Computational Economics: Help for the Underestimated Undergraduate

Our concern in this paper is that the capability of economics undergraduates is substantially underestimated in the design of the present college curriculum and that our students are insufficiently challenged and motivated. Students enter our classrooms with substantial previous knowledge about computers and computation and we are not taking full advantage of this opportunity. We suggest a set of examples from computational economics which are challenging enough to motivate students and simple enough that they can master them within a few hours. By encouraging the students to modify the models in directions of their own interest avenues for creative endeavor are opened which deeply involve the students in their own education.teaching computational economics

A Taylor Rule for Fiscal Policy

In times of rapid macroeconomic change it would seem useful for both fiscal and monetary policy to be modified frequently. This is true for monetary policy with monthly meetings of the Open Market Committee. It is not true for fiscal policy which mostly varies with the annual Congressional budget cycle. This paper proposes a feedback framework for analyzing the question of whether or not movement from annual to quarterly fiscal policy changes would improve the performance of stabilization policy. More broadly the paper considers a complementary rather than competitive framework in which monetary policy in the form of the Taylor rule is joined by a similar fiscal policy rule. This framework is then used to consider methodological improvements in the Taylor and the fiscal policy rule to include lags, uncertainty in parameters and measurement errors.design of fiscal policy, optimal experimentation, stochastic optimization, time-varying parameters, numerical experiments

Silurian graptolite biostratigraphy of the Röstånga-1 drill core, Scania:a standard for southern Scandinavia

The Rostanga-1 core from west-central Scania provides the most complete succession of the Sandbian (Upper Ordovician) through lower Telychian (Silurian, Llandovery) strata of southern Scandinavia. The Hirnantian is identified in the Kallholn Formation by the presence of a Metabolograptus persculptus Biozone fauna. The Akidograptus ascensus, Parakidograptus acuminatus, Cystograptus vesiculosus and Monograptus revolutus biozones can be differentiated in the Rhuddanian. Following a considerable gap (Demirastrites triangulatus Biozone to a level within the Pribylograptus leptotheca Biozone), the succession resumes. The Aeronian also includes the Lituigraptus convolutus and Stimulograptus sedgwickii biozones. The Stimulograptus halli Biozone appears to be missing, but the Telychian Spirograptus guerichi to Streptograptus crispus biozones have been recognized

Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation

Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of PR space that is completely unachievable, and the size of this region depends only on the skew. This paper precisely characterizes the size of that region and discusses its implications for empirical evaluation methodology in machine learning.Comment: ICML2012, fixed citations to use correct tech report numbe

Robustness of computer algorithms to simulate optimal experimentation problems.

Three methods have been developed by the authors for solving optimal experimentation problems. David Kendrick (1981, 2002, Ch.10) uses quadratic approximation of the value function and linear approximation of the equation of motion to simulate general optimal experimentation (active learning) problems. Beck and Volker Wieland (2002) use dynamic programming methods to develop an algorithm for optimal experimentation problems. Cosimano (2003) and Cosimano and Gapen (2005) use the Perturbation method to develop an algorithm for solving optimal experimentation problems. The perturbation is in the neighborhood of the augmented linear regulator problems of Hansen and Sargent (2004). In this paper we take an example from Beck and Wieland which fits into the setup of all three algorithms. Using this example we examine the cost and benefits of the various algorithms for solving optimal experimentation problems.