• Explored Probit and ordered Probit models to analyze firm entry, investigate instrumenting strategies for addressing endogeneity, and apply advanced econometric techniques (e.g., Bajari et al.’s two-step method) to model strategic entry games, enhancing understanding of competitive dynamics in retail industries. 
• Using R, I cleaned and order the data to estimate Probit regressions of entry decisions on market characteristics, estimate ordered Probit models for market structure, and implement a two-step method for modeling strategic entry games using profit functions, integrating firm-market characteristics and strategic interactions.
• Additionally, I analyzed airline entry decisions with Berry’s (1992) framework; estimating Probit models of entry decisions, ordered Probit models of market structure, and simulate market outcomes using maximum likelihood and simulated method of moments techniques, incorporating observed and unobserved heterogeneity. 

• How does the introduction of “skill” in a strategic information transmission model with noise improve welfare outcomes and highlight the link between noise reduction and receiver beliefs about message reliability. 
• Developed a theoretical model by extending the strategic information transmission framework, mathematically proving propositions, and creating algorithms to explore how outcomes vary with different variables and parameters. This work highlights the value of theory in building a foundation for future empirical testing, requiring careful analysis and a strong understanding of the underlying economic concepts.

We investigate strategic information transmission with observation noise, but include a variable that aids in reducing this noise, which we call skill. Our main findings are that skill improves welfare for the receiver when noise is present in the model; and that welfare in the one-partition equilibrium is greater in the skill model than in both the models with and without noise present. With skill present the uninformative optimal action is skewed towards the side of the true state; it can be seen as the receiver believing that even an uninformative message has a level of truth or skill attached to it. Furthermore, the assumptions we make for simplification have significant ramifications for our results, thus leading us to believe that skill has a greater affect on the model than our assumptions and results would lead us to believe.

• Estimated BLP by introducing demand and supply estimation in differentiated goods markets using structural models, equipping researchers to evaluate merger effects and market dynamics. 
• In R and Python, we clean and order the data to estimate Logit and Mixed Logit models, calculate pre- and post-merger market equilibria, analyze markups and marginal costs, and simulate the effects of mergers on prices and quantities. 

• Obtained experience with a broad range of econometric tools essential for causal inference, panel data analysis, and probability modeling 
• Demonstrated proficiency in Stata (replicable in R) by implementing methods like OLS, fixed effects, instrumental variables, and discrete choice models, with a focus on robust application and interpretation.