Behavioral and Experimental Economics

Summer School Experimental Economics

The Behavioral and Experimental Economics group has an influential position in this field in the Netherlands and Europe. CREED, the Amsterdam-based group, focuses particularly on three main projects: economics of political decision making; bounded rationality and institutions and experimental economics. The research of the Rotterdam-based group focuses on two broad themes: decision under risk and uncertainty and intertemporal choice.

Cooperative Behavior, Strategic Interaction and Complex Systems

This research group focuses on: (non-)cooperative game theory; nonlinear dynamics and complex systems; bounded rationality, learning and heterogenous expectations; dynamic models of collective behavior and social networks & dynamic optimization.

Econometrics and Operations Research

Research themes: time series econometrics, panel data, Bayesian econometrics, applied econometrics and econometric methodology. Applications can be found in areas as diverse as monetary economics, labor economics, marketing and asset pricing. Some fellows in this group focus on operations research.


The Finance group at TI spans many of the core fields in finance: asset pricing, corporate finance, financial econometrics, market microstructure, and financial institutions.

Labor, Health, Education and Development

At TI, a large group of fellows works in different areas of labour, health, education and development.

Macroeconomics and International Economics

Fellows in the Macroeconomics and International Economics group carry out research on growth, innovation, international trade and factor mobility, the role of economic geography, banking and monetary economics, and fiscal policy.

Organizations and Markets

The Organizations and Markets (OM) group spans many areas in (applied) microeconomics, including the economics of organizations, industrial organization, entrepreneurship, innovation, and auctions.

Spatial, Transport and Environmental Economics

The STEE group addresses four themes: urban and regional dynamics, land use, transportation, and environment and resources. Many fellows combine policy research with fundamental research.

Journal of Econometrics, 2013, 177(2), 213-32.

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.

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