Schedule spring 2016

Venue: H10-31
Time: 16:00

Roel Oomen (Deutsche Bank)

16 February 2016 (Lunch seminar)

Time: 12:00-13:00 
Venue: H09-02

The Practice of FX Spot Trading and Competition amongst Liquidity Providers


No abstract.

Gerdie Everaert (University of Gent)

25 February 2016

Bias-corrected Common Correlated Effects Pooled estimation in homogeneous dynamic panels


This paper extends the Common Correlated Effects Pooled (CCEP) estimator designed by Pesaran (2006) to dynamic homogeneous models. For static panels, this estimator is consistent as the number of cross-sections (N) goes to infinity irrespectively of the time series dimension (T). However, it suffers from a large bias in dynamic models when T is fixed Everaert and De Groote (2016). We develop a bias-corrected CCEP estimator based on an asymptotic bias expression that is valid for a multi-factor error structure provided that a sufficient number of cross-sectional averages, and lags thereof, are added to the model. We show that the resulting CCEPbc estimator is consistent as N tends to infinity, both for T fixed or T growing large, and derive its limiting distribution. Monte Carlo experiments show that our bias correction performs very well. It is nearly unbiased, even when T and/or N are small, and hence offers a strong improvement over the severely biased CCEP estimator. CCEPbc is also found to be  superior to alternative bias correction methods available in the literature in terms of bias, variance and inference.

Roland Fried (TU Dortmund)

31 March 2016

Robust and nonparametric detection of change-points in time series using U-statistics and U-quantiles


Tests for detecting change-points in weakly dependent (more precisely: near epoch dependent) time series are studied. As examples, we will be able to treat most standard models of time series analysis, such as ARMA and GARCH processes.
The presentation will give certain emphasis to the basic problem of testing for an abrupt shift in location, but other questions like a change in variability will also be considered. The popular CUSUM test is not robust to outliers and can be improved in case of non-normal data, particularly for heavy-tails. The CUSUM test can be modified using the Hodges-Lehmann 2-sample estimator, which is the median of all pairwise differences between the samples. It is highly robust and has a high efficiency under normality. Like for a related test based on the 2-sample Wilcoxon statistic, the asymptotics of the Hodges-Lehmann change-point test can be established under general conditions without any moment assumptions. Both tests offer similar power against shifts in the center of the data, but the test based on the Hodges-Lehmann estimator performs superior if a shift occurs far from the center. MOSUM-type tests restrict attention to data in two subsequent moving time windows.  This may overcome possible masking effects due to several shifts into different directions. The talk investigates CUSUM- and MOSUM-type tests based on the 2-sample Wilcoxon statistic or the Hodges-Lehmann estimator by analyzing asymptotical properties and by comparing the performance in finite samples via simulation experiments.

(Joint work with Herold Dehling and Martin Wendler)

Alexei Onatskiy (University of Cambridge)

7 April 2016

Testing no cointegration in large VARs


We study the asymptotic behavior of Johansen's (1988, 1991) likelihood ratio test for no cointegration when the number of observations and the dimensionality of the vector autoregression diverge to infinity simultaneously and proportionally. We find that the empirical distribution of the squared canonical correlations that the test is based on converges to the so-called Wachter distribution. This finding provides a theoretical explanation for the observed tendency of the test to find "spurious cointegration" in the data. It also sheds light on the workings and limitations of the Bartlett correction approach to the over-rejection problem. We propose a simple graphical device, similar to the scree plot, as a quick check of the null hypothesis of no cointegration in high-dimensional VARs.

Emanuel Moench (Deutsche Bundesbank)

14 April 2016

In Search of a Nominal Anchor: What Drives Long-Term Inflation Expectations?


According to both central bankers and economic theory, anchored inflation expectations are key to successful monetary policymaking. Yet, we know very little about the determinants of those expectations. While policymakers may take some comfort in the stability of long-run inflation expectations, the latter is not an inherent feature of the economy. What does it take for expectations to become unanchored? We explore a theory of expectations formation that can produce episodes of unanchoring. Its key feature is state-dependency in the sensitivity of long-run inflation expectations to short-run inflation surprises. Price-setting agents act as econometricians trying to learn about average long-run inflation. They set prices according to their views about future inflation, which hence feed back into actual inflation. When expectations are anchored, agents believe there is a constant long-run inflation rate, which they try to learn about. Hence, their estimates of long-run inflation move slowly, as they keep adding observations to the sample they consider. However, in the spirit of Marcet and Nicolini (2003), a long enough sequence of inflation suprises leads agents to doubt the constancy of long-run inflation, and switch to putting more weight on recent developments. As a result, long-run inflation expectations become unanchored, and start to react more strongly to short-run inflation surprises. Shifts in agents’ views about long-run inflation feed into their price-setting decisions, imparting a drift to actual inflation. Hence, actual inflation can show persistent swings away from its long-run mean. We estimate the model using actual inflation data, and only short-run inflation forecasts from surveys. The estimated model produces long-run forecasts that track survey measures extremely well. The estimated model has several uses: 1) It can tell a story of how inflation expectations got unhinged in the 1970s; it can also be used to construct a counterfactual history of inflation under anchored long-run expectations. 2) At any given point in time, it can be used to compute the probability of inflation or deflation scares. 3) If embedded into an environment with explicit monetary policy, it can also be used to study the role of policy in shaping the expectations formation mechanism.

Co-auteurs: Carlos Carvalho, Stefano Eusepi and Bruce Preston

Peter Rousseeuw (KU Leuven)

12 May 2016

Detecting anomalous data cells


A multivariate dataset consists of n cases in d dimensions, and is often stored in an n by d data matrix.
It is well-known that real data may contain outliers. Depending on the circumstances, outliers may be (a) undesirable errors which can adversely affect the data analysis, or (b) valuable nuggets of unexpected information. In statistics and data analysis the word outlier usually refers to a row of the data matrix, and the methods to detect such outliers only work when at most 50% of the rows are contaminated.
But often only one or a few cell values in a row are outlying, and they may not be found by looking at
each variable (column) separately. We propose the first method to detect cellwise outliers in
the data which takes the correlations between the variables into account. It has no restriction on the number of contaminated rows, and can deal with high dimensions. Other advantages are that it provides estimates of the `expected' values of the outlying cells, while imputing the missing values at the same time.
We illustrate the method on several real data sets, where it uncovers more structure than found by purely columnwise methods or purely rowwise methods.

This is joint work with Wannes Van den Bossche of the KU Leuven.

Isaiah Andrews (Harvard University)

19 May 2016

Conditional Inference with a Functional Nuisance Parameter


This paper shows that the problem of testing hypotheses in moment condition models without any assumptions about identification may be considered as a problem of testing with an infinite-dimensional nuisance parameter. We introduce a sufficient statistic for this nuisance parameter in a Gaussian problem and propose conditional tests. These conditional tests have uniformly correct asymptotic size for a large class of models and test statistics. We apply our approach to construct tests based on quasi-likelihood ratio statistics, which we show are efficient in strongly identified models and perform well relative to existing alternatives in two examples.

Max Welling (University of Amsterdam)

26 May 2016

Deep Learning from Small Data


Deep learning has become the dominant modeling paradigm in machine learning. It has been spectacularly successful in application areas ranging from speech recognition, image analysis, natural language processing, and information retrieval. But a number of important challenges remain un(der)solved, such as data efficient deep learning, energy efficient deep learning and visualizing deep neural networks. In this talk I will address the problem of “data efficient deep learning” through three distinct approaches: 

  1. Combining generative probabilistic (graphical models) with deep learning using variational auto-encoders (w/ D. Kingma), 
  2. Bayesian deep learning using variational approximations based on matrix-normal distributions on random matrices  (w/ C. Louizos)
  3. Exploiting symmetries using Group-equivariant CNNs (w/ T. Cohen) 

Diego Ronchetti (University of Groningen)

2 June 2016

Consistent estimation of optimized functions for the analysis of portfolio strategies


This paper introduces a novel technique for the consistent estimation of models described by restrictions on optimized conditional moments of state and control variables. The method is nonparametric with respect to the dynamics of these variables, and does not require data on the moment optimizer. The technique is illustrated in a financial application: the estimation of portfolio weights and other properties of the unobservable self-financing strategy that best replicates target cash-flows in a Markovian setting. In addition, the paper discusses how the technique can be employed for the estimation of other optimized functions of state and control variables that are of interest in economic applications, such as maximized expected individual intertemporal utilities in microeconomic models.

Christoph Freay (University of Konstanz)

9 August 2016 (Tuesday (10:30)

Posterior Inference for Portfolio Weights


We investigate estimation uncertainty in portfolio weights through their posterior distributions in a Bayesian regression framework. While we derive analytical posterior results for shrinkage variants of the global minimum variance portfolio (GMVP), the main advantage of our novel approach is that we specify the prior directly on the optimal portfolio weights. This avoids estimating the moments of the asset return distribution and substantially reduces the dimensionality of the estimation problem. In a series of empirical experiments we explore the effect of estimation errors on the performance of the optimal portfolio and propose various practical trading strategies derived from the posterior distribution, which are highly beneficial to the investor. We further show how to incorporate economic views about asset returns in our framework as shrinkage targets and how to account for the investor’s uncertainty about these views through a hierarchical set-up.

Milan Pleus (University of Amsterdam)

11 August 2016 (10:30)

Refined exogeneity tests in linear dynamic panel data models


Exogeneity tests are investigated in linear dynamic panel data models, estimated by GMM. Because in that context usually just internal instruments are being exploited, misclassification of explanatory variables renders either a specific subset of instruments invalid or yields inefficient estimates. Rather than testing all overidentifying restrictions by the Sargan-Hansen test, the focus is on subsets using either the incremental Sargan-Hansen test or a Hausman test. Although it is known in the literature that the Sargan-Hansen test suffers when using many instruments, it is  yet unclear in what way the incremental test is affected. Therefore, test statistics are considered in which the number of employed instruments is deliberately restricted. Two possible refinements are proposed. The procedure of Hayakawa (2014), which forces a block diagonal structure on the weighting matrix in order to reduce problems stemming from taking its inverse, is generalized to the incremental test and a finite sample corrected variance estimate for the vector of contrasts is derived from which two new Hausman test statistics are constructed. Simulation is used to investigate finite sample performance. One of corrected Hausman test statistics and a specific implementation of the incremental Sargan-Hansen test, both using only the one-step residuals calculated under the null hypothesis, are found to perform best in terms of size.

Marieke Musegaas (Tilburg University)

18 August 2016 (10:30)

Three-valued simple games and applications to minimum coloring problems


We introduce the model of three-valued simple games as a natural extension of simple games. We analyze the core and the Shapley value of three-valued simple games. Using the concept of vital players as an extension of veto players, the vital core is constructed and we show that the vital core is a subset of the core. The Shapley value is characterized on the class of all three-valued simple games. As an application, we characterize the class of conflict graphs inducing simple or three-valued simple minimum coloring games. We provide an upper bound on the number of maximum cliques of conflict graphs inducing such games. Moreover, it is seen that in case of a perfect conflict graph, the core of an induced three-valued simple minimum coloring game equals the vital core. 


Andreas Alfons
Room: H11-21
Phone: 010-408288


Wendun Wang
Room: H11-26, 
Phone: 010-4088756

For more information

Anneke Kop
Room: H11-04
Phone: 010-4081259

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