Hello Everyone!
I hope you all had a lovely spring break! Our speaker at Applied Stats this week will be
Herman van Dijk, a Bayesian econometrician and visiting fellow at IQSS, who will giving a
presentation entitled "Forecasting with Many Models in Finance and Economics using
Large Data Sets and Parallel Computing."
As per usual, we will meet in CGIS K354 at 12 noon on Wednesday and lunch will be served.
Here is the abstract for the talk:
We propose a Bayesian combination approach for multivariate predictive densities which
relies upon a distributional state space representation of the combination weights.
Several specifications of multivariate time-varying weights are introduced with a
particular focus on weight dynamics driven by the past performance of the predictive
densities and the use of learning mechanisms. In the proposed approach the model set can
be incomplete, meaning that all models can be individually misspecified. A Sequential
Monte Carlo method is proposed to approximate the filtering and predictive densities. The
combination approach is assessed using statistical and utility-based performance measures
for evaluating density forecasts of simulated data, US macroeconomic time series and
surveys of stock market prices. Simulation results indicate that, for a set of linear
autoregressive models, the combination strategy is successful in selecting, with
probability close to one, the true model when the model set is complete and it is able to
detect parameter instability when the model set includes the true model that has generated
subsamples of data. Also, substantial uncertainty appears in the weights when predictors
are similar; residual uncertainty reduces when the model set is complete; and learning
reduces this uncertainty. For the macro series we find that incompleteness of the models
is relatively large in the 1970’s, the beginning of the 1980’s and during the recent
financial crisis, and lower during the Great Moderation; the predicted probabilities of
recession accurately compare with the NBER business cycle dating; model weights have
substantial uncertainty attached. With respect to returns of the S&P 500 series, we
find that an investment strategy using a combination of predictions from professional
forecasters and from a white noise model puts more weight on the white noise model in the
beginning of the 1990’s and switches to giving more weight to the professional forecasts
over time. Information on the complete predictive distribution and not just on some
moments turns out to be very important, above all during turbulent times such as the
recent financial crisis. More generally, the proposed distributional state space
representation offers great flexibility in combining densities.
The corresponding paper is attached. In addition there is another paper that provides some
background for those who are interested.
See you all on Wednesday!
Tess
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Tess Wise
PhD Candidate
Harvard Department of Government
http://tesswise.com