Christina Cross
Christina Cross >>>>> https://bytlly.com/2tlDFG
Christina Cross is the Director of Strategic Communications at the Wine & Spirits Wholesalers Association (WSWA). In her role, she works closely with senior executives, the state and federal government affairs teams and members across the country to craft and distribute internal and external communications.
Prior to joining WSWA in December 2019, Christina was an Account Supervisor at Crosby Volmer International Communications (CVIC), working across a variety of industries to develop and implement strategic communications for her clients in the trade association space. Before joining CVIC, she was an Account Executive with CounterPoint Strategies, providing counter-adversarial crisis communications for titans of industry, trade associations and financial institutions.
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Research interests: How family structure, change, and dynamics influence individual wellbeing across the life course, particularly among minority and/or low-income populations. Childhood as a key stage in the life course for the emergence and accumulation of social advantages or disadvantages.
Christina Cross is a Postdoctoral Fellow (2019-2021) and Assistant Professor (beginning 2021) of Sociology at Harvard University. She received her BA in Sociology from Emory University. Her research examines how family structure, change, and dynamics influence individual wellbeing across the life course, particularly among minority and/or low-income populations. Much of her work has focused on childhood as a key stage in the life course for the emergence and accumulation of social advantages or disadvantages. Her current work focuses on 1) documenting the prevalence and predictors of previously under-explored family structures that are common among disadvantaged populations (e.g., the extended family); 2) investigating the relationship between family dynamics and child outcomes and the extent to which it differs by social location; and 3) examining within-group heterogeneity in family processes among major racial/ethnic groups.
There are no set rules for choosing the lag order for autoregressive (AR) time series models. Currently, the most common methods employ AIC or BIC. However, AIC has been proven to be inconsistent and BIC is inefficient. Racine proposed an estimator based on Shao's work which he hypothesized would also be consistent, but left the proof as an open problem. We will show his claim does not follow immediately from Shao. However, Shao offered another consistent method for cross validation of linear models called APCV, and we will show that AR models satisfy Shao's conditions. Thus, APCV is a consistent method for choosing lag order. Simulations also show that APCV performs as well, and in some cases, performs better than AIC, AICc, and BIC. 59ce067264