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19 Results

Decision Synthesis in Monetary Policy

Staff Working Paper 2024-30 Tony Chernis, Gary Koop, Emily Tallman, Mike West
We use Bayesian predictive decision synthesis to formalize monetary policy decision-making. We develop a case-study of monetary policy decision-making of an inflation-targeting central bank using multiple models in a manner that considers decision goals, expectations and outcomes.

Parallel Tempering for DSGE Estimation

Staff Working Paper 2024-13 Joshua Brault
I develop a population-based Markov chain Monte Carlo algorithm known as parallel tempering to estimate dynamic stochastic general equilibrium models. Parallel tempering approximates the posterior distribution of interest using a family of Markov chains with tempered posteriors.

Predictive Density Combination Using a Tree-Based Synthesis Function

This paper studies non-parametric combinations of density forecasts. We introduce a regression tree-based approach that allows combination weights to vary on the features of the densities, time-trends or economic indicators. In two empirical applications, we show the benefits of this approach in terms of improved forecast accuracy and interpretability.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C3, C32, C5, C53

Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis

Staff Working Paper 2023-45 Tony Chernis
I show how to combine large numbers of forecasts using several approaches within the framework of a Bayesian predictive synthesis. I find techniques that choose and combine a handful of forecasts, known as global-local shrinkage priors, perform best.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C5, C52, C53, E, E3, E37

Global Demand and Supply Sentiment: Evidence from Earnings Calls

Staff Working Paper 2023-37 Temel Taskin, Franz Ulrich Ruch
This paper quantifies global demand, supply and uncertainty shocks and compares two major global recessions: the 2008–09 Great Recession and the COVID-19 pandemic. We use two alternate approaches to decompose economic shocks: text mining techniques on earnings calls transcripts and a structural Bayesian vector autoregression model.

Behavioral Learning Equilibria in New Keynesian Models

Staff Working Paper 2022-42 Cars Hommes, Kostas Mavromatis, Tolga Özden, Mei Zhu
We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing.

(Optimal) Monetary Policy with and without Debt

How should policy be designed at high debt levels, when fiscal authorities have little room to adjust taxes? Assigning the monetary authority a role in achieving debt sustainability makes it less effective in stabilizing inflation and output.

Understanding Trend Inflation Through the Lens of the Goods and Services Sectors

Staff Working Paper 2020-45 Yunjong Eo, Luis Uzeda, Benjamin Wong
The goods and services sectors have experienced considerably different dynamics over the past three decades. Our goal in this paper is to understand how such contrasting behaviors at the sectoral level affect the aggregate level of trend inflation dynamics.

Endogenous Time Variation in Vector Autoregressions

Staff Working Paper 2020-16 Danilo Leiva-Leon, Luis Uzeda
We introduce a new class of time-varying parameter vector autoregressions (TVP-VARs) where the identified structural innovations are allowed to influence — contemporaneously and with a lag — the dynamics of the intercept and autoregressive coefficients in these models.

State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models

Staff Working Paper 2018-14 Luis Uzeda
Implications for signal extraction from specifying unobserved components (UC) models with correlated or orthogonal innovations have been well investigated. In contrast, the forecasting implications of specifying UC models with different state correlation structures are less well understood.
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