Stelios Michalopoulos from Brown is our seminar speaker this Friday. His talk will be on “Ethnic Inequality” (link here and abstract below) in Lillis 111 from 3:30-5pm.

Stelios is a fast rising development economist working on the empirical-macro side. He received his PhD from Brown in 2008 with creative use of nighttime lights intensity data and a quasi-natural experiment to ask whether culture or national institutions is better at explaining Africa’s development. By comparing same tribes across the national boundaries that were drawn ad hoc at the 1884-85 Berlin conference, he finds the explanation is culture. A paper from his dissertation was published in Econometrica last year, another in the AER.

C. Rich Higgins presented the third chapter of his dissertation which analyzes whether the Great Moderation in the United States was caused by “good luck” or “good policy” (or neither).  The abstract is below.

Abstract: A New Keynesian Model with Financial frictions is augmented with parameter drift and
stochastic volatility. This model is estimated and used to study the causes of the Great Moderation, a period of reduced macroeconomic volatility observed in the U.S. economy from 1984 to 2007. The model finds evidence of stochastic volatility and a decrease in financial frictions, but does not find support for changes in monetary policy. Based on counterfactual studies, the reduction in financial frictions was an important reason for the reduction in volatility observed during the Great Moderation.

The slides for the presentation are here.

Increases in computational power offer economists new and powerful tools in estimating intractable econometric models. But many of the routines most commonly used to solve the large systems of equations that macro economists are often concerned with can be extremely computationally intensive. The computational challenge is an effort to try to develop and understand a set of best practices for the non-programmer economist who wants to use computers to conduct analysis. Graduate students in the UO Department of Economics developed basic code to estimate a probit model using Bayesian techniques including the implementation of a Metropolis-within-Gibbs posterior simulator across a variety of programming languages in order to improve upon baseline code. Among the findings:

• Inverting matrices is costly, and “canned” routines in all languages can be inefficient. Knowledge of the particular computational problem, such as the structure of a matrix, and application of appropriate decomposition can greatly improve computational time.
• By eliminating or streamlining intensive routines such as matrix inversion and calculation of pdfs each of the programming languages considered (Julia, Gauss, MATLAB, and Python) decreased the time taken to run the program by factors ranging from approximately 12 to 6.
• Move routines outside of loops if they do not need to be done repeatedly. For example, one should create storage spaces for estimation results before running the loop rather than appending to the storage matrices in each iteration. While computers are exceptional at repetition, eliminating redundancies within loops can significantly increase computational speed.

Slides from the presentation can be found here.