Minchul Shin

Research Department
Federal Reserve Bank of Philadelphia
Ten Independence Mall
Philadelphia, PA 19106

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Institutional Affiliation: Federal Reserve Bank of Philadelphia

NBER Working Papers and Publications

August 2018Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives
with Francis X. Diebold: w24967
Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality ("partially-egalitarian LASSO"). Ex-post analysis reveals that the optimal solution has a very simple form: The vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures motivated by...

Published: Francis X. Diebold & Minchul Shin, 2018. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, . citation courtesy of

September 2016Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility
with Francis X. Diebold, Frank Schorfheide: w22615
Recent work has analyzed the forecasting performance of standard dynamic stochastic general equilibrium (DSGE) models, but little attention has been given to DSGE models that incorporate nonlinearities in exogenous driving processes. Against that background, we explore whether incorporating stochastic volatility improves DSGE forecasts (point, interval, and density). We examine real-time forecast accuracy for key macroeconomic variables including output growth, inflation, and the policy rate. We find that incorporating stochastic volatility in DSGE models of macroeconomic fundamentals markedly improves their density forecasts, just as incorporating stochastic volatility in models of financial asset returns improves their density forecasts.

Published: Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," Journal of Econometrics, vol 201(2), pages 322-332. citation courtesy of

August 2016Assessing Point Forecast Accuracy by Stochastic Error Distance
with Francis X. Diebold: w22516
We propose point forecast accuracy measures based directly on distance of the forecast-error c.d.f. from the unit step function at 0 ("stochastic error distance," or SED). We provide a precise characterization of the relationship between SED and standard predictive loss functions, and we show that all such loss functions can be written as weighted SED's. The leading case is absolute-error loss. Among other things, this suggests shifting attention away from conditional-mean forecasts and toward conditional-median forecasts.

Published: Francis X. Diebold & Minchul Shin, 2017. "Assessing point forecast accuracy by stochastic error distance," Econometric Reviews, vol 36(6-9), pages 588-598. citation courtesy of

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