Economist & Data Scientist

Hao-Che Hsu

I'm Howard, a Ph.D. candidate in Economics and a Noyce Fellow at University of California-Irvine.

My research focuses on applied econometrics and the intersection of data science and machine learning.


Work in Progress

"Understanding Household Choice of Leisure with Time Allocation and Expenditure Measurements" DS
Abstract: People engage in leisure activities, but they come at a cost. Accounting for both time allocation and the price of leisure, we combined data from ATUS and retail markets scanners to investigate the geography of leisure. To gain insight into consumer behaviors, our study compared price indexes and Engel curves across leisure activities. Then we estimate the elasticity and substitution patterns of leisure with double machine learning and causal forest.
"Decision Parity on Alternative Credit with Shape Constraints" ML
Abstract: We utilized Experian's proprietary alternative credit data to investigate classification algorithms with asymmetric losses. With a deep neural network to assess the risk of loan applicants, we aim to understand algorithmic fairness in credit ratings and construct an optimal decision rule for loan inquiries.
"Choice and Backward Spillovers in the Film Industry" IO
Abstract: This study utilized weekly video sales data and a demand model to investigate factors that influence consumer movie purchasing decisions and recover the market's cost structure. The findings provide evidence of the director's backward spillover effect on sales. Then a counterfactual merger simulation was performed to demonstrate the market's reaction to a structural adjustment.
"Refugee Migration During the 2022 Russia-Ukraine War: Evidence from Queer Social Network Users" metrics
Abstract: This study examines the refugee migration during the 2022 Russia-Ukraine War. Using queer social network data, we develop an interactive visualization map to investigate the city-level factors influencing user migration choices. Furthermore, we employ count models to analyze the impact of price and geography factors on both static user residency decisions and dynamic migration flows within cities. Additionally, we provide an illustration of the migration pattern of the network users.


Map of ALT-credit Loan Inquiries (ALT Loan Lab) Vue.js Highcharts

Please view this interactive map on desktop browsers.


"Product Level Hierarchy Classification with Transformer-based Clustering" ML

I utilize a sentence transformer to embed product names with BERT models. The products gathered from online stores are projected into the embedding space and grouped into finer COICOP categories to calculate price indexes. Unlike traditional NLP models, the Transformer-based model evaluates sentence tokens simultaneously. The inputs are represented by a vector of embeddings that incorporate position and attention information. The high-dimensional embeddings are reduced to ten principal components and clustered with the EM algorithm.
"Community Detection on a Social Network" Graphical Models

This project investigates various community detection techniques using network data from the Hornet social platform. The approaches compared include Mixed Membership Stochastic Blockmodels and K-means clustering on both node edges and individual demographic features. The likelihoods of different sampling methods for the stochastic model are evaluated and the network topology is visualized using Gephi.
"Random Coefficient Logit Model with MCMC Algorithms" metrics

Numerous techniques have been developed to solve the random coefficients logit model. Following a developed method, I modify the prior distribution assumption on the aggregate demand shocks and estimate demand by sequentially updating the market share inversion process with Gibbs and Metropolis-Hasting sampling methods. In particular, I present a practitioner's guide including details of the algorithms' implementations.
"Image Generation with an Introspective Deep Learning Algorithm" ML

This project re-implements the introspective variational autoencoder to synthesize realistic images. IntroVAE repurposes the inference model to additionally act as a discriminator, enabling the model to self-estimate differences between generated and real images in an adversarial manner. We replicate and deliver comparable image quality to those presented in the research, and confirm the advantages of this model over standard VAEs and GANs.
"Natural Language Processing with three Supervised Learning Methods" ML

This project trains three machine learning models: Naive Bayes Classifier, Random Forest Classifier, and the Convolutional Neural Network to predict the sentiments of individual words or a review statement. The models are trained using IMDb movie reviews to optimize the parameters to attain the highest prediction accuracy.


Deep Data Lab (DDL)
Noyce Initiative at UCI

Research Groups

UCI Econometrics & Data Science Brown Bag (UCI Metrics)
Industrial Organization Reading Group
Master Research Group (UW-Madison)


Graduate Student Researcher

Deep Data Lab, UC-Irvine 2020 - present

Data Scientist Intern

Glassbox Learning (Google Research), Google 2022

Economist Intern

Prime Video, 2021

Ph.D. in Economics (STEM)

University of California-Irvine 2018 -

M.S. in Economics

University of Wisconsin-Madison 2017

B.A. in Economics

National Chung Cheng University (中正大學) 2014


Data Science

Structural Modeling

Machine Learning

Causal Inference












Honors and Awards

Noyce Fellowship
Noyce Foundation, 2021-2023

2021-2022 Best Econometrics Paper Prize
Economics Dept., 2022

Government Scholarship to Study Abroad
Taiwan Ministry of Education, 2021-2023

Summer Research Fellowship
Economics Dept., 2020-2022

Graduate Dean's Recruitment Fellowship
UC-Irvine, 2018

Distinguished Scholars Training Program
National Chung Cheng University, 2014


ECON 20A/23: Basic Economics I, Prof. Jiawei Chen (2023 Spring, Head TA)
Section: Mon 4:00 pm - 4:50 pm, SST 220A
OH: Tue 11:00 am - 12:00 pm, SST 338
ECON 105A: Intermediate Quantitative Economics I, Prof. Jiawei Chen (2020 Fall)
OH: Mon. 5:30-7:30 pm by Calendly appointments: schedule a meeting
Asynchronous Discussion Sections: video link available on Canvas
ECON 13: Global Economy, Dr. George Sarraf (2020 Summer II)
OH: Wed. 4:00-5:30 pm by Calendly appointments: schedule a meeting
Asynchronous Discussion Sections: video link available on Canvas
Discussion Handout:
ECON 142CW: Industrial Organization III, Prof. Jiawei Chen (2020 Spring)
OH: Thur. 3-5 pm only by Calendly appointments: schedule a meeting
Zoom (password required):

Direct Link: click to join
Meeting ID: 948 127 519
Join by Skype

ECON 13: Global Economy, Dr. George Sarraf (2019 Fall)
OH: Tue. 4-5 pm (SSPA 3182)
ELC: Tue. 3-4 pm (SST 165)
Discussion Sections:

A1 Mon. 7-8 pm (SST 220A)
A2 Mon. 8-9 pm (SST 220A)

ECON 20A: Basic Economics I, Prof. William Branch (2019 Winter)
OH: Mon. 11-12 am (SST 228)
ELC: Wed. 1-2 pm (SST 165)
Discussion Sections:

A6 Wed. 2-3 pm (SST 220A)
A7 Wed. 3-4 pm (SST 220A)

ECON 20B: Basic Economics II, Dr. Neerja Aggarwal (2018 Fall)
OH: Mon. 6-7 pm (SST 228)
ELC: Mon. 3-4 pm (SST 165)
Discussion Sections:

A1 Mon. 5-6 pm (SST 220A)
A7 Thur. 6-7 pm (DBH 1200)

Discussion Handout:
Game Theory (I), Prof. Meng-Chi Tang (CCU, 2013 Fall)
ECON 134A: Corporate Finance, Prof. Ahmad Sohrabian (2020 Summer I)
OH: Wed. 3:30-5:00 pm only by Calendly appointments: schedule a meeting
Asynchronous Discussion Sections: Tue. 7:00-7:50 pm (video link available on Canvas)
ECON 100C: Intermediate Economics III, Prof. Brian Jenkins (2019 Spring)
OH: Wed. 3-5 pm (SST 228)
ELC: Wed. 2-3 pm (SST 165)
Discussion Sections:

B1 Thur. 5-6 pm (SE 101)
B2 Thur. 6-7 pm (SE 101)

SOC SCI 10B: Probability and Statistics, Dr. Jonathan Lui (2020 Winter)
OH: Tue. 3:30-5:30 pm (SSPA 3182)
Lab Sections:

Lab-6 Wed. 12-1 pm (MSTB 226)
Lab-7 Thur. 9-10 am (ALP 3610)