软着陆还是滞胀？宏观情景概率估计框架

Federal Reserve Board, Washington, D.C.ISSN 1936-2854 (Print)ISSN 2767-3898 (Online) Soft Landing or Stagflation? A Framework for Estimating theProbabilities of Macro Scenarios Eric Engstrom 2025-047 NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment.The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or theBoard of Governors. References in publications to the Finance and Economics Discussion Series (other thanacknowledgement) should be cleared with the author(s) to protect the tentative character of these papers. Soft Landing or Stagflation? A Framework for Estimating the Probabilities of Macro Scenarios 2025 Federal Reserve Board1 Introduction Recent changes in trade policy and ongoing global economic developments have introduced newsources of uncertainty into the U.S. macroeconomic outlook. These developments—ranging fromshifts in tariff schedules to geopolitical conflict—may affect both inflation dynamics and thetrajectory of real economic activity. Against this backdrop, forecasters and policymakers may findit useful to assess the probabilities of various macroeconomic scenarios. Two salient scenarios atthe time of this writing are a soft landing, in which inflation declines to target levels of around 2percent while growth remains positive, and a stagflationary episode, characterized by elevatedinflation alongside subdued or negative output growth. This paper uses a quasi-structural framework to quantify the relative likelihood of these twoscenarios over a four-quarter horizon. The analysis builds on the approach developed by Bekaert,Engstrom, and Ermolov (2025, BEE henceforth)2, which generates joint density forecasts for keymacroeconomic variables by modeling the underlying structural supply and demand shocks. Thismethodology is particularly well suited to the present environment for several reasons. First, itenables a decomposition of observed macroeconomic variation into supply-driven and demand-driven components, allowing a clearer interpretation of inflation and output risks. Second, themodel incorporates time-varying, asymmetric risks, capturing important features such as fat tailsand skewness that are often present during periods of heightened uncertainty. Third, the modeldelivers joint predictive distributions—rather than just point forecasts—allowing for probabilisticassessments of scenarios such as stagflation or soft landings. These distributional forecasts may be informative for both financial market participants andpolicymakers. Market participants can use them to better understand the balance of risks aroundinflation and growth, with implications for pricing interest rate derivatives, inflation-linked assets,and macro-sensitive equities. Policymakers, particularly those operating under dual mandates, maybenefit from tools that clarify whether risks to inflation and growth mandates are aligned, or inconflict. In that sense, this framework helps quantify potential policy tradeoffs under evolvingmacroeconomic conditions. The main results of this paper are estimates of the relative probabilities of a stagflationaryscenarios versus a soft landing in the U.S., and how those probabilities have evolved over time. Topreview, the model estimates that the probability of a stagflation scenario was substantiallyelevated coming out of the pandemic. For instance, at the end of 2022 after inflation had reachedpeak levels and the Federal Reserve had begun an aggressive path of raising interest rates, theprobability of at least mild stagflation –defined as inflation exceeding 3 percent while real GDPgrowth registers below 1 percent on a four-quarter basis—is estimated to have been about 35percent. At the time, the probability of a soft landing over the coming four quarters–defined asinflation returning to near 2 percent while growth remained solid— was estimated to be low,below 5 percent. Over the next couple of years as growth remained solid and inflation slowlydeclined towards the Federal Reserve’s 2 percent goal, the model-implied probability of stagflationis estimated to have fallen steadily by the end of 2024 to about 5 percent, while the probability of asoft landing rose to about 30 percent. By mid-2025, however, that trend had reversed, with theprobability of at least mild stagflation rising precipitously and the probability of a soft landingdeclining notably. The shift in 2025 can be attributed mostly to the uncertain effects of tariffs onthe outlooks for inflation and growth. However, the probability of severely stagflationary scenariosdid not increase materially. Summary of Methodology A detailed description of the methodology, data, estimation, and validation techniques used in thisanalysis is provided in the appendix. A brief summary is presented here for readers who may notrequire the ful