I am Abe Martin and I am an economics PhD candidate at the University of North Carolina at Chapel Hill. I recieved my undergraduate degrees in Mathematics and Economics. My research area is in industrial organization (I.O.) with prior concentrations in econometrics and macro-finance.
Research Interests and Modeling Expertise:
Time Series Modeling: AR/ARMA/VAR/GARCH/General State Space Modeling/Fourier Decompositions
Time Series Algorithm: Kalman Filter, Markov Regime Switching
Classification: Discrete Choice/Multinomial Logit(standard/nested/mixed)/Neural Networks/K Means Clustering/E-M Algorithims/Naive Bayes
Estimation: Structural Modeling through IV/GMM/MLE. Point & Partial Identification:
Optimization: Convex optimzation, Optimal Control
Assessing the Benefits of Micromobility
In this paper, I examine the emission and congestion benefits attributable to fewer gasoline vehicle use due to the substitution to dockless, micromobility electric scooters in urban travel. To estimate the potential market demand for this new mode, a discrete choice, random utility framework is employed to estimate existing mode demand and its displacement effects. I find that the full adoption of short distance, micromobility devices in 52 of the major cities in the U.S. can create a positive externality of $2.86 million in environmental benefits and $3.37 billion in congestion benefits annually under a general equilibrium estimation and I document the local heterogeneity in benefits across all 52 cities
Default Risk Mitigation of Oil Producers
This paper builds a theoretical model of oil & gas producers facing default risk under a stochastic price environment that features forward hedging contracts. I build a framework that allows producers to access forward markets to hedge their forward production, which endogenizes their default rule and lowers their risk of default. From the model, I then quantitatively estimate the oil producers hedging demand and then show that this demand is strongly related to the idiosyncratic cost efficiency, that is cost per barrel of production, of each oil producer. I then empirically confirm this by collecting data on 23 of the largest independent oil & gas firms and then estimating their cost efficiency and showing a strong negative relationship between the oil producers hedging ratio.