified that the Sinhala/Tamil New Year, elections, Christmas and the day prior to public and bank holidays have significant positive impact on demand for currency in Sri Lanka. This methodology may be used in forecasting currency in circulation with careful assessment of day to day current developments in the economy. DOI: 10.4038/ss.v36i1.1230 <em>Staff Studies </em>Volume 36 Numbers 1& 2 2006 p.37-72

Successful Electroconvulsive Therapy in a Patient With Intellectual Disability and Bipolar Disorder, With Catatonic Features Misdiagnosed as Encephalopathy

We describe the successful use of electroconvulsive therapy (ECT) in a 39-year-old woman with a complex clinical picture including bipolar disorder with catatonic features superimposed on a history of multiple congenital anomalies and intellectual and developmental disabilities. At age 34, the patient presented with marked regression and behavioral disturbance diagnosed as encephalopathy. Multiple medical evaluations were inconclusive, and treatment courses with antibiotics and steroids failed. After psychiatric evaluation, her condition was diagnosed as bipolar disorder with catatonic features. She was treated with ECT with dramatic improvement and resumption of her premorbid level of functioning. We discuss the relevant literature on the use of ECT in patients with intellectual disability.

Small Sample Bias in Panel Data

There exists a small sample bias in predictive regressions, when a rate of return is regressed on a lagged stochastic regressor, and the regression disturbance is correlated with the regressors’ innovations. Although this bias can be a serious concern in time-series predictive regressions, it is not significant in panel data setting. By using simulations and stock level data, we document that as the number of cross sections used in the panel data increases the bias in coefficient estimates becomes negligible.

Perfil de pacientes idosos e tempo de permanência em ambulatório geronto-geriátrico

Resumo Objetivo: Identificar o perfil de pacientes idosos em assistência ambulatorial no Núcleo de Atenção ao Idoso e as variáveis que interferem no tempo de permanência no serviço. Métodos: Realizou-se pesquisa quantitativa, descritiva, de corte transversal, baseada na análise de 1.194 prontuários médicos de pacientes atendidos no período de 2000 a 2004. As variáveis foram: dados sociodemográficos, registros da avaliação médica e tempo de permanência do idoso no ambulatório. Para análise da relação entre variáveis clínicas e tempo de permanência, utilizou-se o modelo de regressão logística. Resultados: Em todas as faixas etárias, as mulheres (76,5%) foram mais numerosas que os homens (23,5%). Quanto à escolaridade, 12,9% eram iletrados e a categoria mais prevalente tinha primeiro grau incompleto (37,1%). O número de idosas viúvas (38%) foi três vezes maior que o de viúvos (12%). A maioria dos idosos (94,4%) tinha companhia de moradia e 84,4% não tinham plano de saúde. A média do tempo de permanência do idoso no serviço foi 2 anos e 8 meses, com desvio padrão de 1,5 ano. As variáveis que mais influenciaram o tempo de permanência foram: idade, queixa de tontura, distúrbios refracionais, auditivos, urinários, catarata, fraturas ósseas e antecedentes cirúrgicos. Conclusões: Por regressão logística, identificou-se que antecedentes cirúrgicos, distúrbios auditivos, urinários e fraturas ósseas atuaram aumentando o tempo de permanência, mas queixa de tontura, distúrbios refracionais e catarata reduziram a permanência do idoso no serviço.

Avoiding Invalid Instruments and Coping with Weak Instruments

Archimedes said, “Give me the place to stand, and a lever long enough, and I will move the Earth.” Economists have their own powerful lever: the instrumental variable estimator. The instrumental variable estimator can avoid the bias that ordinary least squares suffers when an explanatory variable in a regression is correlated with the regression's disturbance term. But, like Archimedes' lever, instrumental variable estimation requires both a valid instrument on which to stand and an instrument that isn't too short (or “too weak”). This paper briefly reviews instrumental variable estimation, discusses classic strategies for avoiding invalid instruments (instruments themselves correlated with the regression's disturbances), and describes recently developed strategies for coping with weak instruments (instruments only weakly correlated with the offending explanator).

Skin-covered midline spinal anomalies: a report of four rare cases with a discussion on their genesis and milestones in surgical management

The differential diagnosis for a dorsal midline mass presenting in a newborn encompasses a wide range of pathological conditions, including spinal dysraphisms, tumoral overgrowths including teratoma and hamartoma, disturbances in regression of fetal tail, and pseudotail formation. To present (a) three rare cases of human tails and one case of tethered cord due to a midline anomaly resembling part of a human limb, (b) to discuss different kinds of clinical and pathological conditions which may be encountered in such newborns, (c) to hypothesize a theory about genesis of these congenital lesions, and (d) to remind drawbacks in preoperative evaluations and surgical management of these newborns. Four newborns with dorsal midline malformations initially diagnosed as heterotopias are presented. Appropriate preoperative investigations and proper surgical interventions leading to resection of the lesions were performed. All four were skin-covered lesions containing well-differentiated cellular elements of fat, vascular, muscular, bony, and cartilaginous origin. Midline spinal dysraphism was detectable both pre- and intraoperatively only in one case in which cord untethering was also performed along with the first surgical intervention. In the other three "tailed cases," midline bone defect could not be detected in the available X-ray films; consequently, simple excision of the tail-like lesions was done. The first patient had to be explored for repeat untethering of the cord after 4 years, but the others have not yet developed any sign of tethering during an average period of 12 years follow-up. Morphological diagnosis of these lesions is not easy, and the attending pediatrician and neurosurgeon should be familiar with the differential diagnosis of such lesions and be prepared for possible time-consuming operation mandatory to achieve total resection of the lesion in a newborn under general anesthesia. Reviewing the possible theories regarding the genesis of such anomalies, it is hypothesized and suggested that all similar cases could have been of hamartomatous origin rather than defects of embryogenesis.

Estimation of Chinese agricultural production efficiencies with panel data

Fast and steady economic growth in China during the 1990s attracted much international attention. Given the scarcity of resources, it is important for economic growth to depend on production efficiency improvement to achieve sustainability. As China is the world's second largest foreign capital recipient, foreign capital plays an important role in investment. If economic growth is fuelled by investment, an exodus or a shortage of foreign capital will render growth unsustainable. However, if growth is propelled by improvements in production efficiency, it is more likely to be sustained and to withstand reduction in production input. This paper estimates production efficiency in the agricultural sector in China with a panel data set comprising 30 provinces for the 7-year period, 1991–1997. A panel data model based on the Cobb–Douglas production function is used to represent the production frontier and to compute technical efficiency at the provincial level. Individual effects are tested to determine if pooled estimation is preferred to unpooled (panel) estimation. The test confirms significant differences between the provinces, and hence warrants panel data estimation. Both fixed and random effects models are estimated, with provincial technical inefficiency specified as province-specific intercept terms for the former, and regression disturbances for the latter. Although the random effects model is rejected in favour of the fixed effects model, the latter did not produce estimates with correct signs, and is rejected on economic grounds. Using the random effects model, production efficiency has increased for most provinces, but the gap between the affluent coastal region and the hinterland in the west has increased.

Least Absolute Deviation Estimation of Linear Econometric Models: A Literature Review

Econometricians generally take for granted that the error terms in the econometric models are generated by distributions having a finite variance. However, since the time of Pareto the existence of error distributions with infinite variance is known. Works of many econometricians, namely, Meyer & Glauber (1964), Fama (1965) and Mandlebroth (1967), on economic data series like prices in financial and commodity markets confirm that infinite variance distributions exist abundantly. The distribution of firms by size, behaviour of speculative prices and various other recent economic phenomena also display similar trends. Further, econometricians generally assume that the disturbance term, which is an influence of innumerably many factors not accounted for in the model, approaches normality according to the Central Limit Theorem. But Bartels (1977) is of the opinion that there are limit theorems, which are just likely to be relevant when considering the sum of number of components in a regression disturbance that leads to non-normal stable distribution characterized by infinite variance. Thus, the possibility of the error term following a non-normal distribution exists. The Least Squares method of estimation of parameters of linear (regression) models performs well provided that the residuals (disturbances or errors) are well behaved (preferably normally or near-normally distributed and not infested with large size outliers) and follow Gauss-Markov assumptions. However, models with the disturbances that are prominently non-normally distributed and contain sizeable outliers fail estimation by the Least Squares method. An intensive research has established that in such cases estimation by the Least Absolute Deviation (LAD) method performs well. This paper is an attempt to survey the literature on LAD estimation of single as well as multi-equation linear econometric models.

The robustness of the F-test to spatial autocorrelation among regression disturbances

It is shown that the null distribution of the F-test in a linear regression is rather non-robust to spatial autocorrelation among the regression disturbances. In particular, the true size of the test tends to either zero or unity when the spatial autocorrelation coefficient approaches the boundary of the parameter space.