Welcome to my website!

I'M

I am a Ph.D. candidate in the Department of Economics, Cornell University. My research interests are in econometrics and macroeconomics, both theoretical and applied. Specifically, I am interested in applications of partial identification, time series, Bayesian inference, and information economics.

My advisors are Francesca Molinari (chair), Yongmiao Hong, José Luis Montiel Olea, and Kristoffer Nimark.

I am on the job market during the 2022-2023 cycle and will be available for interviews at the EEA (online) and ASSA meeting (online and in-person).

WHAT I DO

Research

Job Market Paper
Robust Bayesian Estimation and Inference for Dynamic Stochastic General Equilibrium Models Partial Identification Bayesian
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Abstract: This paper introduces a new tool to conduct robust Bayesian estimation and inference in set-identified DSGE models that is designed to avoid commitment to a specific prior. While data only updates beliefs on globally identified parameters, I work on the identified sets of structural parameters to perform robust Bayesian analysis on those partially-identified parameters of which the distributions do not converge to their likelihood even with infinite data. Based on this tool, researchers are allowed to perform the following analysis. (i) With a large number of MCMC draws, check identification of a particular DSGE setup at every point within support, hence model identification. (ii) Find the complete expected identified set of both the deep parameters and any parameters of interest robust of prior choices. (iii) Derive the robust Bayesian credible region for parameters based on data. I then apply this method to the DSGE models of An and Schorfheide (2007) and Smets and Wouters (2007) to achieve robust credible regions for impulse responses and Taylor rule parameters. This algorithm can be extended to accommodate the non-linear cases as well.
Working papers
Centralized or Decentralized? An Empirical Model on Task Assignment of Government in Pandemics (with Qiwei He) Partial Identification IO Time Series Bayesian
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Abstract: One of the central questions in pandemic-related discussions is how to best design mitigation policy to reduce the death rate from COVID-19. This paper enters into the debate by comparing social welfare using centralized and decentralized decision-making. Using indirect inference, we first estimate a structural SIR model with a regional spillover effect. We then set up and structurally estimate a dynamic game model where each US state government makes mitigation policy independently. Socially optimal mitigation policy is then solved by minimizing the sum of local governments' welfare loss using estimated weights on different sectors. Counterfactual analysis of centralized decision-making is conducted to compare the social welfare gain (loss) should the US adopt a mitigation policy at the federal level.
Nowcasting with dynamic factors: A LASSO penalized model averaging approach (with Yongmiao Hong, Yuying Sun) Time Series ML
 PDF coming soon     Slides   
Abstract: With the advent of complex information systems to collect data, real-time nowcasting faces various challenges, including a more significant number of predictors, higher order of lags, unbalanced data structure, model uncertainty and complexity in bridging high-frequency information contained. To address these issues, this paper proposes a new real-time nowcasting forecast combination with dynamic factor regressions, which deletes redundant predictors and simultaneously selects optimal weights for candidate models. It is shown that the selected weight achieves asymptotic optimality and consistency, even when all candidate models are misspecified. The proposed estimator is consistent and asymptotically Gaussian if the true model is included in candidate models. Simulation results show that the proposed method yields lower mean square forecast errors than alternative nowcasting methods, including MIDAS in Ghysels et al. (2004), GARS in Giannone et al. (2008), and FADL-MIDAS in Andreou et al. (2013). The proposed method is applied to forecast quarterly GDP with a set of 118 macroeconomic monthly data series, which compares favorably to other competing methods.
Work in progress
Venture Capital Investment Geography
(with Qinshu Xue, Bin Zhao) Search & Matching IO
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Abstract: This paper studies the geographic concentration of VC investment in the US. We discovered that both VC firms and VC-backed companies are highly clustered in the Bay-Boston-NY area. Using the detailed Uniform Commercial Code (UCC) filings that are self-reported by lenders to stake a claim to specific pieces of collateral, we track the transaction of capital across firms. We propose the vintage capital market density as an essential determinant of VC investment concentration. Since young firms can benefit from cheap vintage capital while old firms can exit with a high scrap value, VC investments are attracted due to a high entry rate and a low exit cost. By modeling a market for the vintage capital, we aim to endogenize the scrap value of firms. Our paper highlights the critical role of the capital market in determining the industry and venture capital agglomeration. Industry policy that helps promote local capital market density would also attract VC investment in places with the greatest economic need.
Private and common information acquisition over the business cycle: Evidence from probability forecasts (with Nathan Mislang, Kristoffer Nimark) Information Acquisition Bayesian
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Abstract: We propose a method to decompose a cross-section of observed belief revisions into private and common components. We define a common signal as the single signal that if observed by all agents can explain the maximum amount of belief revisions across agents. Private signals are defined to explain the remaining residual belief revision unaccounted for by the common signal. When applied to probability forecasts from the Survey of Professional Forecasters we find that on average, private information accounts for more of the observed belief revisions than common information. However, private information tend to increase uncertainty, i.e. to lead forecasters to assign higher probability to more extreme outcomes. The importance and precision of private and common information are positively correlated over time, though the importance and precision of private information is more volatile and more strongly correlated with business cycle indicators. We argue that no existing theoretical model can explain the documented facts. The proposed method is non-parametric and only assumes that agents use Bayes rule to update their beliefs.
My Experience

Resume

Education
Ph.D. in Economics
2017-2023

Cornell University

M.S. in Economics
2015-2017

University of Wisconsin-Madison

B.S. in Economics
2011-2015

Southwest Jiaotong University.

Research Experience
Cornell University
Research Assistant

The Safety Net as a Springboard? A General Equilibrium based Policy Evaluation.
(Domenico Ferraro, Nir Jaimovich, Francesca Molinari and Cristobal Young, working paper)

Spring 2021
Cornell University
Research Assistant

Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem.
(Charles Manski and Francesca Molinari,Journal of Econometrics 2020)

Summer 2020
Cornell University
Research Assistant

Confidence Intervals for Projections of Partially Identified Parameters.
(Hiroaki Kaido, Francesca Molinari and Jorg Stoye, Econometrica 2019)

Spring 2020
TA

Teaching

Econ 6190: Econometrics I (Ph.D.)
Econ 6200: Econometrics II (Ph.D.)
Econ 1120: Introduction to Macroeconomics (Undergrad)
Econ 3120: Applied Econometrics (Undergrad)
Econ 1110: Introduction to Microeconomics (Undergrad)
Math 3110: Introduction to Analysis (Undergrad)
Math 2940: Linear Algebra for Engineers (Undergrad)
More about me

Contact

Let's connect
kkkuang

Github

economics.cornell.edu

Cornell University