forecast data from the SPF, CBO, Federal Reserve, and asset prices. Unanticipated fiscal stimulus and interest rate shocks we … the ability of fiscal and monetary policy to moderate output and inflation. We find that coordinated fiscal and monetary …

structural and empirical models in a consistent framework using Bayesian and other estimation techniques. We find that money … money in forecasting euro-area inflation. We compare the predictive performance within and among various classes of … contains relevant information for inflation in some model classes. Money-based New Keynesian DSGE models and VARs incorporating …

Intro -- Contents -- I. INTRODUCTION -- II. INFLATION AND MONETARY POLICY IN THE KYRGYZ REPUBLIC -- III. SEASONAL … CHARACTERISTICS OF CONSUMER PRICES -- IV. MODELING AND FORECASTING INFLATION -- V. CONCLUDING REMARKS -- References -- Appendix …

We examine whether changes in the distribution of household inflation expectations contain information on future … inflation. We first discuss recent shifts in micro data from the US, UK, Germany, and Canada. We then zoom in on the US to … explore econometrically whether distributional characteristics help predict future inflation. We find that the shape of the …

This paper analyzes the inflation forecast errors over the period 2021Q1-2022Q3 using forecasts of core and headline … information. Focusing on core inflation forecast errors in 2021, four factors provide a potential ex post explanation: a stronger … different governments in 2020 correlates positively with core inflation forecast errors in advanced economies. This result hints …

machine learning (ML) models to forecast near-term core inflation in Japan post-pandemic. Japan is a challenging case, because … systematically outperform the benchmark models, with LASSO providing the most accurate forecast. Useful predictors of inflation post …Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and …

Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper … we extend the BMA framework to panel data models where the lagged dependent variable as well as endogenous variables … appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using …

method can reproduce the smoothness of professional forecasts subject to various constraints and slightly improve forecast …

This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the …