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The current environment of misinformation is causing expensive and negative consequences for society. Fake news is affecting democracy and its foundations, as well as newspapers and media companies that aim to combat this “pandemic.” In order to effectively provide accurate information, these companies are in need of a workforce with specific attitudes, skills, and knowledge (ASK). However, several studies show that students either do not have those ASK or have poorly developed them, indicating the need for better media literacy skills. Given that such skills are often not taught in school, nor is there a way for students to efficiently obtain them and tangibly show them to future employers, we propose a model ¹ that enhances the way students learn and how we measure such learning. Journalism students – enrolled in liberal arts, general studies, and humanities – have the potential to be upskilled and become the new critical thinking and fact-checking force needed to neutralize misinformation and foster a healthy society. Our model applies learning science and behavioral research on feedback and intrinsic motivation to foster students' ASK through a digital apprenticeship model that uses structured activities together with mentorship and feedback. Students participate in the creation of digital products for the journalism and news media industry. This prepares them for the types of tasks they will be required to perform in the job market. The digital apprenticeship matches students with the proper mentor, peer, and professional network. Students' work is compared against professional news media production to generate feedback, improve quality, and track progress. During the digital apprenticeship, students receive the ASK-SkillsCredit, a digital badge, which serves as a “nutritional” fact label that displays how students created the media content, the level of efficacy of the apprenticeship, and the standard of journalism quality of the piece. Lastly, we propose to enhance existing learning management systems to capture and promote a learner's profile data and expose aligned opportunities in news media outlets.

The discrete Fourier transform (dft) of a fractional process is studied. An exact representation of the dft is given in terms of the component data, leading to the frequency domain form of the model for a fractional process. This representation is particularly useful in analyzing the asymptotic behavior of the dft and periodogram in the nonstationary case when the memory parameter d≥12. Various asymptotic approximations are established including some new hypergeometric function representations that are of independent interest. It is shown that smoothed periodogram spectral estimates remain consistent for frequencies away from the origin in the nonstationary case provided the memory parameter d < 1. When d = 1, the spectral estimates are inconsistent and converge weakly to random variates. Applications of the theory to log periodogram regression and local Whittle estimation of the memory parameter are discussed and some modified versions of these procedures are suggested for nonstationary cases.

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.

The author develops and extends the asymptotic F- and t-test theory in linear regression models where the regressors could be deterministic trends, unit-root processes, near-unit-root processes, among others. The author considers both the exogenous case where the regressors and the regression error are independent and the endogenous case where they are correlated. In the former case, the author designs a new set of basis functions that are invariant to the parameter estimation uncertainty and uses them to construct a new series long-run variance estimator. The author shows that the F-test version of the Wald statistic and the t-statistic are asymptotically F and t distributed, respectively. In the latter case, the author shows that the asymptotic F and t theory is still possible, but one has to develop it in a pseudo-frequency domain. The F and t approximations are more accurate than the more commonly used chi-squared and normal approximations. The resulting F and t tests are also easy to implement – they can be implemented in exactly the same way as the F and t tests in a classical normal linear regression.