Projects (Code in Python)

Cross-Validated High-dimensional Conditional Density Estimation [link]

• A unified framework for estimating conditional density with high-dimensional covariates

  • new representation: conditional density ~ many conditional means
  • allowing for any machine learners of conditional means (e.g sklearn)
  • The estimator is fully data-driven, achieved through cross-validation
  • new metric/loss for cross-validation, easy to implement
  • theoretical guarantee for the optimality

Difference-in-Differences Models with Continuous Treatment [link]

• Extending the diff-in-diff framework to continuous treatment

  • estimating average treatment effect on treated (ATT) at any continuous treatment intensity
  • under double/debiased machine learning (DML) framework: debiased score + crossfitting
  • can accommodate high-dimensional covariates
  • the estimator is asymptotically normal with explicit asymptotic variance
  • bonus multiplier bootstrap confidence interval

Approximate Sparsity Class and Minimax Estimation [link]

• Proposing a new type of sparisty: approximate sparsity

  • complexity (metric entropy) and minimax rates are established
  • LASSO (as a selection mechanism) is still optimal
  • data-driven LASSO threshold based on a maximal (Talagrand’s) inequality
  • simple code that implements the theoretical results
  • post-processing algorithm and additional Monte-Carlo simulations are provided

Consumer Preferences, Choices, and Counterfactuals [link]

• Implementation of Bayer, Ferreira, and McMillan (2007) in Python

  • combination of BLP and intrumental variable
  • contraction mapping
  • counterfactual estimation

Introduction to Econometrics [link]

• Jupyter Notebook for undergraduate econometrics course at UCLA

  • basic data manipulations (pandas and numpy)
  • linear regressions and hypothesis testing (statsmodels)
  • basic plotting (matplotlib)
  • special topic(s) (diff-in-diff)