Working Papers

Inflation Target at Risk: A Time-Varying Parameter Distributional Regression. Joint work with Yunyun Wang and Tatsushi Oka.(Link)

Inflation exhibits state-dependent, skewed, and fat-tailed dynamics that make risk a central concern for monetary policy. Accordingly, inflation risks are distributional and cannot be fully captured by mean-based models. We propose a flexible time-varying parameter distributional regression model that estimates the full conditional distribution of inflation, allowing macroeconomic drivers to have nonlinear and asymmetric effects across the distribution. Applied to U.S. inflation, the model captures major shifts in tail-risk probabilities. Analysis of risk drivers shows that deflationary pressures arise primarily from demand-side weakness and inflation persistence, whereas upside risks are driven mainly by supply-side shocks, particularly energy price inflation. Examining the impact of key drivers further reveals that the unemployment-inflation relationship weakens in the distributional tails. Energy price shocks, by contrast, have little effect on deflation risk but exhibit strongly time-varying and asymmetric effects on high-inflation risk.

U.S. Economy and Global Stock Markets: Insights from a Distributional Approach. Joint work with Ping Wu. (Link)

Financial markets are interconnected, with micro-currents propagating across global markets and shaping economic trends. This paper moves beyond traditional stock market indices to examine cross- sectional return distributions—15 in our empirical application, each representing a distinct global market. To facilitate this analysis, we develop a matrix functional VAR method with interpretable factors extracted from cross-sectional return distributions. Our approach extends the existing framework from modeling a single function to multiple functions, allowing for a richer representation of cross-sectional dependencies. By jointly modeling these distributions with U.S. macroeconomic indicators, we uncover the predictive power of financial market in forecasting macro-economic dynamics. Our findings reveal that U.S. contractionary monetary policy not only lowers global stock returns, as traditionally under-stood, but also dampens cross-sectional return kurtosis, highlighting an overlooked policy transmission. This framework enables conditional forecasting, equipping policymakers with a flexible tool to assess macro-financial linkages under different economic scenarios.

Modeling and Forecasting Count Data with Bayesian Vector Autoregressions. Joint work with Davide Pettenuzzo and Aubrey Poon. (Link)

We develop a novel framework for modeling and forecasting time series of count data, extending the traditional Vector Autoregression (VAR) framework to

accommodate count-like outcomes. Our approach allows for joint modeling of multiple count and continuous variables, capturing their dynamic interactions

within a unified system. By introducing a latent utility-based structure and incorporating multivariate stochastic volatility, our method can flexibly handle

over-dispersion, skewness, and time-varying volatility in the data. We cast our model in a Bayesian framework and introduce a novel state-space representation

and an efficient sampler to handle its estimation. An extensive simulation study and two empirical macroeconomic applications illustrate the robustness and forecasting accuracy of the proposed approach.

Fast Posterior Sampling in Tightly Identifed SVARs Using ‘Soft’ Sign Restrictions. Joint work with Matthew Read. (Link)

We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on ‘soft’ sign restrictions. This step draws from a target density that smoothly penalises parameter values violating the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws from the desired distribution conditional on the ‘hard’ sign restrictions. Relative to standard accept-reject sampling, the method substantially improves computational efficiency when identification is ‘tight’. It can also greatly reduce the computational burden of implementing prior-robust Bayesian methods. We illustrate the broad

applicability of the approach in a model of the global oil market identified using a rich set of sign, elasticity and narrative restrictions.

Uncertainty and the Term Structure of Interest Rates. Joint work with Jamie Cross and  Aubrey Poon.(Link)

We present a new stylized fact about the link between uncertainty and the term structure of interest rates: Unexpectedly heightened uncertainty elicits a lower, steeper, and fatter yield curve. This result is established through a Yields-Macro model that includes dynamic Nelson-Siegel factors of U.S. Treasury yields, and accounts for endogenous feed back with observable measures of uncertainty, monetary policy, and macroeconomic aggregates. It is also robust to three distinct measures of uncertainty pertaining to the financial sector, the macroeconomy and economic policy. An efficient Bayesian algorithm for estimating the class of Yields-Macro models is also developed.