Evaluating the Specification Errors of Asset Pricing Models

This paper examines the specification errors of several asset pricing models using the methodology of Hansen and Jagannathan (1997) and a common data set. The models are the CAPM, the Consumption CAPM, the Jagannathan and Wang (1996) conditional CAPM, the Campbell (1996) dynamic asset pricing model, the Cochrane (1996) production-based model, and the Fama-French (1993) three-factor and five-factor models. We use returns on the Fama-French twenty-five portfolios sorted by size and book-to-market ratio and the risk-free rate as our test assets. The sample is 1952 to 1997. We allow the parameters of the models' pricing kernels to fluctuate with the business cycle which we measure in two ways. One uses the Hodrick-Prescott (1997) filter applied to either industrial production for monthly models or real GNP for quarterly models. The second approach for quarterly models uses the consumption-wealth measure developed by Lettau and Ludvigson (1999). While we cannot reject correct pricing for Campbell's model, a stability test indicates that the parameters may not be stable. None of the models correctly prices returns that are scaled by the term premium.

As Schools Close due to the Coronavirus, Mental Health Care for Children Must be Protected

This data slice highlights the expansive nation-wide school closings due to COVID-19 and encourages providers and government bodies to support the mental health needs of students across the country

Child Poverty Has Been Declining in Single-Mother Families, but the Gap Remains Large

Family structure (whether a child lives in a single parent or married family) is a strong predictor of childhood poverty. While childhood poverty has been on the decline, there is still a high rate of poverty among children in single-mother families, affecting the health of these children. This data slice describes the trends of childhood poverty among different family structures in the U.S

Level search schemes for scalable information retrieval

Latent Semantic Indexing (LSI) has been demonstrated to outperform lexical matching in information retrieval. However, the enormous cost associated with the Singular Value Decomposition (SVD) of the large term-by-document matrix becomes a barrier for its application to scalable information retrieval. This thesis shows that information filtering using level search techniques can reduce the SVD computation cost for LSI. For each query, level search extracts a much smaller subset of the original term-by-document matrix with an average of 25% of the original non-zero entries. When LSI is applied to such subsets, the average precision only degrades by 5% due to level search filtering; however, for some document collections an increase in precision has been observed. Level search techniques are enhanced by a pruning scheme that deletes terms connected to only one document from the query-specific submatrix. An average 65% reduction in the number of non-zeros has been observed with a precision loss of 5% for most collections