Economics Faculty Publications

Empirical Methods-A Review: With an Introduction to Data Mining and Machine Learning

Matt Bogard — Fri, 17 Jun 2011 08:47:27 PDT

This presentation was part of a staff workshop focused on empirical methods and applied research. This includes a basic overview of regression with matrix algebra, maximum likelihood, inference, and model assumptions. Distinctions are made between paradigms related to classical statistical methods and algorithmic approaches. The presentation concludes with a brief discussion of generalization error, data partitioning, decision trees, and neural networks.

Empirical Methods for Predicting Student Retention- A Summary from the Literature

Matt Bogard — Fri, 17 Jun 2011 08:47:25 PDT

The vast majority of the literature related to the empirical estimation of retention models includes a discussion of the theoretical retention framework established by Bean, Braxton, Tinto, Pascarella, Terenzini and others (see Bean, 1980; Bean, 2000; Braxton, 2000; Braxton et al, 2004; Chapman and Pascarella, 1983; Pascarell and Ternzini, 1978; St. John and Cabrera, 2000; Tinto, 1975) This body of research provides a starting point for the consideration of which explanatory variables to include in any model specification, as well as identifying possible data sources. The literature separates itself into two major camps including research related to the hypothesis testing and the confirmation or empirical validation of theoretical retention models (Herzog, 2005; Ronco and Cahill, 2006; Stratton et al 2008) vs. research specifically focused on the development of applied predictive models (Miller, 2007; Miller & Herreid, 2008; Herzog, 2006; Dey & Astin, 1993; Delen 2010; Yu et al, 2010). Literature indicates that data mining or algorithmic approaches to prediction can provide superior results vis-à-vis traditional statistical modeling approaches (Delen et al, 2004; Sharda and Delen, 2006; Delen et al, 2007; Kiang 2007; Li et al 2009). However, little research in higher education has focused on the employment of data mining methods for predicting retention (Herzog, 2006).

Monopoly, Regulation, and Innovation

Matt Bogard — Sun, 21 Nov 2010 14:43:31 PST

Recently the Justice department has started investigations into alleged anti-trust violations by Monsanto. This has helped fuel a lot of already hyped discontent with one of the world’s leaders in innovative solutions for sustainable agriculture. This article discusses how the regulatory environment could possibly have contributed to more concentration and power in the biotech industry. Increasing regulation would likely have the opposite effect of creating a level playing field in the agriculture industry. From AgWeb, March 27,2010 http://www.agweb.com/blog/Economic_Sense_190/Monopoly_Regulation__and_Innovation_10771/

Sustainable Budgeting

Brian K. Strow et al. — Wed, 04 Aug 2010 11:51:15 PDT

At the forefront of current discussion and social conscience is the importance of ecological sustainability. An also important but often overlooked issue is the importance of sustainable budgeting practices for our various levels of government. Governments often provide social services such as food, clothing, shelter, education, public safety, or health care. If the money to pay for the social services comes from additional government debt, social services to future citizens become imperiled. In this paper, the authors set forth a theoretical framework for sustainable government decision making with special emphasis on sustainable governmental budgeting. The authors outline the intricacies of sustainable budgeting, describe why sustainable budgeting is important, and illustrate which countries and states are doing the best and worst jobs of sustainable budgeting. Finally, the authors offer practical advice for creating sustainable budgeting practices.

Using R

Matt Bogard — Sun, 01 Aug 2010 09:32:27 PDT

R is a statistical programming language with a command line interface that is becoming more and more popular every day. I have used R for data visualization, data mining/machine learning, as well as social network analysis. Initially embraced largely in academia, R is becoming the software of choice in various corporate settings.

Using Twitter to Demonstrate Basic Concepts from Network Analysis

Matt Bogard — Mon, 19 Apr 2010 07:18:35 PDT

Social network analysis focuses on finding patterns in interactions between people or entities. These patterns may be described in the form of a network. Network analysis in general has many applications including models of student integration and persistence, business to business supply chains, terrorist cells, or analysis of social media such as Facebook and Twitter. This presentation provides a reference for basic concepts from social network analysis with examples using tweets from Twitter.

Mathematical Themes in Economics, Machine Learning, and Bioinformatics

Matt Bogard — Mon, 29 Mar 2010 07:20:49 PDT

Graduate students in economics are often introduced to some very useful mathematical tools that many outside the discipline may not associate with training in economics. This essay looks at some of these tools and concepts, including constrained optimization, separating hyperplanes, supporting hyperplanes, and ‘duality.’ Applications of these tools are explored including topics from machine learning and bioinformatics.

An Economic Approach to Climate Change

Matt Bogard — Wed, 27 May 2009 09:08:52 PDT

Economics is the study of choices, and how they are made compatible. The issue of global warming can be viewed in the context of a set of choices that lead to climate change. In a free society choices are made compatible via the price system. If prices do not reflect the true cost of carbon, then carbon intensive production and consumption choices may not be compatible given the choice sets across all members of society. For example, my choice to burn fossil fuels may be incompatible with your choice to live in a coastal area given certain climate change scenarios. Economic literature is rich in analyzing situations where choices are incompatible, (often referred to as externalities) and proposing solutions. This presentation reviews some of the literature and economic theory related to externalities, and focuses on two particular economist’s approaches to climate change in this context.

Taxation Causes Kentucky to Lag Tennessee in Growth

Stephen E. Lile — Tue, 17 Jun 2008 08:36:35 PDT

Kentucky's Debt Relief Attempt

Stephen E. Lile — Tue, 17 Jun 2008 08:36:33 PDT