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11 – 20 of 481Adnan Aldholay, Osama Isaac, Zaini Abdullah, Rasheed Abdulsalam and Ahmed Hamoud Al-Shibami
While many researchers have investigated the adoption and usage of online learning in different settings, one area that has yet to be examined thoroughly, particularly in the…
Abstract
Purpose
While many researchers have investigated the adoption and usage of online learning in different settings, one area that has yet to be examined thoroughly, particularly in the context of online learning in Yemen, is the self-efficacy role. The purpose of this paper is to extend the Delone and McLean information system success model by incorporating a self-efficacy construct as an antecedent to user satisfaction and actual usage to predict student performance.
Design/methodology/approach
Questionnaire survey method was used to collect primary data from 448 students in nine public universities in Yemen. The six constructs in the proposed model were measured using existing scales. The data analysed using confirmatory factor analysis and structural equation modelling via AMOS.
Findings
Three main results were revealed, namely that overall quality (system, information and service quality) and self-efficacy have a positive impact on user satisfaction and actual usage; that actual usage significantly predicts user satisfaction; and that both user satisfaction and actual usage have a positive impact on student performance.
Research limitations/implications
First as the study population were students from nine public universities, it excluded academics and administrative staff. Second, the study depends on self-reported measures to test the proposed research model. This is because getting objective data about the performance was not probable due to the issue of privacy.
Practical implications
The findings of this study can be a guideline for Yemeni higher education institutions to develop efficient and effective plans to improve the performance of education institutions, and train and develop student ability to use online learning. Additionally, it highlights the areas that university management needs to concentrate on, namely information systems (IS) tools that will contribute to higher student enrolment, address the lack of infrastructure and improve the quality of education outcomes, things which represent Yemen’s main challenges in the higher education sector.
Originality/value
This paper adds to the existing literature of IS by combining overall quality, self-efficacy, actual usage and user satisfaction to predict performance impact of online learning among students in nine public universities in Yemen. Furthermore, the predictive power of the proposed model has a higher ability to explain and predict performance impact compared to those obtained from some of the previous studies.
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Whayoung Jung and Ji Hyung Lee
This chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive…
Abstract
This chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive conditional quantile model and propose a new construction of quantile impulse response functions (QIRFs). The tool set of QIRFs provides detailed distributional evolution of an outcome variable to economic shocks. The authors show the left tail of economic activity is the most responsive to monetary policy and financial shocks. The impacts of the shocks on Growth-at-Risk (the 5% quantile of economic activity) during the Global Financial Crisis are assessed. The authors also examine how the economy responds to a hypothetical financial distress scenario.
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Han-Ying Liang, Yu Shen and Qiying Wang
Joon Y. Park is one of the pioneers in developing nonlinear cointegrating regression. Since his initial work with Phillips (Park & Phillips, 2001) in the area, the past two…
Abstract
Joon Y. Park is one of the pioneers in developing nonlinear cointegrating regression. Since his initial work with Phillips (Park & Phillips, 2001) in the area, the past two decades have witnessed a surge of interest in modeling nonlinear nonstationarity in macroeconomic and financial time series, including parametric, nonparametric and semiparametric specifications of such models. These developments have provided a framework of econometric estimation and inference for a wide class of nonlinear, nonstationary relationships. In honor of Joon Y. Park, this chapter contributes to this area by exploring nonparametric estimation of functional-coefficient cointegrating regression models where the structural equation errors are serially dependent and the regressor is endogenous. The self-normalized local kernel and local linear estimators are shown to be asymptotic normal and to be pivotal upon an estimation of co-variances. Our new results improve those of Cai et al. (2009) and open up inference by conventional nonparametric method to a wide class of potentially nonlinear cointegrated relations.
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Kohtaro Hitomi, Keiji Nagai, Yoshihiko Nishiyama and Junfan Tao
In this study, the authors investigate methods of sequential analysis to test prospectively for the existence of a unit root against stationary or explosive states in a p-th order…
Abstract
In this study, the authors investigate methods of sequential analysis to test prospectively for the existence of a unit root against stationary or explosive states in a p-th order autoregressive (AR) process monitored over time. Our sequential sampling schemes use stopping times based on the observed Fisher information of a local-to-unity parameter. In contrast to the Dickey–Fuller (DF) test statistic, the sequential test statistic has asymptotic normality. The authors derive the joint limit of the test statistic and the stopping time, which can be characterized using a 3/2-dimensional Bessel process driven by a time-changed Brownian motion. The authors obtain their limiting joint Laplace transform and density function under the null and local alternatives. In addition, simulations are conducted to show that the theoretical results are valid.
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Abstract
This chapter considers the estimation of a parametric single-index predictive regression model with integrated predictors. This model can handle a wide variety of non-linear relationships between the regressand and the single-index component containing either the cointegrated predictors or the non-cointegrated predictors. The authors introduce a new estimation procedure for the model and investigate its finite sample properties via Monte Carlo simulations. This model is then used to examine stock return predictability via various combinations of integrated lagged economic and financial variables.
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Martín Almuzara, Gabriele Fiorentini and Enrique Sentana
The authors analyze a model for N different measurements of a persistent latent time series when measurement errors are mean-reverting, which implies a common trend among…
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The authors analyze a model for N different measurements of a persistent latent time series when measurement errors are mean-reverting, which implies a common trend among measurements. The authors study the consequences of overdifferencing, finding potentially large biases in maximum likelihood estimators (MLE) of the dynamics parameters and reductions in the precision of smoothed estimates of the latent variable, especially for multiperiod objects such as quinquennial growth rates. The authors also develop an R2 measure of common trend observability that determines the severity of misspecification. Finally, the authors apply their framework to US quarterly data on GDE and GDI, obtaining an improved aggregate output measure.
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This essay responds to comments on Southern Theory by Mustafa Emirbayer, Patricia Hill Collins, Raka Ray, and Isaac Reed as part of a larger discussion about the future of…
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This essay responds to comments on Southern Theory by Mustafa Emirbayer, Patricia Hill Collins, Raka Ray, and Isaac Reed as part of a larger discussion about the future of postcolonial sociology. It clarifies aspects of Southern Theory that are commented upon while stressing the big claim of Southern Theory, which is that the periphery produces social theory that sociology should take seriously in order to make for a more global and democratic intellectual project of social change.
The authors develop a novel forecast combination approach based on the order statistics of individual predictability from panel data forecasts. To this end, the authors define the…
Abstract
The authors develop a novel forecast combination approach based on the order statistics of individual predictability from panel data forecasts. To this end, the authors define the notion of forecast depth, which provides a ranking among different forecasts based on their normalized forecast errors during the training period. The forecast combination is in the form of a depth-weighted trimmed mean. The authors derive the limiting distribution of the depth-weighted forecast combination, based on which the authors can readily construct prediction intervals. Using this novel forecast combination, the authors predict the national level of new COVID-19 cases in the United States and compare it with other approaches including the ensemble forecast from the Centers for Disease Control and Prevention (CDC). The authors find that the depth-weighted forecast combination yields more accurate and robust predictions compared with other popular forecast combinations and reports much narrower prediction intervals.
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The authors consider the quasi maximum likelihood (MLE) estimation of dynamic panel models with interactive effects based on the Ahn et al. (2001, 2013) quasi-differencing methods…
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The authors consider the quasi maximum likelihood (MLE) estimation of dynamic panel models with interactive effects based on the Ahn et al. (2001, 2013) quasi-differencing methods to remove the interactive effects. The authors show that the quasi-difference MLE (QDMLE) over time is inconsistent when
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Florens Odendahl, Barbara Rossi and Tatevik Sekhposyan
The authors propose novel tests for the detection of Markov switching deviations from forecast rationality. Existing forecast rationality tests either focus on constant deviations…
Abstract
The authors propose novel tests for the detection of Markov switching deviations from forecast rationality. Existing forecast rationality tests either focus on constant deviations from forecast rationality over the full sample or are constructed to detect smooth deviations based on non-parametric techniques. In contrast, the proposed tests are parametric and have an advantage in detecting abrupt departures from unbiasedness and efficiency, which the authors demonstrate with Monte Carlo simulations. Using the proposed tests, the authors investigate whether Blue Chip Financial Forecasts (BCFF) for the Federal Funds Rate (FFR) are unbiased. The tests find evidence of a state-dependent bias: forecasters tend to systematically overpredict interest rates during periods of monetary easing, while the forecasts are unbiased otherwise. The authors show that a similar state-dependent bias is also present in market-based forecasts of interest rates, but not in the forecasts of real GDP growth and GDP deflator-based inflation. The results emphasize the special role played by monetary policy in shaping interest rate expectations above and beyond macroeconomic fundamentals.
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