My research can be broadly defined as touching economic questions as they relate to both time and space. For most of my graduate career, I have been funded by the Freight Transportation Research Institute within the School of Economic Sciences at WSU; hence, my research tends to use transportation related data to answer economic questions. I have also dabbled in the economics of housing markets.
My research interests are in applied microeconomics, applied econometrics, and behavioral economics. You can view my statement on research here.
The title of my dissertation is Topics in Spatial-Temporal Economics. The papers that make up my dissertation are:
The first paper looks at reference-point updating (a feature of Prospect Theory) within periods. The second paper adds evidence to whether including the 3rd moment (i.e. skewness) is useful in predicting the preferred route. The last paper explores whether or not the rise and fall of housing markets influence voting preferences in U.S. presidential elections.
My family and friends think my job market paper is something like this.
My job market paper actually looks at how/if reference points (from Cumulative Prospect Theory) are updated within periods, i.e., after an event takes place, but before the outcome is realized. Transportation data is perfect to answer this question. Drivers choose a route or leave for a destination, but don’t fully realize the outcome until final arrival at their respective destinations. The abstract for the paper is below:
The defining feature separating (cumulative) prospect theory from expected utility theory is that potential outcomes are measured relative to a reference point as opposed to final asset allocation. Determining the reference point, therefore, is vital to correct analysis. While many theories and assumptions have been proposed concerning reference point updating between repeated choices, few researchers have explored reference point updating within periods. This paper seeks to find if and when drivers change their refer- ence point in a transportation setting when faced with an unexpected delay en route. A novel, yet conservative, approach is proposed to estimate reference point adaption amid data uncertainty. Using this new estimation technique, it is found that drivers are more likely to change their reference point if the unexpected delay occurs near the endpoints of travel.
You may view the first few pages of my job market paper here. If you would like the full copy, you may email me here.
The code behind my job market paper can be found on GitHub.
To fulfill the capstone requirement for the MS in Statistics, I looked at how price diffuses across significant geographic boundaries (i.e., mountains or the like) using vector autoregressive models. The abstract for this paper is below:
While it has been shown that housing prices diffuse across arbitrary political boundaries, does price also diffuse across natural geographic barriers? This paper develops a vector autoregressive model for a sample data set of Washington housing prices from the 4th quarter of 2007 through the 4th quarter of 2015. Regions are constructed such that the boundaries between them are more substantive (i.e. mountains) than arbitrary political lines. Additional vector autoregressive models with the inclusion of exogenous variables are also estimated. In general, the results are consistent across the models. However, due to small sample size and p-values on the boundary, the null hypothesis is only weakly rejected. That is, housing prices in Western Washington weakly Granger-cause housing prices in Eastern Washington.
You may view the first few pages of my capstone project here. If you would like the full copy, send me an email.