An interview with Luis Raúl Pericchi

Luis Raúl Pericchi Guerra was born in Caracas, Venezuela, on 11 March 1952. He completed a B.S. in Mathematics in 1975 at the Universidad Simón Bolívar in Caracas, an M.S. in Statistics at the University of California Berkeley in 1978 and a Ph.D. in Statistics at Imperial College London in 1981. After graduating from Imperial College, Luis Raúl went back to Universidad Simón Bolívar. There, he played a key role in the developing of graduate programmes in Statistics and single handedly built an internationally recognised group focused on Bayesian statistics. In 2001, he moved to the Universidad de Puerto Rico in Rio Piedras to become the Chair of the Mathematics Department. At Universidad de Puerto Rico, he was instrumental in the establishment of a Ph.D. track in Computational Mathematics and Statistics. Luis Raúl has published over 120 papers in statistical and domain-specific journals, making significant contributions to several areas of Bayesian statistics (especially in the areas of model selection and Bayesian robustness) and their application (especially in hydrology). He is a Fellow of the American Statistical Association, the International Society for Bayesian Analysis, the John Simon Guggenheim Memorial Foundation and an Elected Member of the International Statistical Institute. This conversation took place over multiple sessions during the 2022 O'Bayes meeting in Santa Cruz, California, and the months that followed.     

Procyclical volatility in Chinese stock markets

Review of Quantitative Finance and Accounting - We investigate the macroeconomic determinants of stock market volatility in China using the two-component GARCH-MIDAS model of Engle et al....     

Forward-Looking Belief Elicitation Enhances Intergenerational Beneficence

Environmental and Resource Economics - One of the challenges in managing the Earth’s common pool resources, such as a livable climate or the supply of safe drinking water, is to motivate...     

Public overspending in higher education

We study the trade-off between governmental investments in pretertiary and tertiary education from an efficiency point of view. We develop a model comprising agents with different incomes and abilities, public and private schools, and public universities that select applicants based on an admission exam. Reallocating governmental resources from tertiary to pretertiary education may positively affect aggregate production and human capital if some conditions are satisfied. For instance, in an economy with a high proportion of credit-constrained students, a reallocation of expenditure toward public schools benefits many students, compensating for the negative effect of a decrease in public university investments. We also quantitatively investigate the optimal allocation of public investment between pretertiary and tertiary education, and we find that a 10% increase in productivity of public investments in pretertiary education could increase the optimal GDP between 2.1% and 3%.     

Inference in difference-in-differences: How much should we trust in independent clusters?

We analyze the challenges for inference in difference-in-differences (DID) when there is spatial correlation. We present novel theoretical insights and empirical evidence on the settings in which ignoring spatial correlation should lead to more or less distortions in DID applications. We show that details, such as the time frame used in the estimation, the choice of the treated and control groups, and the choice of the estimator, are key determinants of distortions due to spatial correlation. We also analyze the feasibility and trade-offs involved in a series of alternatives to take spatial correlation into account. Given that, we provide relevant recommendations for applied researchers on how to mitigate and assess the possibility of inference distortions due to spatial correlation.