University of California, Davis

About: University of California, Davis is a education organization based out in Davis, California, United States. It is known for research contribution in the topics: Population & Gene. The organization has 78770 authors who have published 180033 publications receiving 8064158 citations. The organization is also known as: UC Davis & UCD.

Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors

Abstract: Individual investors who hold common stocks directly pay a tremendous performance penalty for active trading. Of 66,465 households with accounts at a large discount broker during 1991 to 1996, those that traded most earned an annual return of 11.4 percent, while the market returned 17.9 percent. The average household earned an annual return of 16.4 percent, tilted its common stock investment toward high-beta, small, value stocks, and turned over 75 percent of its portfolio annually. Overconfidence can explain high trading levels and the resulting poor performance of individual investors. Our central message is that trading is hazardous to your wealth.

Robust Inference with Multi-way Clustering

Abstract: In this article we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit, and GMM. This variance estimator enables cluster-robust inference when there is two-way or multiway clustering that is nonnested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g., Liang and Zeger 1986; Arellano 1987) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state–year effects example of Bertrand, Duflo, and Mullainathan (2004) to two dimensions; and by application to studies in the empirical literature where two-way clustering is present.

Bootstrap-Based Improvements for Inference with Clustered Errors

Abstract: Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (five to thirty) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo, and Mullainathan (2004). Rejection rates of 10% using standard methods can be reduced to the nominal size of 5% using our methods.

Measuring Global Self-Esteem: Construct Validation of a Single-Item Measure and the Rosenberg Self-Esteem Scale

Abstract: Four studies examined the construct validity of two global self-esteem measures. In Studies 1 through 3, the Single-Item Self-Esteem Scale (SISE) and the Rosenberg Self-Esteem Scale (RSE) showed strong convergent validity for men and women, for different ethnic groups, and for both college students and community members. The SISE and the RSE had nearly identical correlations with a wide range of criterion measures, including domain-specific self-evaluations, self-evaluative biases, social desirability, personality, psychological and physical health, peer ratings of group behavior, academic outcomes, and demographic variables. Study 4 showed that the SISE had only moderate convergent validity in a sample of children. Overall, the findings support the reliability and validity of the SISE and suggest it can provide a practical alternative to the RSE in adult samples. More generally, the findings contribute to the research literature by further elaborating the nomological network of global self-esteem.