Yunxuan Liu

• Processed and structured large-scale panel datasets including China Family Panel Studies (CFPS), China Household Finance Survey (CHFS), and high-frequency weather data (1980–2020), totaling 300,000+ observations. • Engineered quantitative features such as Growing Degree Days (GDD), Harmful Degree Days (HDD), and rolling moving averages to capture dynamic climate exposure and temporal variation. • Implemented statistical analysis and bootstrap inference in STATA to examine evolving relationships between climate shocks, agricultural production, and investment decisions. • Built reproducible empirical workflows and produced data visualizations supporting ongoing applied econometric research. • Contributed literature synthesis connecting empirical results to economic mechanisms of climate adaptation and decision-making.

• Constructed a large U.S. climate panel dataset (1950–2022) using Schlenker & Roberts (2009) gridded weather data covering nationwide agricultural regions. • Replicated and extended Burke & Emerick (2016) using updated production and climate data to study long-run adaptation dynamics under extreme temperature shocks. • Applied long-difference and panel fixed-effects models to separate short-run volatility effects from persistent structural adaptation responses. • Quantified heterogeneous responses across regions and farm scales, identifying adaptation as a dynamic selection process under climate risk.

Analyzing the market based on SWOT while managing talents and holding management training for the organization.

• Led the planning and execution of initiatives, overseeing program progress, logistics, and team coordination. • Organized and designed online cross-cultural communication events with 105+ participants between Sri Lanka, Indonesia, Malaysia, Japan and China. • Initiated, designed, and organized an original murder-mystery role-playing event (“Who Killed These Cats?”) held on World Stray Animals Day, leveraging interactive narrative design to promote public awareness of stray animal welfare and protection.

• Contributed to a team promoting intangible cultural heritage through series of workshops for local residents. • Collaborated with Guangzhou Pharmaceutical Holdings to deliver a Traditional Chinese Medicine (TCM) workshop, introducing herbal sachet crafting and foundational TCM knowledge. • Co-organized an urban exploring event with a local organization, integrating the cultural and historical background of the Beijing Road area into a game-based format to promote public engagement with local heritage.

Core Courses: - ECMA 30400: MA Econ Math Camp - ECMA 30100: Introduction of Price Theory (Microeconomics) - ECMA 33220: Introduction to Advanced Macroeconomic Analysis - ECMA 31000: Introduction to Empirical Analysis (Econometrics) - ECMA 31000: Introduction to Empirical Analysis II - BUSN 41910: Time-series Analysis for Forecasting and Model Building (PhD Level) - ECMA 33603: Macroeconomic and Financial Frictions - ECMA 31350: Machine Learning for Economists - ECMA 30720: Analytical Political Economics - BUSN 35130: Fixed Income Asset Pricing

Core courses: Advanced Microeconomics, Advanced Macroeconomics, Advanced Econometrics, Programming with Python, Linear Algebra, Probability and Statistics, Mathematical Analysis, etc. This is a highly academic-oriented program where undergraduates are trained at the master’s level that they are fully equipped to conduct advanced economic and quantitative analysis using forefront empirical strategies and analytical tools. During the program, I acquired skills in quantitative and empirical analysis using methods such as IV-DID, SDID, PSM-DID, etc. I also deepened my understanding of machine learning algorithms and gained the ability to conduct quantitative analysis with Neural Networks, SVM, and Random Forests.