Reconciling Conflicting Gauss-Markov Conditions in the Classical Linear Regression Model

Larocca, Roger

doi:10.1093/pan/mpi011

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…For multiple regression, data normality or normal distribution of collected data is a vital assumption [17]. Table 4 showed that skewness and kurtosis were between ±1.96 [18], which indicated data normality.…”

Section: Data Normalitymentioning

confidence: 98%

A Scientific Novel Way of Article and Thesis Writing: Findings from a Survey on Keyword, Sequence, and Importance (KSI) Technique

Uddin¹,

Uzir²,

Hasan³

et al. 2020

ujer

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Academic scientific writing is essential to graduation and post-graduation. Many graduate students suffer from a lack of systematic technique. An effective and efficient writing skill and technique guide the students to produce a good article and thesis in a short period. Keyword, Sequence, and Importance (KSI) is a robust academic writing technique. The study investigated the KSI model's effectiveness in academic writing using the knowledge, attitude, and practice (KAP) model. The authors of articles and dissertations are sometimes in a fix on how to start, how to flow, how many citations are needed, how many words to be included in a paragraph, which should come first, and the sequence, etc. A total of 55 students (Ph.D., Masters, and others), who participated in the workshop to learn the KSI model in the workshop, participated in this study, and provided necessary data. SPSS statistical tool was used to analyze the collected data to examine KAP's impact on authors' writing performance. The results of multiple regression showed that attitude and practice had a significant effect on scientific writing performance. The findings implicate that the KSI model is a persuasive writing technique with which the supervisors can adequately guide their students. The students can produce scientific writing in a minimum time. The KSI model, practically, benefits a student to get graduate on time (GOT). The study findings also contribute to the KAP model to support its theoretical assumptions in the education sector.

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Section: Data Normalitymentioning

confidence: 98%

A Scientific Novel Way of Article and Thesis Writing: Findings from a Survey on Keyword, Sequence, and Importance (KSI) Technique

Uddin¹,

Uzir²,

Hasan³

et al. 2020

ujer

View full text Add to dashboard Cite

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“… See Shanken (1992) for an approach to correct for standard error bias in asset pricing studies estimating market (and other) betas.10 The treatment and language associated with this and other Gauss-Markov assumptions varies across econometric textbooks (see, e.g., table 1 and the related discussion inLarocca [2005]). …”

mentioning

confidence: 99%

Standard Error Biases When Using Generated Regressors in Accounting Research

Chen¹,

Hribar

Melessa

2023

J of Accounting Research

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We analyze the standard error bias associated with the use of generated regressors-independent variables generated from first-step regressions-in accounting research settings. Under general conditions, generated regressors do not affect the consistency of coefficient estimates. However, commonly used generated regressors can cause standard errors to be understated. Problematic generated regressors include predicted values, coefficient estimates, and measures derived from these estimates. Widely used generated regressors in accounting include measures of earnings persistence, normal accruals, litigation risk, and conditional conservatism. Using simple regression models and simulation, we demonstrate how generated regressors can produce understated standard errors in accounting research settings. We also demonstrate how the magnitude of the standard error bias is inversely related to the

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