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Kevin J. Lansing

Senior Research Advisor
Macroeconomic Research
Macroeconomics, Monetary economics, Asset pricing

kevin.j.lansing (at) sf.frb.org | kevin.j.lansing@gmail.com

Profiles: Google Scholar | RePEc |

Demand versus Supply: Which is More Important for Inflation?

2025-08 April 2025

I use Phillips curve type regressions to assess the relative contributions of demand and supply forces to U.S. inflation during the pandemic era from February 2020 onward and the decade following the end of the Great Recession. In the first specification (Model 1), demand and supply forces are measured using the vacancy-unemployment ratio and the New York Fed’s Global Supply Chain Pressure Index, respectively. In the second specification (Model 2), demand and supply forces are measured using the demand-driven and supply-driven components of PCE inflation from Shapiro (2025). The results derived from the two models are largely in agreement. For both models, variance decompositions imply that demand forces became more important for inflation during the pandemic era and dominated the influence of supply forces. In counterfactual simulations, both models imply that supply forces, together with the endogenous response of expected inflation, were the primary drivers of persistently low inflation after the Great Recession. Given that monetary policy operates to influence demand-driven inflation, this result helps to account for the Fed’s difficulty in achieving its 2% inflation goal during these years.

Credit-Fuelled Bubbles

2016-02 | with Doblas-Madrid | March 2016

In the context of recent housing busts in the United States and other countries, many observers have highlighted the role of credit and speculation in fueling unsustainable booms that lead to crises. Motivated by these observations, we develop a model of credit-fuelled bubbles in which lenders accept risky assets as collateral. Booming prices allow lenders to extend more credit, in turn allowing investors to bid prices even higher. Eager to profit from the boom for as long as possible, asymmetrically informed investors fuel and ride bubbles, buying overvalued assets in hopes of reselling at a profit to a greater fool. Lucky investors sell the bubbly asset at peak prices to unlucky ones, who buy in hopes that the bubble will grow at least a bit longer. In the end, unlucky investors suffer losses, default on their loans, and lose their collateral to lenders. In our model, tighter monetary and credit policies can reduce or even eliminate bubbles. These findings are in line with conventional wisdom on macro prudential regulation, and stand in contrast with those obtained by Galí (2014) in an overlapping generations context.

Learning About a Shift in Trend Output: Implications for Monetary Policy and Inflation

2000-16 | December 2000

This paper develops a small forward-looking macroeconomic model where the Federal Reserve estimates the level of potential output in real time by running a regression on past output data. The Fed’s perceived output gap is used as an input to the monetary policy rule while the true output gap influences aggregate demand and inflation. I investigate the consequences of two postulated shifts in the growth rate of U.S. potential output: the first occurs in the early-1970s and the second in the mid-1990s. Initially, Fed policymakers interpret these shifts to be cyclical shocks but their regression algorithm allows them to gradually discover the truth as the economy evolves over time. Under a Taylor-type rule, the model can produce a hump-shaped pattern in trend inflation that peaks around 1979 and a downward movement in trend inflation since 1995. Under a nominal income growth rule, these low-frequency movements in inflation are substantially reduced but not eliminated. The business cycle stabilization properties of the two rules turn out to be quite similar. Finally, using stochastic simulations, I show that efforts to identify the Fed’s policy rule using a regression based on final data can create the illusion of strong interest rate smoothing behavior when in fact none exists.