Two-stage non Gaussian QML estimation of GARCH models and testing the efficiency of the Gaussian QMLE

In generalized autoregressive conditional heteroskedastic (GARCH) models, the standard identifiability assumption that the variance of the iid process is equal to 1 can be replaced by an alternative moment assumption. We show that, for estimating the original specification based on the standard identifiability assumption, efficiency gains can be expected from using a quasi-maximum likelihood (QML) estimator based on a non Gaussian density and a reparameterization based on an alternative identifiability assumption. A test allowing to determine whether a reparameterization is needed, that is, whether the more efficient QMLE is obtained with a non Gaussian density, is proposed.     

Inference with dependent data using cluster covariance estimators

This paper presents an inference approach for dependent data in time series, spatial, and panel data applications. The method involves constructing t and Wald statistics using a cluster covariance matrix estimator (CCE). We use an approximation that takes the number of clusters/groups as fixed and the number of observations per group to be large. The resulting limiting distributions of the t and Wald statistics are standard t and F distributions where the number of groups plays the role of sample size. Using a small number of groups is analogous to ‘fixed-b’ asymptotics of [Kiefer and Vogelsang, 2002] and [Kiefer and Vogelsang, 2005] (KV) for heteroskedasticity and autocorrelation consistent inference. We provide simulation evidence that demonstrates that the procedure substantially outperforms conventional inference procedures.     

Subsampling the distribution of diverging statistics with applications to finance

In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known or unknown rates when the underlying time series is strictly stationary and strong mixing. Based on our results we provide a detailed discussion of how to estimate extreme order statistics with dependent data and present two applications to assessing financial market risk. Our method performs well in estimating Value at Risk and provides a superior alternative to Hill's estimator in operationalizing Safety First portfolio selection.     

An Algorithm for Balancing Commodity-flow Systems

In many cases there is a need to balance commodity-flow system data quickly and with limited resources. In order to compile new supply and use tables and investment matrices consistent with revised national accounts data, it can be necessary to apply some sort of automatic balancing technique. The paper proposes an algorithm that, contrary to RAS, can balance a commodity-flow system with unequal net row and column sums. The algorithm is applied to a recent revision of Danish capital stock estimates.     

Wages, Experience and Seniority

In this paper we study the sources of wage growth. We identify the contribution to such growth of general, sector specific and firm specific human capital. Our results are interpretable within the context of a model where the returns to human capital may be heterogeneous and where firms may offer different combinations of entry level wages and firm specific human capital development. We allow for the possibility that wages are match specific and that workers move jobs as a result of identifying a better match. To estimate the average returns to experience, sector tenure and firm specific tenure within this context, we develop an identification strategy which relies on the use of firm closures. Our data source is a new and unique administrative data-set for Germany that includes complete work histories as well as individual characteristics. We find positive returns to experience and firm tenure for skilled workers. The returns to experience for unskilled workers are small and insignificant after 2 years of experience. Their returns to sector tenure are also zero. However, their returns to firm tenure are substantial.     

An empirical investigation of the sustainability of the public deficit in Portugal

In this paper, we investigate Portuguese government expenditures and revenues as an example for a long time series. Our hypothesis states that there may be periods when the deficit is sustainable and those when it is not. Usually, after a period of unsustainable deficits, a new regime takes over. These regime shifts call for an approach that takes into account a non-constant structure of the underlying data generating process. Consequently, we use different tests which we set up in a time-varying framework. We apply and compare the results of the Trace test, Breitung’s non-parametric test and the Bohn test. We identify several break points and find that the Trace test performs worst in this case while Breitung’s test and the Bohn test give similar results. Comparing the results with history, we find that the last two tests best reflect what happened historically.     

R&D in a strategic delegation game revisited: a note

In this note we reconsider the paper of Zhang and Zhang (1997), published in Managerial and Decision Economics, who analyze a strategic delegation model with R&D spillovers in an imperfectly competitive market. We were motivated to study their setup by a puzzling result given in their paper: delegating the production and R&D decisions to managers is never beneficial for the owners of the firm. When we tried to understand the driving forces of this result, we found however that the findings of Zhang and Zhang (1997) are incorrect. We explain why their derivations are wrong and demonstrate via counterexamples that the main propositions in their paper do not hold. In addition, we show how the correct solution of this R&D model with spillovers can be obtained. Copyright © 2006 John Wiley & Sons, Ltd.