Fall 2009

Venue H10-31, time: 15:30h

Sept. 3 

Jan Groen (Federal Reserve Bank of New York) 

  Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting


We compare a number of data-rich methods that are widely used in macroeconomic forecasting with a lesser known alternative: partial least squares (PLS) regression. In this method, linear, orthogonal combinations of a large number of predictor variables are con- structed such that the covariance between a target variable and these common components are maximized. We show theoretically that when the data have a factor structure, PLS re- gression can be seen as an alternative way to approximate this unobserved factor structure.

We also prove that when a large data set has a weak factor structure, which possibly vanishes in the limit, PLS regression still provides asymptotically the best fit for the target variable of interest. Monte Carlo experiments confirm our theoretical results that PLS regression is at least as good as principal components regression and close to Bayesian regression when the data has a factor structure. When the factor structure in the data is weak, PLS regression always outperforms principal components and, in most cases, Bayesian regressions. Finally, we apply PLS, principal components, and Bayesian regressions on a large panel of monthly U.S. macroeconomic data to forecast key variables across different subperiods, and PLS re- gression usually has the best out-of-sample performance.

Sept. 17

Aviv Nevo (Northwestern University)


A Simple Model of Demand Anticipation

In the presence of intertemporal substitution, static demand estimation yields biased estimates and fails to recover long run price responses. The goal of this paper is to present a computationally simple way to estimate dynamic demand using aggregate data. Previous work on demand dynamics has been computationally intensive and data demanding. We estimate our model using store level data on soft drinks and find a disparity between static and long run estimates of price responses. Alternatives solutions offered in the literature perform poorly. The simplicity of the proposed model of storage allows us to start exploring the supply side.


Sept. 30

James Hamilton (University of California, San Diego)


The Propagation of Regional Recessions

This paper develops a framework for inferring common Markov-switching components in a panel data set with large cross-section and time-series dimensions. We apply the framework to studying similarities and differences across U.S. states in the timing of business cycles. We hypothesize that there exists a small number of cluster designations, with individual states in a given cluster sharing certain business cycle characteristics. We find that although oil-producing and agricultural states can sometimes experience a separate recession from the rest of the United States, for the most part, differences across states appear to be a matter of timing, with some states entering recession or recovering before others.

Link: http://dss.ucsd.edu/~jhamilto/Owyang.pdf

 Oct. 15

Dagfinn Rime (Norges Bank)


Asymmetric Information in the Interbank Foreign Exchange Market

This paper provides evidence of private information in the interdealer foreign exchange market. In so doing it provides support for the hypothesis that information is an important reason for the strong positive correlation between order flow and returns. It also provides evidence that information influences order-book structure. Our data comprise the complete record of interdealer trades at a good-sized Scandinavian bank during four weeks in 1998 and 1999, including bank identities. Our results indicate that larger banks have more information than smaller banks, that the relation between order flow and returns is stronger for larger banks than smaller banks, and that larger banks exploit their information advantage in limit-order placement.

Also see: http://www.norges-bank.no/templates/article____73190.aspx


 Oct. 22

Gunter Maris (University of Amsterdam)


Parameter identifiability, model equivalence and the interpretation of parameters in an educational measurement context

In the context of educational measurement, statistical models are developed that relate the ability of a student to the probability with which a correct response to particular questions is given. Such models are refered to as Item Response Theory models. We consider an illustrative example where
different researchers analyze the same data with different instances of the same measurement model. One researcher reaches the conclusion that students guess, whereas the other one concludes that students do not guess. Both models are shown to be statistically equivalent, and the example shows precisely where and how the beliefs of researchers have an effect on their respective substantive conclusions. We consider this problem in relation to the use of statistical models for scientific research.

Also see:  http://www.informaworld.com/smpp/title~db=all~content=t775653679, Volume 7, issue 2, with commentaries

Nov. 5

Matteo Ciccarelli (European Central Bank)


Trusting the Bankers: A New Look at the Credit Channel of Monetary Policy


Nov. 12

Ilias Tsiakas (Warwick Business School)



Spot and Forward Volatility in Foreign Exchange

This paper investigates the empirical relation between spot and forward
implied volatility in foreign exchange. We formulate and test the
forward volatility unbiasedness hypothesis, which is the volatility
analogue to the extensively researched hypothesis of unbiasedness in
forward exchange rates.  Using a new data set of spot implied volatility
quoted on over-the-counter currency options, we compute the forward
implied volatility that corresponds to the forward contract on future
spot implied volatility known as a forward volatility agreement.  We
find statistically significant evidence that forward implied volatility
is a systematically biased predictor that overestimates future spot
implied volatility.  The bias in forward volatility generates high
economic value to an investor exploiting predictability in the returns
to volatility speculation and indicates the presence of predictable
volatility term premiums in foreign exchange.


Nov. 18

Jesus Crespo (University of Innsbruck)


Spatial Filtering, Model Uncertainty and the Speed of Income Convergence in Europe

In this paper we put forward a Bayesian Model Averaging method dealing with model uncertainty in the presence of potential spatial autocorrelation. The method uses spatial filtering in order to account for different types of spatial links. We contribute to existing methods that handle spatial dependence among observations by explicitly taking care of uncertainty stemming from the choice of a particular spatial structure. Our method is applied to estimate the conditional speed of income convergence across 255 NUTS-2 European regions for the period 1995-2005. We show that the choice of a spatial weight matrix - and in particular the choice of a class thereof - can have an important effect on the estimates of the parameters attached to the model covariates. We also show that estimates of the speed of income convergence across European regions depend strongly on the form of the spatial patterns which are assumed to underly the dataset. When we take into account this dimension of model uncertainty, the posterior distribution of the speed of convergence parameter appears bimodal, with a large probability mass around no convergence (0% speed of convergence) and a rate of convergence of 1%, approximately half of the value which is usually reported in the literature.

Venue H09-02, time: 15:00h.
Coordinator: Lorenzo Pozzi (pozzi@remove-this.ese.eur.nl)

Paper: http://econpapers.repec.org/paper/innwpaper/2009-17.htm

Nov. 26

Nikolaus Hautsch (Humboldt-Universität zu Berlin)


A Blocking and Regularization Approach to High Dimensional Realized Covariance Estimation

We introduce a two-step blocking and regularization approach for the estimation of high-dimensional covariances using high-frequency data. In a first step, we group assets according to their observation frequencies and then estimate the covariance matrix in a block-wise manner using realised kenels with group-specific time scales. In a second step, the covariance matrix is regularized using random matrix theory. The resulting "RnB" estimator is positive-definite, well-conditioned and makes more efficient use of the data than the standard multi-variate realized kernel estimator of Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a). The performance of the new estimator is analyzed in an extensive simulation study mimicking the liquidity and market microstructure features of the S&P 1500 universe. The regularization and blocking procedure yields significant efficiency gains for varying liquidity settings, noise-to-signal ratios, and dimensions. An empirical application to the forecasting of daily covariances of the S&P 500 index confirms the simulation results.

Dec. 3

Paul de Boeck (University of Amsterdam)


ARIP: An additive random item parameter model for binary and ordered-category data, with random induction and random sensitivity parameters


Based on the ARIP model, binary and ordered-category data on behaviors (as items) can be approached with mixed logistic models, containing random parameters for the persons and for the behaviors: a propensity parameter of the person, an induction parameter for how much the behavior is induced, and a sensitivity parameter for how sensitive the behavior is to the underlying propensity of the person. The induction and sensitivity parameters are each defined as a linear function of behavioral features and a random error term. Because the behaviors as well as the persons are treated as random effects, the ARIP model is a crossed random-effect model. Such models are difficult to estimate because integrals are involved which are not only intractable, but also high-dimensional. Two solutions are developed, a Bayesian approach and an alternating imputation posterior approach with adaptive quadrature (Cho & Rabe-Hesketh, 2009). The application to verbal aggression data shows, among other findings, that the inductive power is lower for doing than for wanting and that wanting has a higher degree of discrimination, and thus reflects better than doing the underlying propensity.

Venue: 15:30, H10-31

Dec. 8

Asani Sarkar (Federal Reserve Bank of New York)


Liquidity Spillovers and Cross-Autocorrelations


We examine changes in betas for stocks included in the S&P500 index. In contrast to the prior literature, we find no change in the market beta following inclusions, after controlling for the Fama-French SMB and HML factors. Further, we find a significant reduction in the loadings of stock returns on the SMB and HML factors after index additions. The cross-sectional evidence indicates that reductions in factor loadings are significantly determined by increases in earnings per share and market  capitalization around index additions. These results hold both at the daily and weekly data frequency. We conclude that changes in comovements around S&P 500 additions are consistent with improved firm fundamentals at the time.

Venue: 12:00h, T03-43

Dec. 17

Anita Vlam (Erasmus University Rotterdam)


Financial Innumeracy: Consumers Cannot Deal with Interest Rates


Consumers often have to make decisions involving computations with interest rates. It is well known that computations with percentages and thus with interest rates amount to a difficult task. We survey a large group of consumers, and we find that questions on interest rates are answered correctly in about 20\% of the cases, which in our setting amounts to a random choice. Additional to the available literature, we also document that consumers are too optimistic in the sense that they believe loans are paid off sooner than is true, which provides empirical evidence of self-serving bias. The results are robust to corrections for general numeracy.

Venue: 12:00h, H10-31


Andreas Alfons
Room: H11-21
Phone: 010-408288
Email: alfons@remove-this.ese.eur.nl


Wendun Wang
Room: H11-26,
Phone: 010-4088756
Email: wang@ese.eur.nl

For more information:

Anneke Kop
Room: H11-04
Phone: 010-4081259
Email: eb-secr@remove-this.ese.eur.nl


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