DOES GNP EXAGGERATE GROWTH IN “ACTUAL” OUTPUT? THE CASE OF THE UNITED STATES

Measures of national product can be misleading because there is nonmarket production. There are also distortions due to transactional activities, which are expenditures to support transactions, not actual output consumed. For 1950–89, this study recalculates output for the United States, adjusting for transactional activities and nonmarket production. Due to relatively rapid growth in transactional activities, GNP overstates output growth in the 1950s; because there was slow expansion of transactional activities in the early 1970s, GNP understates actual output. Since 1974, increases in transactional activities and shifts to market production lead GNP to exaggerate improvement of "actual" output per capita.     

Consistency of elementwise-weighted total least squares estimator in a multivariate errors-in-variables model <Emphasis Type="Bold">AX=B</Emphasis>

A multivariate measurement error model AXB is considered. The errors in [A,B] are rowwise independent, but within each row the errors may be correlated. Some of the columns are observed without errors, and in addition the error covariance matrices may differ from row to row. The total covariance structure of the errors is supposed to be known up to a scalar factor. The fully weighted total least squares estimator of X is studied, which in the case of normal errors coincides with the maximum likelihood estimator. We give mild conditions for weak and strong consistency of the estimator, when the number of rows in A increases. The results generalize the conditions of Gallo given for a univariate homoscedastic model (where B is a vector), and extend the conditions of Gleser given for the multivariate homoscedastic model. We derive the objective function for the estimator and propose an iteratively reweighted numerical procedure.Acknowledgements.A. Kukush is supported by a postdoctoral research fellowship of the Belgian office for Scientific, Technical and Cultural Affairs, promoting Scientific and Technical Collaboration with Central and Eastern Europe. S. Van Huffel is a full professor with the Katholieke Universiteit Leuven. This paper presents research results of the Belgian Programme on Interuniversity Poles of Attraction (IUAP Phase V-22), initiated by the Belgian State, Prime Ministers Office-Federal Office for Scientific, Technical and Cultural Affairs, of the Concerted Research Action (GOA) projects of the Flemish Government MEFISTO-666 (Mathematical Engineering for Information and Communication Systems Technology), of the IDO/99/03 project (K.U. Leuven) Predictive computer models for medical classification problems using patient data and expert knowledge, of the FWO projects G.0200.00, G.0078.01 and G.0270.02. The scientific responsibility is assumed by its authors. The authors would like to thank Maria Luisa Rastello and Amedeo Premoli for bringing the EW-TLS problem to their attention. The authors are grateful to two anonymous referees for the valuable comments.     

Attribute differences between U.S. GAAP and IFRS earnings: An exploratory study

In this study we explore attribute differences between U.S. GAAP and IFRS earnings. Our study is motivated by the ongoing harmonization process in accounting standard setting as well as by recent convergence projects by the FASB and the IASB. We test two market-based earnings attributes, i.e., value relevance and timeliness, as well as two accounting-based earnings attributes, i.e., predictability and accrual quality. These attributes are tested for German New Market firms as they are allowed to choose between IFRS and U.S. GAAP for financial reporting purposes. Overall, we find that U.S. GAAP and IFRS only differ with regard to predictive ability. The fact that U.S. GAAP accounting information outperforms IFRS also holds after controlling for differences in firm characteristics, such as size, leverage and the audit firm. However, our results also seem to suggest that these differences are not fully valued by investors, as we do not observe significant and consistent differences for the value-relevance attribute.