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Working Paper
Forecasting US Inflation in Real Time
We perform a real-time forecasting exercise for US inflation, investigating whether and how additional information--additional macroeconomic variables, expert judgment, or forecast combination--can improve forecast accuracy and robustness. In our analysis we consider the pre-pandemic period including the Global Financial Crisis and the following expansion--the longest on record--featuring unemployment that fell to a rate not seen for nearly sixty years. Distinguishing features of our study include the use of published Federal Reserve Board staff forecasts contained in Tealbooks and a focus on ...
Working Paper
A Robust Method for Microforecasting and Estimation of Random Effects
We propose a method for forecasting individual outcomes and estimating random effects in linear panel data models and value-added models when the panel has a short time dimension. The method is robust, trivial to implement and requires minimal assumptions. The idea is to take a weighted average of time series- and pooled forecasts/estimators, with individual weights that are based on time series information. We show the forecast optimality of individual weights, both in terms of minimax-regret and of mean squared forecast error. We then provide feasible weights that ensure good performance ...