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R Introduction Part 3

6 minute read

Published:

  • This is my lecture note written for undergraduate R programming course, which is a supplementary course of the Advanced Macroeconomics course.
  • Most figures and tables are extracted from R in Action.
  • All exercises are copied from my teacher Jing Fang’s R Workshop, you can find the data for exercises on the website of R Workshop.

R Introduction Part 2

3 minute read

Published:

  • This is my lecture note written for undergraduate R programming course, which is a supplementary course of the Advanced Macroeconomics course.
  • Most figures and tables are extracted from R in Action.
  • All exercises are copied from my teacher Jing Fang’s R Workshop, you can find the data for exercises on the website of R Workshop.

R Introduction Part 1

7 minute read

Published:

  • This is my lecture note written for undergraduate R programming course, which is a supplementary course of the Advanced Macroeconomics course.
  • Most figures and tables are extracted from R in Action.
  • All exercises are copied from my teacher Jing Fang’s R Workshop, you can find the data for exercises on the website of R Workshop.

portfolio

publications

WP: The Market for Bitcoin Transactions

Available on SSRN, 2020

Transaction fees in the bitcoin system work differently from that in conventional payment systems due to the design of the bitcoin mining algorithm. In particular, transaction fees in the bitcoin system increase whenever the network is congested, and results from a simple VAR show that it is indeed the case. Under this feature, we also find that an increase in demand for bitcoin transactions drives out those of smaller volume. To account for the empirical findings, we build a model where users and miners together determine the level of the transaction fees. However, a back-of-envelope calculation shows that, compared with conventional payment systems, fluctuations of the transaction fee rate impose only a negligible cost to the users. But this calculation may underestimate the cost due to the crowding-out effect on small transactions during the congested period.

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WP: The Impact of Stay-at-Home Orders on US Output: A Network Perspective

Available on SSRN, 2020

Under the stay-at-home orders issued by states, economic activities are reduced or put on hold by some states across the U.S. to control the spread of COVID-19. By combining several sources of data, we estimate the output loss due to such restrictions using a network approach. Based on our most conservative estimates, the measures as of April 15, 2020 reduce 26% of total US output per period, and about 43% of which is due to the input-output connections in the production network. Using a SIR model with an inter-state infection network, we also calculate the cost of reducing each infection to be approximately 150,000 dollars during the period of March 19 to April 15, 2020. Simulation results of various hypothetical stay-at-home orders show that the unit cost of infection reduction of the existing order is about 13% higher than the local minimum.

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Price dispersion in bitcoin exchanges

Published in Economics Letters, 2020

Bitcoin is traded in a number of exchanges, and there is a large and time-varying price dispersion among them. We identify the sources of price dispersion using a standard time-varying vector autoregression model with stochastic volatility, and we find that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion.

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talks

teaching

Virginia Tech

Undergrad & Grad Courses, Virginia Tech, Department of Economics, 2020