Determinants of the Effective Real Exchange Rate of the Synthetic Euro: Alternative Methodological Approaches

On the basis of historical data aggregated over the period 1973 to 2000, we have experimented with four different approaches to estimate the synthetic euro's equilibrium exchange rate. Using a number of competing models with the same data set, variable definitions and sample period offers the possibility to assess the uncertainty surrounding such equilibrium levels, both from an empirical (different estimates) and a theoretical viewpoint (different specifications). In this exercise, the 'Rest of the World' is proxied by the US, the UK, Japan and Switzerland, aggregated on the basis of trade weights.
We employed reduced form co–integration models, a structural VAR, a NATREX model (estimated in structural form) and the ECB's small–sized euro area wide macro–econometric model. In this order the approaches feature an increasing degree of 'structure', in the sense of the constraints based on economic theory embedded in the econometric models that were estimated. The results confirm the high likelihood for the euro having been undervalued in Q4 2000, while stressing the significant empirical and theoretical uncertainty with respect to the equilibrium exchange rate level.     

Inferential Causal Models: Integrating Quality & Quantity

In-depth data analysis plus statistical modeling can produce inferentialcausal models. Their creation thus combines aspects of analysis by close inspection,that is, reason analysis and cross-tabular analysis, with statistical analysis procedures,especially those that are special cases of the generalized linear model (McCullaghand Nelder, 1989; Agresti, 1996; Lindsey, 1997). This paper explores some of the roots of this combined method and suggests some new directions. An exercise clarifies some limitations of classic reason analysis by showing how the cross tabulation of variables with controls for test factors may produce better inferences. Then, given the cross tabulation of several variables, by explicating Coleman effect parameters, logistic regressions, and Poisson log-linear models, it shows how generalized linear models provide appropriate measures of effects and tests of statistical significance. Finally, to address a weakness of reason analysis, a case-control design is proposed and an example is developed.     

Time-Based Management of the New Product Development Process

This study explored the problem of compressing new product development by focusing on the specific phases of the innovation process. These phases manifest significant qualitative differences that require attention for understanding the complexities of accelerating new product development. Based on data from 35 high-technology companies, Necmi Karagozoglu and Warren Brown identified several different acceleration methods. Results revealed unexpected and at times inconsistent insights than those reflected in the case study and anecdotally based literature, and implied also that some of the well documented approaches to successful new product development need to be replaced with their time-based versions.     

AN EMPIRICAL INVESTIGATION INTO THE IMPACT OF PROFIT SHARING SCHEMES OF EXECUTIVIES ON THE CONTENT OF CORPORATE SUBMISSIONS ON PROPOSED ACCOUNTING STANDARDS

This paper examines the comments submitted by UK companies on 20 proposed accounting standards to test the hypotheses that executives favor standards that increase, or dampen the variance of, accounting profit numbers on which their incentive remuneration is based. Test results were generally as hypothesised but only the profit variance outcomes were statistically significant. Allowing for political environment changes affected only the profit variance results. There was no evidence that the relative monetary size of bonus payments was a significant lobbying factor. No significant differences were found between the lobbying preferences of companies with or without executive incentive schemes.     

Do Mothers Matter? A Comparison of Models of the Influence of Mothers' and Fathers' Educational and Occupational Status on Children's Educational Attainment

The first objective of this article is to clarify which model best captures the structure and trend of the influence of social origin on children's education. The second objective is to analyse how general conclusions on historical trends in educational reproduction change if we add mother's status background to the model. Six contrasting hypotheses are derived fromthe body of literature dealing with models on families' socioeconomic status. All hypotheses are translated into empirical models and their explained variance is compared. A pooled data set is used that contains data from the Netherlands, West Germany, and the USA. The Modified Dominance Model, that distinguishes the influence of the highest from thelowest status parent, has the best model fit. Regarding the second objective we see that adding the mother's influence to that of the father's does not change general conclusions on trends in educational reproduction. Over time the influence of both parents decreases continuously. However, the influence of the mother's education and occupational status on children's educational attainment is substantive.     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.