CV

General Information

Full Name Andrew Heinzman
Location Washington DC
Positions Economist, Data Scientist

Education

  • 2023
    PhD in Economics
    University of California, Los Angeles (UCLA)
    • New Product Introductions, Retailer Learning, and Pricing (Job Market Paper)
  • 2020
    MA in Economics
    University of California, Los Angeles (UCLA)
  • 2016
    BA in Economics and Statistics
    University of Virginia

Experience

  • 2023 - present
    Associate
    Cornerstone Research
    • Experienced in antitrust casework on topics including mergers, algorithmic collusion, and vertical integration. Conducted economic analysis that led to Alaska Airlines, Hawaiian Airlines merger clearance by the DOJ.
    • Built models of demand and supply to simulate consumer choices and predict the impact of policy choices on prices and sales.
    • Designated and managed teams of 3–6 data analysts for 9 month long projects in order to perform data cleaning, data visualization, and document reviews.
  • 2022
    Economist - Intern
    Amazon
    • Worked on the People Experience and Technology Central Science (PXTCS) designing auction mechanisms to optimize staffing levels.
  • 2016-2018
    Analyst
    Cornerstone Research
    • Analyzed economic and financial data to write reports and support PhD experts during legal testimony.
    • Utilized data science techniques such as regression analysis, big data management, and data visualization to calculate damages and show causality.

Research

  • New Product Introductions, Retailer Learning, and Pricing
    • Abstract I study how retailers introduce new products and learn to set prices across a network of stores. Retailers are often uncertain about the demand for new products which makes setting an appropriate price difficult. To learn about demand for a product that is new to a market, retailers use demand for existing, similar products and demand in similar markets. As a result, retailers know the least about the demand for products that are new to all markets and have few substitutes. When retailers are uncertain about the demand for a new product, they introduce the product to an initial subset of stores rather than their full network. The retailer then learns about demand by observing sales in that market and similar markets. The result is that retailers learn about demand and set prices for new products based on their experience with close substitutes and by utilizing similarities in demand across their network of stores.
    • Pricing Strategy, Structural Learning, Supermarkets
  • High Frequency Traders Slow Information Revelation
    • Abstract Modern financial markets are often divided on two dimensions, information and speed. Investors focused on uncovering and profiting from new information compete with high frequency traders (HFTs), who have invested in a speed advantage rather than uncovering new information. I examine the competition between these two types of traders to better understand how HFTs impact market outcomes. In a dynamic model, I study the impacts on market liquidity and the speed at which new information is incorporated into prices. HFT's speed advantage reduces the speed at which new information is incorporated into prices and improves liquidity (as measured by bid-ask spreads). As the speed of HFTs increases relative to information investors, information investors reduce their trading intensity, slowing the revelation of new information.
    • High Frequency Trading, Economic Theory

Academic Interests

  • Empirical Industrial Organization
  • Pricing
  • Product Selection
  • Demand Estimation
  • Structural Modeling
  • Simulated Competition
  • Machine Learning

Skills

  • R
  • SQL
  • Python