Yun Young Gwak

Distinguishing between sector-specific and aggregate shocks and assessing their contributions to inflation are vital for informed policy. This paper quantifies cross-sectoral spillovers in U.S. consumer price inflation using a factor-adjusted network approach that jointly models aggregate factors and sectoral network propagation. Using disaggregated personal consumption expenditure data across 26 sectors from 1959–2024, the model employs Lasso nuclear-norm regularization to estimate high-dimensional VARs while controlling for aggregate influences. Cross-sectoral spillovers account for roughly two-fifths of total price variation—more than twice the share attributable to aggregate factors—and are systematically mismeasured in conventional models: factor models understate spillovers by absorbing network transmission into common components, while VARs without factors  overstate them by conflating comovement with propagation. The spillover structure is highly granular, dominated by large consumer-facing sectors such as food, furnishings, and services, with gasoline exerting more moderate but persistent effects. Spillovers propagate mainly through backward production linkages and scale with sector size, indicating that large downstream sectors play a disproportionate role in transmitting sector-specific shocks across the price network. The findings underscore the need for integrating sectoral networks and aggregate factors in modeling inflation dynamics and policy design.

This paper investigates the impact of financial conditions on inflation expectations, with a particular focus on the evolving mandates of central banks since the global financial crisis. As institutions increasingly incorporate financial stability alongside price stability, understanding the interplay between these objectives becomes critical. We seek to answer: How responsive are expectations to financial shocks? And how do monetary policy and financial conditions interact? Using state-dependent local projection methods combined with Kitagawa-Blinder-Oaxaca decompositions, we aim to quantify the marginal effects of financial shocks on inflation expectations, conditional on monetary-financial interactions. The findings will inform whether central banks should more explicitly consider financial conditions in their efforts to manage inflation dynamics.

What causes global inflation and what can monetary policy do about it? This project addresses this fundamental question by examining how global forces shape inflation and exploring their implications for domestic monetary policy in open economies. This study has two aims: (1) to quantify the extent to which global forces influence inflation across countries using dynamic factor models and (2) to understand how monetary policy should respond to these global forces. The primary focus is on the first aim, employing econometric methods to identify and establish causal links between global influences and inflation dynamics. The secondary aim, concerning policy responses, is explored with less emphasis but remains critical in interpreting the results, especially given the recent synchronization of policy rates across central banks worldwide. 

This project investigates sectoral heterogeneity in the Phillips curve relationship between inflation and economic slack. Building on the multi-sector New Keynesian framework of Rubbo (2023), we estimate sectoral Phillips curves using disaggregated U.S. data. Our identification strategy exploits cross-sectional variation in sectoral characteristics and further tests nonlinear interactions with sectoral traits such as intermediate input shares, production network positions, price rigidities, and global value chain participation, which are interacted with instrumented output gaps. Relevant findings would have direct implications for monetary policy: the aggregate Phillips curve reflecting the economy's evolving sectoral composition and network architecture.