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 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
Robust Bayesian Estimation and Inference for Dynamic Stochastic General Equilibrium Models (JMP)Partial Identification Bayesian
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Abstract: This paper introduces a new algorithm to conduct robust Bayesian estimation and inference in dynamic stochastic general equilibrium models. The algorithm combines standard Bayesian methods with an equivalence characterization of model solutions. This algorithm allows researchers to perform the following analysis: First, find the complete range of posterior means of both the deep parameters and any parameters of interest robust to the choice of priors in a sense I make precise. Second, derive the robust Bayesian credible region for the model parameters. I prove the validity of this algorithm and apply this method to the models in Cochrane (2011) and An and Schorfheide (2007) to achieve robust estimations for structural parameters and impulse responses. In addition, I conduct a sensitivity analysis of optimal monetary policy rules with respect to the choice of priors and provide bounds to the optimal Taylor rule parameters.
Individual and Common Information: Model-free Evidence from Probability Forecasts (with Nathan Mislang, Kristoffer Nimark) Information Acquisition Bayesian
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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
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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 LASSO penalized frequentist model averaging approach (with Yongmiao Hong, Yuying Sun) Time Series ML
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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
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Abstract: Coming soon
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Topic in macro-econometrics