Monetary Policy Shocks

I’m excited to share my latest research on how conventional monetary policy shocks propagate through the U.S. economy. In "Exploring Monetary Policy Shocks with Large-Scale Bayesian VARs" I develop a high-dimensional Bayesian VAR that: 🎾 Provides a comprehensive Bayesian methodology and computational algorithm for assessing monetary policy in data-rich environments 🏀 Merges high-frequency Fed surprises with sign restrictions for robust shock recovery 🏈 Allows time-varying effects, stochastic volatility, and fat-tailed errors to handle pandemic-era outliers Applied to U.S. data through May 2024, the model reveals pronounced heterogeneity in CPI responses: core goods disinflated swiftly after Fed tightening, while services, especially housing, adjusted much more gradually, with these dynamics shifting markedly during the 2022-24 inflation surge. If you’re curious about the evolving transmission of monetary policy using large VAR methods, take a look: 👉 https://lnkd.in/eAXyk_gd Feedback and discussion are welcome! #BayesianVAR #MonetaryPolicy #InflationDynamics #Macroeconometrics

How does monetary policy shape who gets a mortgage? 🏠💶 I’m excited to share my new working paper published by the National Bank of Belgium, co-authored with Salima OUERK. We study how ECB monetary policy surprises affect new mortgage lending across the household income distribution in France, using loan-level data from the national credit registry. We uncover a striking U-shaped pattern: ➡️ Middle-income households, especially first-time buyers, react the most to changes in financing conditions. These households, while creditworthy, remain liquidity constrained and therefore more sensitive to both monetary policy and macroeconomic signals. Our results highlight the expectations channel and the information channel: - Expansionary pure policy and forward guidance surprises boost credit demand. - Positive macroeconomic signals also raise borrowing. A one-standard-deviation expansionary surprise over two years leads to a 10% increase in new mortgage lending among middle-income households. 🔗 Read the full paper here: https://lnkd.in/eDSNiYYA

𝐎𝐩𝐞𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐁𝐥𝐚𝐜𝐤 𝐁𝐨𝐱 𝐨𝐟 𝐋𝐨𝐜𝐚𝐥 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐢𝐨𝐧𝐬 is my new working paper with Karin Klieber. (paper: https://lnkd.in/e8Z29EeJ, deck: https://lnkd.in/eGEUCJsr). Local projections are now routinely used to estimate impulse response functions (IRFs) in empirical macroeconomics. Yet in many ways, they are black boxes. The mechanisms behind the curves are often unclear. Perhaps most importantly: do the episodes we think are driving the causal effect estimates actually 𝘥𝘰 𝘴𝘰? Are those numerous enough to be confident about external validity? We introduce a 𝗻𝗲𝘄 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 of LP estimates that makes their historical foundation transparent. Each estimate is expressed as a sum of contributions from individual events—weights times observed responses—revealing how specific periods shape the average effect. We plot cumulative contributions over time, which converge by construction to the LP estimate at horizon 𝘩 (see figure below). We also visualize the weights as a time series and introduce LP concentration statistics to summarize how broad or narrow the evidence base is. 𝗧𝗵𝗲 𝗺𝗲𝗮𝗻𝗶𝗻𝗴(𝘀) 𝗼𝗳 𝘄𝗲𝗶𝗴𝗵𝘁𝘀. In linear local projections estimated via least squares, the weight series admits two interpretations. First, by a variation of the Frisch-Waugh-Lovell theorem, the weights can be viewed as purified and standardized shocks. Second, they are proximity scores, measuring the similarity between the projected intervention and past interventions in the sample. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴. The proximity-based interpretation of weights extends beyond linear models, as many machine learning (ML) algorithms generate IRFs that, while nonlinear in regressors, remain linear in the outcome variable. Proximity weights offer a common scale for comparing linear and ML-based LPs. 𝗘𝗺𝗽𝗶𝗿𝗶𝗰𝗮𝗹𝗹𝘆, we apply the method to various domains: ▶ 𝘔𝘰𝘯𝘦𝘵𝘢𝘳𝘺 𝘱𝘰𝘭𝘪𝘤𝘺: We find that Cholesky VAR shocks produce a price puzzle by misreading stagflation episodes from the 1970s. Romer and Romer (2004) shocks get the IRF sign right by offsetting this misinterpretation with a long stretch of monetary loosening shocks coinciding with the peak of late 1970s inflation. Random Forest refines the portrait by assigning elevated proximity weights only to well-known episodes of political interference with the Fed. ▶ 𝘍𝘪𝘴𝘤𝘢𝘭 𝘱𝘰𝘭𝘪𝘤𝘺: Ramey and Zubairy’s (2018) state-dependent fiscal multipliers in recessions are driven almost entirely by a single event: World War II. ▶ 𝘊𝘭𝘪𝘮𝘢𝘵𝘦 𝘴𝘩𝘰𝘤𝘬𝘴: The long-run GDP impact of global temperature shocks in Bilal and Känzig (2024) appears fragile, primarily driven by the pairing of a single 1960s volcanic eruption and exceptional post-war growth. École des sciences de la gestion (ESG UQAM) Oesterreichische Nationalbank, European Central Bank #econometrics #causalinference #economics #machinelearning

We have a new paper (https://lnkd.in/eQHt8att) on the effects of conventional monetary policy on output and prices, based on a multi-year data collection effort. We collect and analyse 146,463 point estimates and confidence bands from 4,871 impulse-response functions reported in 409 primary studies (joint work with Matthias Enzinger, Sebastian Gechert, Franz Prante and Daniel Romero). Our main finding: We show that the results reported in the literature on how output and prices respond to conventional monetary policy shocks are plagued by p-hacking and publication bias, leading to inflated effect sizes. p-hacking is the preference for statistically significant results. Publication bias includes p-hacking but also a tendency to prefer large effect sizes or to conform to theoretical expectations and seminal publications. We document a robust pattern of selective reporting of statistically significant dampening effects of contractionary monetary policy shocks on output and prices, in particular at the most relevant response horizons. The naïve average of all IRFs points to substantial contractionary effects of a 100 basis points interest rate hike on output (with a peak effect of −1 percent after around 2 years) and the price level (with a peak effect of −0.75 percent after 4 to 5 years). However, such a conclusion would be misleading since the literature suffers from substantial publication bias according to a series of established tests. When we correct for this bias, the resulting range of corrected IRFs points to substantially smaller dampening effects of contractionary monetary policy shocks on the economy. The strongest corrections would be consistent with zero effects and the mildest corrections would imply a peak effect of −0.7 percent for output and −0.5 percent for the price level. The mean IRF beyond publication bias for output peaks at −0.25 percent after 1 to 2 years and for the price level at −0.15 percent after 4 years. Bias corrections reduce effect sizes by half or more. Our findings suggest that the power of conventional monetary policy to steer prices and the business cycle may have been overstated in the past, based on a simple average assessment of the empirical literature, seminal empirical studies in leading journals, the predictions of standard New Keynesian models and a summary given by a leading AI. We also investigate how study and estimation characteristics such as identification strategies, samples, author affiliations, and journal ranking are related to the variation of reported effect sizes. Shock identification choices and publication characteristics correlate with effect sizes but are quantitatively less important than publication bias. Link to our paper (comments welcome): https://lnkd.in/eQHt8att The documentation and replication files are available via: https://lnkd.in/e-wJG8cj Pre-registration: https://osf.io/cduq4

Where the fiscal stops, the monetary begins: Today's budget was a clear shift of focus from capex to consumption. While the last few years had seen a major shift and increase to capex spending, for FY26, capex remains almost constant at 11.1 TN INR. It is perhaps high time that the so called private crowding in effect of high govt capex actually take place which has been missing for last few years. It is the consumption push which this budget focusses on by rationalisation of tax slabs across all income slabs. But consumption always has lower multiplier effect on growth than investment spending. Hence the core issue of anemic growth remains unsolved considering today's budget still continues to be fiscally conservative. So if the problem statement is that there is a growth issue with Indian economy then the solution now entire lies in monetary levers since fiscal lever has not been used in today's budget. Assuming potential output at 7% for Indian economy even at 6.5% for FY26 (which looks aggressive), output loss is significant. And most of the output loss is on account of high real rates & misplaced notions of FX stability & inflation growth dynamics. Indian equities saw a 78k Crs outflow from FPIs in Jan'25. For FY25, it is -89k Crs. FPI shareholdings in equities have fallen from long term average of 18-19% levels to 16%. FPIs are exiting not only because they are getting comfortable exit levels because of domestic fund flows but also because India's current growth nos do not justify the high valuations. Only if the FPIs see a growth opportunity then only they might return to equities. That will also solve the deprecation issue of INR. So contrary to theoretical concept that lower rates might lead to further depreciation, the reverse could be true. And this is evident in recent monetary policy actions of Indonesia, China, ECB. Each country is trying to preserve it's growth in an environment of global instability due to tariffs/wars/dxy strength. Moving on to monetary side, local policy rates have been kept elevated to protect INR volatility as well as high food inflation. But this has led to a significant growth sacrifice. Perhaps it is time to let INR function as per market forces and use the monetary lever to stimulate growth. Else both INR & growth will suffer. Hence the need for monetary action to cut rates by 75bps in FY26 in an environment of sufficient core liquidity. FY27 has a huge repayment wall of 7TN INR in IGBs. As the banker for GoI, refinancing such a huge maturity at elevated rates will only spoil the interest serviceability of the sovereign. For that reason too, local rates need to come down significantly in FY26. Summary: While there might be short term pain points for bond yields due to higher switch, slightly higher gross borrowing etc today, in medium term local rates in India can fall significantly in FY26. Expecting 50 bps CRR cut & the 1st 25 bps repo rate cut in Feb policy. Total 75-100 bps cut in FY26. O

How should monetary policy respond to the energy price shock? We are in the midst of a major shock to global energy markets. And there is genuine uncertainty — in the Frank Knight sense — about how this will evolve: how much further might prices rise, and for how long? Yesterday, the MPC voted unanimously to leave Bank Rate unchanged at 3.75%. In my blog this week, I look at how monetary policy should respond from here. https://lnkd.in/ecddG2gh I draw out five in particular, using a simple New Keynesian framework augmented to include imported energy in consumption and production, staggered wage-setting, and Nash bargaining in the labour market. Lesson 1: an energy shock makes the UK poorer. As an energy importer, a rise in oil and gas prices is a terms-of-trade shock. If it persists, there will be a persistent hit to living standards. That is the uncomfortable starting point, and it is something policymakers need to explain clearly. Lesson 2: monetary policy cannot prevent the first-round effect of that loss of income. A large energy price shock will feed mechanically into headline CPI. The Bank of England cannot prevent that direct effect. Lesson 3: what matters is not the oil shock itself, but whether it triggers second-round effects. This is central-bank jargon for the risk that the initial hit to prices propagates into wages, mark-ups and broader inflation persistence. Here I think the comparison with 2022 is critical. The labour market has loosened materially since then: vacancies have fallen, unemployment has risen, and wage growth has moderated. These are not the conditions in which a wage-price spiral is most likely to take hold. Lesson 4: monetary policy should react to domestically generated inflation, not mechanically to the initial rise in headline CPI. In the model, a Taylor rule focused on core or domestic inflation performs much better than one that responds directly to headline CPI. That is another way of saying: look through the first round, lean against the second. Lesson 5: the right response is patience on rates, paired with much clearer scenario-based communication. The Bank should be ready to respond forcefully if second-round effects do emerge. But it should not tighten simply because oil prices have risen. This is exactly the sort of environment in which scenario analysis should come to the fore. Some have argued that the Bank should lean aggressively against the shock because of the scars left by 2022. I think that is the wrong lesson. The focus should be on core inflation and on whether the economy is showing signs of renewed persistence — not on the oil price in isolation. Here's my favourite simulation from the model, showing how the second round effects differ depending on the tightness of the labour market.

Me: May I meet you? MPC Rate: Yes. Zambian Economy: I am not who you think I am. There is something we need to confront honestly this week. Monetary policy is set for one unified economy, but Zambia is not one economy. It is a set of parallel tracks that respond differently to the same signal. The three rate charts below illustrate part of the story. When the policy rate rose, retail borrowers absorbed the shock while corporate lending stayed insulated. Margins narrowed in both segments, but the decrease was far less pronounced for households with limited bargaining power. Meanwhile, 12-m retail deposits barely moved, while banks selectively paid up for corporate funding to defend their liquidity. This is why a 25-basis-point cut cannot deliver broad relief. The structure does not transmit policy. It protects balance sheets and preserves margins. Although we do not have comparable disaggregated evidence for behaviour in an easing environment, as the Bank of Zambia only began publishing disaggregated retail and non-retail lending rate data in 2023, the structure itself offers guidance. Retail borrowers are captive. Corporates multi-bank and shift business quickly. The dynamics that created insulation during tightening will shape behaviour in easing. The architecture beneath the charts matters. The system remains shallow, dollar-dominated, and sovereign-centred. A lower policy rate is not the same as improving affordability. It does not close the gap between those who can borrow and the many who cannot. Moreover, because lower inflation only means prices rise more slowly, it does not ease the real cost of living for many outside the formal credit channel. Zambia has a structurally K-shaped household economy and a four-track credit economy. • A small formal segment with access to credit. • A larger formal segment with income but no access. • An informal sector where income is unstable and inflation bites hardest. • A separate informal credit ecosystem outside monetary policy. One policy rate. Four different outcomes. This is why the conversation about easing requires discipline. A premature cut offers comfort to the small formal segment that already borrows. It does not change access for new entrants or expand inclusion. The majority, who carry the highest inflation burden, feel nothing. In practice, easing under these conditions slows disinflation by sustaining spending power for the least vulnerable while prices remain elevated for everyone else. This is not an argument for permanent tightness. It is an argument for sequence—structure before sentiment. Transmission must be built before easing can work. On Wednesday, I will release Part Two of the series titled "Money Base and Credit. Why Easing Will Not Transmit." It will walk through the data in detail, including money supply structure, foreign currency share in deposits, IMF findings, and the allocation patterns that shape daily economic life. Image: Bank of Zambia, Own Research

Some papers arrive as theory; others arrive as diagnosis. The latest International Monetary Fund Working Paper, "𝘛𝘩𝘦 𝘊𝘦𝘯𝘵𝘳𝘢𝘭 𝘉𝘢𝘯𝘬’𝘴 𝘋𝘪𝘭𝘦𝘮𝘮𝘢: 𝘓𝘰𝘰𝘬 𝘛𝘩𝘳𝘰𝘶𝘨𝘩 𝘚𝘶𝘱𝘱𝘭𝘺 𝘚𝘩𝘰𝘤𝘬𝘴 𝘰𝘳 𝘊𝘰𝘯𝘵𝘳𝘰𝘭 𝘐𝘯𝘧𝘭𝘢𝘵𝘪𝘰𝘯 𝘌𝘹𝘱𝘦𝘤𝘵𝘢𝘵𝘪𝘰𝘯𝘴?", is both. Its importance lies in the problem it refuses to simplify: supply shocks are no longer side events in the inflation story; they are central to it.  Oil, food, logistics, geopolitics, and exchange-rate pass-through keep reminding small open economies that inflation is not always made at home. 𝗧𝗵𝗲 𝗽𝗮𝗽𝗲𝗿 𝗮𝘀𝗸𝘀 𝗮 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗰𝗲𝗻𝘁𝗿𝗮𝗹-𝗯𝗮𝗻𝗸𝗶𝗻𝗴:  when should policymakers look through supply shocks, and when must they tighten to prevent expectations from drifting? At its core, the paper argues that monetary policy must be state-contingent. Central banks can look through supply shocks while expectations remain anchored, but they must pivot decisively when the anchor begins to move. 𝘈𝘮𝘰𝘯𝘨 𝘵𝘩𝘦 𝘵𝘩𝘦 𝘬𝘦𝘺 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘱𝘢𝘱𝘦𝘳 𝘢𝘳𝘦: 1. 𝘚𝘶𝘱𝘱𝘭𝘺 𝘴𝘩𝘰𝘤𝘬𝘴 𝘤𝘳𝘦𝘢𝘵𝘦 𝘢 𝘳𝘦𝘢𝘭 𝘮𝘰𝘯𝘦𝘵𝘢𝘳𝘺-𝘱𝘰𝘭𝘪𝘤𝘺 𝘥𝘪𝘭𝘦𝘮𝘮𝘢. Looking through shocks can protect output and employment, but doing so too long can de-anchor inflation expectations. 2. 𝘌𝘹𝘱𝘦𝘤𝘵𝘢𝘵𝘪𝘰𝘯𝘴 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘢𝘭 𝘤𝘦𝘯𝘵𝘳𝘦 𝘰𝘧 𝘨𝘳𝘢𝘷𝘪𝘵𝘺. The interesting case is bounded rationality: agents partly understand policy, but do not fully internalize its implications. 3. 𝘖𝘱𝘵𝘪𝘮𝘢𝘭 𝘱𝘰𝘭𝘪𝘤𝘺 𝘱𝘪𝘷𝘰𝘵𝘴. The central bank may look through shocks initially, but once they cumulate beyond a threshold, the optimal response becomes a sharp hawkish shift. 4. 𝘈 𝘱𝘪𝘷𝘰𝘵 𝘤𝘢𝘯 𝘭𝘰𝘸𝘦𝘳 𝘪𝘯𝘧𝘭𝘢𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘧𝘰𝘳𝘤𝘪𝘯𝘨 𝘢 𝘳𝘦𝘤𝘦𝘴𝘴𝘪𝘰𝘯. If tightening re-anchors expectations, inflation can fall through the expectations channel rather than mainly through engineered slack. 5. 𝘋𝘦𝘭𝘢𝘺 𝘪𝘴 𝘤𝘰𝘴𝘵𝘭𝘺 𝘸𝘩𝘦𝘯 𝘦𝘹𝘱𝘦𝘤𝘵𝘢𝘵𝘪𝘰𝘯𝘴 𝘣𝘦𝘨𝘪𝘯 𝘵𝘰 𝘮𝘰𝘷𝘦. Once expectations absorb repeated shocks, tightening too late or too slowly becomes expensive. For emerging and frontier economies, the message is especially relevant. Food and fuel shocks move transport costs, wage demands, fiscal politics, and household welfare. A central bank may know inflation is imported, but households live in prices, not decompositions. That is why this paper is worth reading. It does not say: always look through. It does not say: always tighten. It says something more useful: look through while the anchor holds; pivot when the anchor starts to move.

Working paper alert: "Production Networks and the (Asymmetric) Transmission of Monetary Policy" Using producer prices for 271 U.S. industries, I find that more upstream industries—those farther from the final consumer—adjust prices substantially more after a monetary shock. This is because upstream firms reprice more often (while cost-cascading through the supply chain plays a secondary role). The network architecture decides who is flexible. Importantly, the effect is asymmetric: large price increases after rate cuts, near zero after rate hikes—a "rockets and feathers" pattern across the production network. That is, it's easier for monetary policy to create inflation than to fight it, and the supply chain is part of why. The potency of monetary policy depends on the economy's production structure, not just average price stickiness—something representative-firm models miss. Paper: https://lnkd.in/eAbaaPmt. Feedback welcome. #MonetaryPolicy #Macroeconomics #Inflation #Network #Supplychain