Search Results
Working Paper
Forecasting in the Absence of Precedent
We survey approaches to macroeconomic forecasting during the COVID-19 pandemic. Due to the unprecedented nature of the episode, there was greater dependence on information outside the econometric model, captured through either adjustments to the model or additional data. The transparency and flexibility of assumptions were especially important for interpreting real-time forecasts and updating forecasts as new data were observed. With data available at the time of writing, we show how various assumptions were violated and how these systematically biased forecasts.
Working Paper
Estimating the Effects of Demographics on Interest Rates: A Robust Bayesian Perspective
There are a vast range of estimates for the effect of demographics on interest rates. I show that these magnitudes are not well-identified without data on capital and life-cycle consumption. However, these data are often omitted. Using nonparametric prior sensitivity analysis for an overlapping generations model estimated through Bayesian methods, I show that without these data, small changes in the prior for the discount rate, intertemporal elasticity of substitution, and capital depreciation rate can shift the posterior quantiles for the effects of demographics by up to 1.5 percentage ...
Briefing
Are We There Yet? The Road Back to 2 Percent Inflation
Trend inflation has remained between 2.5 percent and 3.0 percent since the third quarter of 2023.Housing and food are key sectors for this trend.While services inflation has steadily fallen from its peak, goods inflation has risen since the latter half of 2024.
Briefing
What Does Sectoral Inflation Tell Us About the Aggregate Trend in Inflation?
To know the appropriate stance of monetary policy, policymakers need to determine the overall trend in inflation. This is challenging in the face of varied and evolving patterns in inflation across sectors. We describe a multisector statistical model that provides a systematic approach to appropriately weight incoming inflation data from each sector. By flexibly applying time-varying weights to different sectors, this model adjusts to changing patterns in these sectors over time — including during the pandemic — and suggests that trend inflation is lower than might be suggested by the ...
Briefing
How Do Demographics Influence r*?
Demographic trends are evolving in the U.S. as well as globally, potentially affecting the behavior of interest rates. This includes the natural rate of interest, denoted r*. Through the lens of a simple model, we describe supply and demand channels through which these demographic trends may affect r* and show a range of estimates for the potential quantitative impact.
Working Paper
Global Robust Bayesian Analysis in Large Models
This paper develops a tool for global prior sensitivity analysis in large Bayesian models. Without imposing parametric restrictions, the methodology provides bounds for posterior means or quantiles given any prior close to the original in relative entropy, and reveals features of the prior that are important for the posterior statistics of interest. The author develops a sequential Monte Carlo algorithm and uses approximations to the likelihood and statistic of interest to implement the calculations. Applying the methodology to the error bands for the impulse response of output to a monetary ...
Briefing
Economic Effects Everywhere All at Once
The recent tariffs have brought global trade linkages to the forefront of academic and policy discussions. The global swings in the stock market and sentiment measures have emphasized how U.S. economic policy and conditions have important implications internationally.This global interconnectedness has been present for decades and spurred much academic research even prior to recent developments. Indeed, output and inflation have moved in parallel across countries for many years now. While economists continue to analyze and quantify the sources of this comovement, cross-country linkages are ...
Briefing
COVID-19 over Time and across States: Predictions from a Statistical Model
We discuss a statistical time series model to capture and forecast the dynamics of COVID-19 in the fifty U.S. states and Washington, D.C. We design the model to replicate the typical pattern of infections during a pandemic. We rely on Bayesian methods, which provide a straightforward way to quantify the uncertainty surrounding our estimates and forecasts. In this brief, we focus on North Carolina and Washington, D.C., since they have experienced different trajectories of COVID-19 and may have different implications for the efficacy of our approach.
Working Paper
Averaging Impulse Responses Using Prediction Pools
Macroeconomists construct impulse responses using many competing time series models and different statistical paradigms (Bayesian or frequentist). We adapt optimal linear prediction pools to efficiently combine impulse response estimators for the effects of the same economic shock from this vast class of possible models. We thus alleviate the need to choose one specific model, obtaining weights that are typically positive for more than one model. Three Monte Carlo simulations and two monetary shock empirical applications illustrate how the weights leverage the strengths of each model by (i) ...
Working Paper
How To Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters
This paper estimates a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. The paper's Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point ...