Abstract We review recent research and experiences linking inflation and expectations, emphasizing what has been learned since 2020. One clear lesson is that the inflation expectations of most economic agents have been and remain unanchored. The unanchored nature of inflation expectations, in combination with supply shocks, can explain much of the inflation surge and subsequent disinflation when viewed through the lens of an expectations-augmented Phillips curve, both in the U.S. and abroad.

Abstract This paper analyses possibly time-varying shock transmission in structural vector autoregressive (VAR) models when the reduced-form VAR coefficients are time-invariant and the shocks are identified through non-Gaussianity. To check for possible time-variation in the impulse responses, we propose Wald tests for two situations: (1) homoskedastic and (2) heteroskedastic structural shocks with changes in the unconditional variances. For the latter case, the challenge is to ensure that the test does not indicate time-varying impulse responses if the changes are due only to changes in the variances of the shocks. To illustrate the usefulness of the tests, they are applied to an empirical model of the crude oil market. They support time-varying shock transmission reflected in impulse response functions that change over time.

Abstract Different proxy variables used in fiscal policy SVARs lead to contradicting conclusions regarding the size of fiscal multipliers. Our analysis suggests that the conflicting results may stem from violations of the proxy exogeneity assumptions. We propose a novel approach to include proxy variables into a Bayesian non-Gaussian SVAR, tailored to accommodate potentially endogenous proxies. Using our model, we find that increasing government spending is more effective in stimulating the economy than reducing taxes.

Abstract This paper extends difference-in-differences to settings with continuous treatments. Specifically, the average treatment effect on the treated (ATT) at any level of treatment intensity is identified under a conditional parallel trends assumption. Estimating the ATT in this framework requires first estimating infinite-dimensional nuisance parameters, particularly the conditional density of the continuous treatment, which can introduce substantial bias. To address this challenge, we propose estimators for the causal parameters under the double/debiased machine learning framework and establish their asymptotic normality. Additionally, we provide consistent variance estimators and construct uniform confidence bands based on a multiplier bootstrap procedure. To demonstrate the effectiveness of our approach, we apply our estimators to the 1983 Medicare Prospective Payment System (PPS) reform studied by Finkelstein (2008), reframing it as a DiD with continuous treatment and nonparametrically estimating its effects.

Abstract This study presents a model that enables automatic trend detection in Bayesian vector autoregressions (BVARs). The proposed model features cyclical components that follow a stationary VAR and trend components that evolve as a random walk. We employ a spike-and-slab prior on the variance of shocks in the trend component, allowing for the automatic identification of stochastic trends and, if present, their estimation within the same Gibbs sampling procedure. A marginal likelihood comparison provides evidence in favor of the proposed model over standard BVARs. Furthermore, out-of-sample forecasting exercises demonstrate that our model significantly enhances predictive accuracy, particularly for highly persistent variables and longer-horizon forecasts. These results remain robust across models of different sizes, including small, medium, and large.

Summary Impulse responses and forecasts are central concepts for policymakers. They are also sufficient statistics to solve many important macroeconomic problems, from policy counterfactuals to policy evaluation, and offer a promising alternative to the standard structural modelling approach. In this work, we discuss and extend recent progress on the use of these sufficient macro statistics for policy evaluation. We illustrate the methods by evaluating the performance of the European Central Bank over 1999–2023.

Summary We consider forecast comparison in the presence of instability when this affects only a short period of time. We demonstrate that global tests do not perform well in this case because they were not designed to capture very short-lived instabilities, and their power vanishes altogether when the magnitude of the shock is very large. We then discuss non-parametric approaches that are more suitable to detect such situations. We illustrate these results in a Monte Carlo exercise and in a comparison of the nowcast of the quarterly US nominal GDP from the Survey of Professional Forecasters against a naive benchmark of no growth, over a period that includes the GDP instability brought by the COVID-19 crisis. We recommend that forecasters do not pool the sample, but exclude the short periods of high local instability from the evaluation exercise.