[gov3009-l] Applied Stats this Wednesday: Herman van Dijk

Wise, Tess wise at fas.harvard.edu
Mon Mar 24 14:58:26 EDT 2014

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 Wise
PhD Candidate
Harvard Department of Government

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