Yizhou Kuang (匡逸舟)

I am a Lecturer (Assistant Professor) at the Department of Economics, University of Manchester. My research interests lie at the intersection of econometrics and macroeconomics, encompassing both theoretical and applied aspects. 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.



Working papers
Bayesian Sensitivity Analysis for Set-identified Structural Models
(JMP, Submitted) 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 Nathan Mislang, Kristoffer Nimark, Submitted) 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.
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.
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.
Work in progress
Identification-driven Monte Carlo
(with Toru Kitagawa) Partial Identification Bayesian
 PDF      Slides   
Abstract: Coming soon

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Reference Letter Policy
Topic in macro-econometrics