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1. Learning Latent Factors From Diversified Projections and Its Applications to Over-Estimated and Weak Factors

   https://doi.org/10.1080/01621459.2020.1831927  

   Fan, Jianqing ; Liao, Yuan ( October 2020 , Journal of the American Statistical Association)

   Estimations and applications of factor models often rely on the crucial condition that the number of latent factors is consistently estimated, which in turn also requires that factors be relatively strong, data are stationary and weakly serially dependent, and the sample size be fairly large, although in practical applications, one or several of these conditions may fail. In these cases, it is difficult to analyze the eigenvectors of the data matrix. To address this issue, we propose simple estimators of the latent factors using cross-sectional projections of the panel data, by weighted averages with predetermined weights. These weights are chosen to diversify away the idiosyncratic components, resulting in “diversified factors.” Because the projections are conducted cross-sectionally, they are robust to serial conditions, easy to analyze and work even for finite length of time series. We formally prove that this procedure is robust to over-estimating the number of factors, and illustrate it in several applications, including post-selection inference, big data forecasts, large covariance estimation, and factor specification tests. We also recommend several choices for the diversified weights. 

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2. Factor models with many assets: Strong factors, weak factors, and the two-pass procedure

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   Anatolyev, Stanislav ; Mikusheva, Anna ( July 2022 , Journal of Econometrics)
3. Disparities in affecting factors of housing price: A machine learning approach to the effects of housing status, public transit, and density factors on single-family housing price

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4. Dynamic correlation multivariate stochastic volatility with latent factors: Dynamic correlation MSV with latent factors

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5. Development of the Cooperative Adoption Factors Instrument to measure factors associated with instructional practice in the context of institutional change

   https://doi.org/10.1186/s40594-022-00364-w  

   McAlpin, Jacob D. ; Ziker, John P. ; Skvoretz, John ; Couch, Brian A. ; Earl, Brittnee ; Feola, Stephanie ; Lane, A. Kelly ; Mertens, Karl ; Prevost, Luanna B. ; Shadle, Susan E. ; et al ( July 2022 , International Journal of STEM Education)

   Many institutional and departmentally focused change efforts have sought to improve teaching in STEM through the promotion of evidence-based instructional practices (EBIPs). Even with these efforts, EBIPs have not become the predominant mode of teaching in many STEM departments. To better understand institutional change efforts and the barriers to EBIP implementation, we developed the Cooperative Adoption Factors Instrument (CAFI) to probe faculty member characteristics beyond demographic attributes at the individual level. The CAFI probes multiple constructs related to institutional change including perceptions of the degree of mutual advantage of taking an action (strategic complements), trust and interconnectedness among colleagues (interdependence), and institutional attitudes toward teaching (climate).

   From data collected across five STEM fields at three large public research universities, we show that the CAFI has evidence of internal structure validity based on exploratory and confirmatory factor analysis. The scales have low correlations with each other and show significant variation among our sampled universities as demonstrated by ANOVA. We further demonstrate a relationship between the strategic complements and climate factors with EBIP adoption through use of a regression analysis. In addition to these factors, we also find that indegree, a measure of opinion leadership, correlates with EBIP adoption.

   The CAFI uses the CACAO model of change to link the intended outcome of EBIP adoption with perception of EBIPs as mutually reinforcing (strategic complements), perception of faculty having their fates intertwined (interdependence), and perception of institutional readiness for change (climate). Our work has established that the CAFI is sensitive enough to pick up on differences between three relatively similar institutions and captures significant relationships with EBIP adoption. Our results suggest that the CAFI is likely to be a suitable tool to probe institutional change efforts, both for change agents who wish to characterize the local conditions on their respective campuses to support effective planning for a change initiative and for researchers who seek to follow the progression of a change initiative. While these initial findings are very promising, we also recommend that CAFI be administered in different types of institutions to examine the degree to which the observed relationships hold true across contexts.

    

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