Economic Risk Seminar

Nonparametric Identification of Endogenous and Heterogeneous Discrete Choice Models

Speaker(s): 
Fabian Dunker (Ruhr-Universität Bochum)
Date: 
Monday, April 27, 2015 - 2:00pm
Location: 
Spandauer Straße 1, Room 23

Separable unobserved heterogeneity in duration models: testing and generalisations

Speaker(s): 
Petyo Bonev (MINES ParisTech)
Date: 
Monday, April 20, 2015 - 2:00pm
Location: 
Spandauer Straße 1, Room 23

Separabilty of unobserved heterogeneity is a common way to achieve identification in hazard models. In the first part of this paper, we develop two different frameworks for testing the multiplicative unobserved heterogeneity assumption. In the first framework, we assume that we observe multiple duration variables that are caused by a shared unobserved characteristics component (shared frailty). This assumption comes originally from the peer effects literature. The test statistics is based on a ratio of partial derivatives of the joint survival function.

Local Adaptive Multiplicative Error Models for High-Frequency Forecasts

Speaker(s): 
Andrija Mihoci (Humboldt-Universität zu Berlin)
Date: 
Monday, April 13, 2015 - 2:00pm
Location: 
Spandauer Straße 1, Room 23

We propose a local adaptive multiplicative error model (MEM) accommodating time-varying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time. Analysing 1-minute cumulative trading volumes of five large NASDAQ stocks in 2008, we show that local windows of approximately 3 to 4 hours are reasonable to capture parameter variations while balancing modelling bias and estimation (in)efficiency.

Bounded-influence robust estimation of copulas

Speaker(s): 
Samuel Orso (University of Geneva)
Date: 
Monday, February 2, 2015 - 2:00pm
Location: 
Spandauer Straße 1, Room 23

Copula functions are very convenient for modeling multivariate observations. Popular estimation methods are the maximum likelihood and a pseudo likelihood. Unfortunately, the resulting estimators can often be biased whenever relatively small model deviations occur at the marginal and copula levels. In this paper, we propose two robust estimators that do not share this undesirable feature.

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Speaker(s): 
Alexander Kempf (Köln)
Date: 
Monday, January 19, 2015 - 2:00pm
Location: 
Spandauer Straße 1, Room 23

Testing the lag structure of assets' realized volatility dynamics

Speaker(s): 
Francesco Audrino (Uni St. Gallen)
Date: 
Monday, January 12, 2015 - 2:00pm
Location: 
Spandauer Straße 1, Room 23

A (conservative) test is constructed to investigate the optimal lag structure for forecasting realized volatility dynamics. The testing procedure relies on the recent theoretical results that show the ability of the adaptive least absolute shrinkage and selection operator (adaptive lasso) to combine efficient parameter estimation, variable selection, and valid inference for time series processes.

Econometrics of a Global investment Time Series

Speaker(s): 
Frank Tan (SMU Singapore)
Date: 
Monday, January 5, 2015 - 2:00pm
Location: 
Spandauer Straße 1, Room 23

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Speaker(s): 
Erik Theissen (Mannheim)
Date: 
Monday, December 15, 2014 - 2:00pm
Location: 
Spandauer Straße 1, Room 23

Some Extensions of Regression Based Cointegration Analysis

Speaker(s): 
Martin Wagner (TU Dortmund)
Date: 
Monday, November 24, 2014 - 2:00pm
Location: 
Spandauer Straße 1, Room 23

The analysis of cointegrating relationships in a regression framework is typically carried out using modified least squares estimators that employ corrections for the effects of endogeneity and error serial correlation to obtain limiting distributions that allow for asymptotic standard inference. Several such estimation procedures are available in the literature. We discuss extensions of such approaches along two dimensions.

Testing Missing at Random using Instrumental Variables

Speaker(s): 
Christoph Breunig
Date: 
Monday, November 17, 2014 - 2:00pm
Location: 
Spandauer Straße 1, Room 23

This paper proposes a test for missing at random (MAR). The MAR assumption is shown to be testable given instrumental variables which are independent of response given potential outcomes. Given such instruments, MAR is shown to be equivalent to an identified conditional moment restriction. A nonparametric testing procedure is proposed which replaces the conditional moment by series estimators and is based on integrated squared distance. For this test statistic, the asymptotic distribution under the MAR hypothesis is derived.

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