Dongho Song

Department of Economics
Boston College
140 Commonwealth Avenue
Chestnut Hill, MA 02467

E-Mail: EmailAddress: hidden: you can email any NBER-related person as first underscore last at nber dot org

NBER Working Papers and Publications

July 2014Identifying Long-Run Risks: A Bayesian Mixed-Frequency Approach
with Frank Schorfheide, Amir Yaron: w20303
We develop a nonlinear state-space model that captures the joint dynamics of consumption, dividend growth, and asset returns. Our model consists of an economy containing a common predictable component for consumption and dividend growth and multiple stochastic volatility processes. The estimation is based on annual consumption data from 1929 to 1959, monthly consumption data after 1959, and monthly asset return data throughout. We maximize the span of the sample to recover the predictable component and use high-frequency data, whenever available, to efficiently identify the volatility processes. Our Bayesian estimation provides strong evidence for a small predictable component in consumption growth (even if asset return data are omitted from the estimation). Three independent volatility pr...
December 2013Real-Time Forecasting with a Mixed-Frequency VAR
with Frank Schorfheide: w19712
This paper develops a vector autoregression (VAR) for time series which are observed at mixed frequencies - quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time data set, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly-frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time.

Published: Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, vol 33(3), pages 366-380. citation courtesy of

April 2013Improving GDP Measurement: A Measurement-Error Perspective
with S. Boraǧan Aruoba, Francis X. Diebold, Jeremy Nalewaik, Frank Schorfheide: w18954
We provide a new and superior measure of U.S. GDP, obtained by applying optimal signal-extraction techniques to the (noisy) expenditure-side and income-side estimates. Its properties - particularly as regards serial correlation - differ markedly from those of the standard expenditure-side measure and lead to substantially-revised views regarding the properties of GDP.

Published: Aruoba, S. Borağan & Diebold, Francis X. & Nalewaik, Jeremy & Schorfheide, Frank & Song, Dongho, 2016. "Improving GDP measurement: A measurement-error perspective," Journal of Econometrics, Elsevier, vol. 191(2), pages 384-397. citation courtesy of

September 2011Improving GDP Measurement: A Forecast Combination Perspective
with S. Boragan Aruoba, Francis X. Diebold, Jeremy Nalewaik, Frank Schorfheide: w17421
Two often-divergent U.S. GDP estimates are available, a widely-used expenditure side version, GDPE, and a much less widely-used income-side version GDPI . We propose and explore a "forecast combination" approach to combining them. We then put the theory to work, producing a superior combined estimate of GDP growth for the U.S., GDPC. We compare GDPC to GDPE and GDPI , with particular attention to behavior over the business cycle. We discuss several variations and extensions.

Published: "Improving GDP Measurement: A Forecast Combination Perspective," in X. Chen and N. Swanson (eds.), Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis: Essays in Honor of Halbert L. White Jr., Springer, 1- 26, 2012. With B. Aruoba, J. Nalewaik, F. Schorfheide and D. Song,

NBER Videos

National Bureau of Economic Research, 1050 Massachusetts Ave., Cambridge, MA 02138; 617-868-3900; email:

Contact Us