Lizi Yu
Ph.D. Candidate, Columbia University
Welcome! I am a Ph.D. candidate at the Department of Economics in Columbia University, New York.
I am on the job market and will be available for interviews at the 2023 ASSA and AFA Meetings in New Orleans, Louisiana.
Education
2017-Present
Ph.D. Student in Economics
Department of Economics
Columbia University, New York
2015-2017
M.A. in Economics
Department of Economics
Duke University, North Carolina
2011-2015
B.A. in Finance
Kuang Yaming Honors School
Nanjing University, China
Working Papers
Strategic Alliance and Endogenous Production Network
Job Market Paper
Abstract: This paper examines how the U.S. firm's involvement in strategic alliance interacts with its endogenous choice of production networks. The results reveal that the alliance firm is more likely to actively create and break supply chains, especially with customers or suppliers from the industries within the alliance-related industrial scope. Moreover, such interactions are stronger when the updated customers and suppliers have a closer proximity to the alliance-related industries. To rationalize these stylized findings, I developed a model featured with the firm's endogenous searching of supplier candidates and endogenous input sourcing strategy. Additionally, strategic alliance is introduced as a mitigation of the friction in the candidate searching. The model implies that the strategic alliance could encourage the firm's searching of supplier candidates, and boost the adding and dropping of production networks simultaneously.
R&D, Risk Premia, and Credit Spreads
Jointly with Z. Liu
Abstract: Empirical evidence suggests that the R&D-intensive firms tend to show higher expected equity returns, but lower leverage, default rates, and credit spreads than the R&D-nonintensive ones. To provide a unified explanation for this cross-sectional pattern, we propose a production-based DSGE model featured with innovation-driven endogenous growth, and long-run and disaster risk. The model generates sizable heterogeneity in the quantities of interest between the R&D and Non-R&D sector and reconciles the coexistence of high equity returns and low leverage of the R&D sector. Additionally, our model fits the aggregate macroeconomic moments reasonably well.
Working in Progress
Measuring Industry-Specific Uncertainty: A Bayesian Approach
Abstract: In this paper, I estimate the common and industry-specific uncertainty measures from
U.S. quarterly firms’ accounting data with a Bayesian dynamic factor model, where the industry-specific uncertainty governs the fluctuations within one industry and the common uncertainty drives the fluctuations of the aggregate
economy. Based upon the estimation, these measures are further linked to the stock returns and generate implications regarding asset pricing.
Teaching
Teaching Assistance in Columbia University
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2021-2022: Corporate Finance (Graduate-Level)
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2018, 2020-2021: Financial Economics (Undergraduate-Level)
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2019: Microeconomics (Economics M.A. Course)
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2019: Principle of Economics (Undergraduate-Level)
Teaching Assistance in Duke University
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2017: Econometrics (Economics M.A. Course)