Yizhou Kuang (匡逸舟)
I joined the Department of Economics at the University of Manchester as a Lecturer (Assistant Professor) in 2023, following my graduation from Cornell University. My research focuses on macroeconometrics. I am also corganizing the brown bag seminars and co-organizing the econometrics seminars at Manchester.
Outside of work, my enthusiasm lies in rock climbing, especially bouldering. If you share this passion, I welcome the opportunity to climb together.
WHAT I DO
Research
Working Papers
Bayesian Sensitivity Analysis for Set-identified Structural Models
(JMP, Under Review) Partial Identification Bayesian
PDF
Abstract: This paper proposes a new algorithm to conduct robust Bayesian analysis for set-identified structural models. It combines standard Bayesian procedure with a characterization of observationally equivalent parameters. The algorithm finds the range of posterior means and the Bayesian credible region of both the structural parameters and any parameters of interest, ensuring robustness against the selection of priors within a class that produces identical marginal likelihoods. I provide theoretical support for this algorithm and apply the method to several monetary policy models, to show its relevance in policy analysis. The methodology finds that, in set-identified models, parameters of primary interest like impulse responses can have very different implications based on the prior, even within the same class. Additionally, optimal monetary policy rules could vary with the choice of prior within that class, particularly when historical policy parameters are not identified.
(JMP, Under Review) Partial Identification Bayesian
Abstract: This paper proposes a new algorithm to conduct robust Bayesian analysis for set-identified structural models. It combines standard Bayesian procedure with a characterization of observationally equivalent parameters. The algorithm finds the range of posterior means and the Bayesian credible region of both the structural parameters and any parameters of interest, ensuring robustness against the selection of priors within a class that produces identical marginal likelihoods. I provide theoretical support for this algorithm and apply the method to several monetary policy models, to show its relevance in policy analysis. The methodology finds that, in set-identified models, parameters of primary interest like impulse responses can have very different implications based on the prior, even within the same class. Additionally, optimal monetary policy rules could vary with the choice of prior within that class, particularly when historical policy parameters are not identified.
Individual and Common Information: Model-free Evidence from Probability Forecasts
(with Kristoffer Nimark, Under Revision) Information Acquisition Bayesian
PDF Slides
Abstract: We propose a method to empirically decompose a cross-section of observed belief revisions into components driven by individual and common information under weak assumptions. 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. Individual signals are defined to explain the residual belief revisions unaccounted for by the common signal. When applied to probability forecasts from the Survey of Professional Forecasters we find that individual signals account for more of the observed belief revisions than common signals. There is a large cross-sectional heterogeneity in signal precision, with about 2/3 of forecasters observing individual signals that are more precise than the common signal. Unconditionally, the informativeness of individual and common signals are positively correlated. Inflation volatility, perceived stock market volatility and a high risk of recession are all factors associated with increased informativeness and precision of both individual and common signals. We discuss the implications of our findings for theoretical models of information acquisition and we show how our procedure maps into alternative information structures.
(with Kristoffer Nimark, Under Revision) Information Acquisition Bayesian
PDF Slides
Abstract: We propose a method to empirically decompose a cross-section of observed belief revisions into components driven by individual and common information under weak assumptions. 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. Individual signals are defined to explain the residual belief revisions unaccounted for by the common signal. When applied to probability forecasts from the Survey of Professional Forecasters we find that individual signals account for more of the observed belief revisions than common signals. There is a large cross-sectional heterogeneity in signal precision, with about 2/3 of forecasters observing individual signals that are more precise than the common signal. Unconditionally, the informativeness of individual and common signals are positively correlated. Inflation volatility, perceived stock market volatility and a high risk of recession are all factors associated with increased informativeness and precision of both individual and common signals. We discuss the implications of our findings for theoretical models of information acquisition and we show how our procedure maps into alternative information structures.
Identification-driven Markov Chain Monte Carlo
(with Toru Kitagawa) Partial Identification Bayesian
PDF Slides
Abstract: Leaving posterior sensitivity concerns aside, non-identifiability of the parameters does not raise a difficulty for Bayesian inference as far as the posterior is proper, but multi-modality or flat regions of the posterior induced by the lack of identification leaves a challenge for modern Bayesian computation. Sampling methods often struggle with slow or non-convergence when dealing with multiple modes or flat regions of the target distributions. This paper develops a novel Markov chain Monte Carlo (MCMC) approach for non-identified models, leveraging the knowledge of observationally equivalent sets of parameters, and highlights an important role that identification plays in Bayesian analysis. We show that our proposal overcomes the issues of being trapped in a local mode and achieves a faster rate of convergence than the existing MCMC techniques including random walk Metropolis-Hastings and Hamiltonian Monte Carlo. The gain in the speed of convergence is more significant as the dimension or cardinality of the identified sets increases. Simulation studies show its superior performance compared to other popular computational methods including sequential Monte Carlo. We illustrate use and effectiveness of our proposal in a non-invertible structural vector moving average (SVMA) model.
(with Toru Kitagawa) Partial Identification Bayesian
PDF Slides
Abstract: Leaving posterior sensitivity concerns aside, non-identifiability of the parameters does not raise a difficulty for Bayesian inference as far as the posterior is proper, but multi-modality or flat regions of the posterior induced by the lack of identification leaves a challenge for modern Bayesian computation. Sampling methods often struggle with slow or non-convergence when dealing with multiple modes or flat regions of the target distributions. This paper develops a novel Markov chain Monte Carlo (MCMC) approach for non-identified models, leveraging the knowledge of observationally equivalent sets of parameters, and highlights an important role that identification plays in Bayesian analysis. We show that our proposal overcomes the issues of being trapped in a local mode and achieves a faster rate of convergence than the existing MCMC techniques including random walk Metropolis-Hastings and Hamiltonian Monte Carlo. The gain in the speed of convergence is more significant as the dimension or cardinality of the identified sets increases. Simulation studies show its superior performance compared to other popular computational methods including sequential Monte Carlo. We illustrate use and effectiveness of our proposal in a non-invertible structural vector moving average (SVMA) model.
Work in Progress
BVARs, Generalized Bayes, and Outliers
(with Dimitris Korobilis) Bayesian
PDF Slides
Abstract: not yet available
(with Dimitris Korobilis) Bayesian
PDF Slides
Abstract: not yet available
Supervised MIDAS Latent-Factor Regression
(with Rosnel Sessinou) Time Series ML
PDF Slides
Abstract: not yet available
(with Rosnel Sessinou) Time Series ML
PDF Slides
Abstract: not yet available
Dormant Projects
Venture Capital Investment Geography
(with Qinshu Xue, Bin Zhao) Search & Matching IO
PDF Slides
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.
(with Qinshu Xue, Bin Zhao) Search & Matching IO
PDF Slides
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.
Centralized or Decentralized? An Empirical Model on Task Assignment of Government in Pandemics
(with Qiwei He) Partial Identification IO Time Series Bayesian
PDF Slides
Abstract: This paper empirically studies the government task assignment problem in the context of dynamic policy implementation. We focus on effective mitigation policy design to reduce virus spread in the COVID-19 pandemics. We start by estimating a structural SIR model with regional spillover effects using indirect inference to model virus transmission. Then, we develop and estimate a dynamic game model where each U.S. state independently forms mitigation policies. 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.
(with Qiwei He) Partial Identification IO Time Series Bayesian
PDF Slides
Abstract: This paper empirically studies the government task assignment problem in the context of dynamic policy implementation. We focus on effective mitigation policy design to reduce virus spread in the COVID-19 pandemics. We start by estimating a structural SIR model with regional spillover effects using indirect inference to model virus transmission. Then, we develop and estimate a dynamic game model where each U.S. state independently forms mitigation policies. 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 Penalized Model Averaging Approach
(with Yongmiao Hong, Yuying Sun) Time Series ML
PDF 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. We show 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.
(with Yongmiao Hong, Yuying Sun) Time Series ML
PDF 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. We show 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.
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My Updates
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- Conference
- Seminars
- Visiting
SETA 2025
June 1-3
Macau, China.
Cambridge Econometrics Seminar
March 12
Cambridge, UK.
Aarhus Workshop in Econometrics V
October 24 - October 25
Aarhus, Denmark.
More about me
Contact
Office hour
By appointmentOffice
Room 3.004, Arthur Lewis Building
Department of Economics, SoSS
University of Manchester
Oxford Road, Manchester
M13 9PL
United Kingdom