Explore the vast range of titles available.

This study examines the impact of students’ self-directed learning (SDL) ability and parental expenditures for shadow education, and other factors, on academic achievement in South Korea during the pandemic. Busan Education Longitudinal Study panel data for 2019 (pre-COVID) and 2020–2021 (COVID) were utilized. National exam scores of middle and high school students in Korean, English, and math subject areas were analyzed using multiple regression, random effects, and two-way random effects models. The analysis revealed that, during the pandemic, parents’ high expenditures to provide shadow education to complement their children’s public education did not significantly influence academic achievement. In contrast, SDL ability was found to have a significant impact on achievement in all three subject areas during the pandemic. The findings underscore the importance of programs, policies, and teaching strategies that develop students’ SDL skills to enable them to learn on their own when a crisis restricts traditional teacher-guided classroom instruction.

For a panel data linear regression model with both individual and time effects, empirical studies select the two-way random-effects (TWRE) estimator if the Hausman test based on the contrast between the two-way fixed-effects (TWFE) estimator and the TWRE estimator is not rejected. Alternatively, they select the TWFE estimator in cases where this Hausman test rejects the null hypothesis. Not all the regressors may be correlated with these individual and time effects. The one-way Hausman-Taylor model has been generalized to the two-way error component model and allow some but not all regressors to be correlated with these individual and time effects. This paper proposes a pretest estimator for this two-way error component panel data regression model based on two Hausman tests. The first Hausman test is based upon the contrast between the TWFE and the TWRE estimators. The second Hausman test is based on the contrast between the two-way Hausman and Taylor (TWHT) estimator and the TWFE estimator. The Monte Carlo results show that this pretest estimator is always second best in MSE performance compared to the efficient estimator, whether the model is random-effects, fixed-effects or Hausman and Taylor. This paper generalizes the one-way pretest estimator to the two-way error component model.

A simple general method is proposed for constructing confidence intervals for arbitrary functions of variance components in balanced normal-theory models. The concept of \"surrogate variables\" is introduced as part of the description of the method. \"Equal-tail\" and \"shortest-length\" confidence intervals from this method can be computed easily using simulations. As an illustration, the two-way random-effects model is considered. It is shown that the proposed method produces intervals that are comparable in confidence coefficient and average length to those produced by the best existing methods.

This chapter discusses one‐way random effects model, two‐way random effects model, and two‐way mixed effects model. The introduced random effects models can be classified also as mixed effects models, since they have a general mean as a fixed effect. They are formulated as a general Gaussian linear mixed effects model. The chapter also discusses problems of negative estimators of variance components. The negative best unbiased estimator is a difficult problem, often encountered in the estimation of variance components. The chapter then introduces the idea of Smith and Murray to justify the negative estimator by regarding a certain variance component as a covariance. Next, the variance components are estimated by the moment method. Some authors evaluated the variances of estimation by the methods G 1 and G 2 of Yates and G 3 of Henderson in various unbalanced two‐way designs.

Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a 'pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s2, which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes.

Corresponding to the applications of all ordinary least squares (OLS) regressions, including the instrumental variables models, this chapter presents their extensions and modifications by using the general least squares (GLS) estimation method. The chapter also presents various effects models with numerical time‐independent variables. It begins with a discussion on cross‐section random effects models (CSREMs). Another section presents a comparative study between selected simple LV(1) CSREMs. Separate sections present CSREMs with the numerical time independent variables, CSERMs by time or time period, and period random effects models (PEREMs). For presenting the empirical results of PEREMs with a large number of parameters, the chapter makes use one of the work files available in EViews, namely the CES_wf1. A section presents additional two‐way fixed effects models (TWFEMs). Finally, the chapter explains a generalized method of moments/dynamic panel data, and advanced interaction effects models.

In this paper, we propose tests for the existence of random effects and interactions for two-way models with dependent errors. We prove that the proposed tests are asymptotically distribution-free which have asymptotically size τ and are consistent. We elucidate the nontrivial power under the local alternative when a sample size tends to infinity and the number of groups is fixed. A simulation study is performed to investigate the finite-sample performance of the proposed tests. In the real data analysis, we apply our tests to the daily log-returns of 24 stock prices from six countries and four sectors. We find that there is no strong evidence to support the existence of substantial differences in the log-return across countries, nor to the existence of interactions between countries and sectors. However, there exists random effect differences in the daily log-return series across different sectors.

Along with linguistic messages, prosody is an essential paralinguistic component of emotional speech. Prosodic parameters such as intensity, fundamental frequency (F0), and duration were studied worldwide to understand the relationship between emotions and corresponding prosody features for various languages. For evaluating prosodic aspects of emotional Marathi speech, the Marathi language has received less attention. This study aims to see how different emotions affect suprasegmental properties such as pitch, duration, and intensity in Marathi's emotional speech. This study investigates the changes in prosodic features based on emotions, gender, speakers, utterances, and other aspects using a database with 440 utterances in happiness, fear, anger, and neutral emotions recorded by eleven Marathi professional artists in a recording studio. The acoustic analysis of the prosodic features was employed using PRAAT, a speech analysis framework. A statistical study using a two-way Analysis of Variance (two-way ANOVA) explores emotion, gender, and their interaction for mean pitch, mean intensity, and sentence utterance time. In addition, three distinct linear mixed-effect models (LMM), one for each prosody characteristic designed comprising emotion and gender factors as fixed effect variables, whereas speakers and sentences as random effect variables. The relevance of the fixed effect and random effect on each prosodic variable was verified using likelihood ratio tests that assess the goodness of fit. Based on Marathi's emotional speech, the R programming language examined linear mixed modeling for mean pitch, mean intensity, and sentence duration.

The general case where the time specific effect in a two way model follows an arbitrary ARMA process has not been considered previously. We offer a straightforward maximum likelihood estimator for this case. Allowing for general ARMA processes raises the issue of model specification and we propose tests of the null hypothesis of no serial correlation as well as tests for discriminating between different specifications. A Monte-Carlo experiment evaluates the finite-sample properties of the estimators and test-statistics. [PUBLICATION ABSTRACT]

This article examines the macroeconomic factors influencing the flow of remittances to selected English-speaking Caribbean countries. A balanced two way fixed effects (FE) model, a random effects (RE) model and the adjusted fully modified ordinary least squares (FMOLS) model (designed to correct biases in OLS) are employed in estimating a relationship between per capita remittances and selected macroeconomic variables. This article also examines the time series properties of the data within a panel unit root and cointegration framework and in this respect, adds an important new dimension to the time series models on remittances by removing the possibility of a spurious relationship (Pedroni 1995,1997, 2000). The results strongly suggest that there is cointegration among the variables, and that remittances are influenced not only by altruistic motives but also by the investment motive in financial instruments. In addition, the results further indicate that there may be scope for public policy to increase these flows.