Pinned Repositories
accbpg
Accelerated Bregman Proximal Gradient Methods
advanced-text-analysis-2
apgpy
Accelerated proximal gradient package in python
applied-methods-phd
Repo for Yale Applied Empirical Methods PHD Course
applied_metrics
A PhD course in Applied Econometrics and Panel Data
blp
BLP-1
blp-demand
estimate BLP demand model in Matlab using state-of-the-art techniques
BLP-Python
BLP-Python provides an implementation of random coefficient logit model of Berry, Levinsohn and Pakes (1995)
Courses
MOSEK reletated course material
zjt9101's Repositories
zjt9101/SpatialStatistics_R
Point pattern analysis, spatial autocorrelation statistics, and geostatistical interpolation to estimate values across continuous and discrete distributions.
zjt9101/PyShopper
[WIP] Python implementation of Shopper, a probablistic model of shopping baskets. 🛒🐍
zjt9101/DingelNeiman-workathome
"How Many Jobs Can be Done at Home?" by Jonathan Dingel and Brent Neiman
zjt9101/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
zjt9101/vis-for-data-analysis-bokeh-tensorspace
zjt9101/pygpu-workshop
zjt9101/Tutorials
A collection of tutorials for the MOSEK package
zjt9101/MFSS
Mixed Frequency State Space toolbox
zjt9101/R-large-scale
Materials for RCC workshop, "Large-scale data analysis in R."
zjt9101/glassdoor-review-scraper
Scrape reviews from Glassdoor
zjt9101/Mercury-Tutorial
Tutorial on the Mercury Computing Cluster
zjt9101/DingelMiscioDavis
Replication package for "Cities, Lights, and Skills in Developing Economies"
zjt9101/firmlevelrisk
Example code to create firm level risk in Hassan et al. (2020)
zjt9101/shopper-src
Code for Shopper, a probabilistic model of shopping baskets
zjt9101/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
zjt9101/gitignore
A collection of useful .gitignore templates
zjt9101/apgpy
Accelerated proximal gradient package in python
zjt9101/SLURM_WORKSHOP
zjt9101/transformers
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
zjt9101/ML_Codes
Empirical Data and Some Simulation Codes
zjt9101/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
zjt9101/persp-research-econ_Spr20
Course site for MACS 30250 (Spring 2020) - Perspectives on Computational Research in Economics
zjt9101/proxmin
Proximal optimization in pure python
zjt9101/accbpg
Accelerated Bregman Proximal Gradient Methods
zjt9101/blp-demand
estimate BLP demand model in Matlab using state-of-the-art techniques
zjt9101/pylbfgs
:mountain_cableway: Python/Cython wrapper for liblbfgs
zjt9101/Enterprise-Registration-Data-of-Chinese-Mainland
zjt9101/edward
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
zjt9101/dmlmt
Double Machine Learning for Multiple Treatments
zjt9101/p-emb
Exponential family embeddings (Poisson or Bernoulli) for discrete data