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The potential mutation of the Sub-Prime banking crisis into a sovereign debt one in Euro area countries is investigated. After reviewing the criteria used to measure the debt vulnerability, the balance sheet approach (BSA) is presented in order to illustrate the potential connections between these two types of crises. A graphical analysis yields evidence that at the end 2009 the probability of observing a Euro area country defaulting is less likely than six month before. Nevertheless, the serious threats, which concern Greece and Ireland, do not permit us to exclude the occurrence of a contagious, or self-fulfilling, sovereign debt or currency crises in Euro area in the future.

Cover; Half-title; Title; Copyright; Contents; Contributors; Acknowledgments; Introduction; 1 Time series analysis and simultaneous equation econometric models (1974); 2 Statistical analysis of econometric models (1979); 3 Structural econometric modeling and time series analysis: an integrated approach (1983); 4 Time series analysis, forecasting, and econometric modeling: the structural econometric modeling, time series analysis (SEMTSA) approach (1994); 5 Large-sample estimation and testing procedures for dynamic equation systems (1980).

For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA hereafter), we show that the presence of common cyclical features or cointegration leads to a reduction of the order of the implied univariate autoregressive-integrated-moving average (ARIMA hereafter) models. This finding can explain why we identify parsimonious univariate ARIMA models in applied research although VAR models of typical order and dimension used in macroeconometrics imply non-parsimonious univariate ARIMA representations.
Next, we develop a strategy for studying interactions between variables prior to possibly modelling them in a multivariate setting. Indeed, the similarity of the autoregressive roots will be informative about the presence of co-movements in a set of multiple time series. Our results justify both the use of a panel setup with homogeneous autoregression and heterogeneous cross-correlated vector moving average errors and a factor structure, and the use of cross-sectional aggregates of ARIMA series to estimate the homogeneous autoregression.

This paper introduces dynamics in the R&D to innovation and innovation to productivity relationships, which have mostly been estimated on cross-sectional data. It considers four nonlinear dynamic simultaneous equations models that include individual effects and idiosyncratic errors correlated across equations and that differ in the way innovation enters the conditional mean of labor productivity: through an observed binary indicator, an observed intensity variable or through the continuous latent variables that correspond to the observed occurrence or intensity. It estimates these models by full information maximum likelihood using two unbalanced panels of Dutch and French manufacturing firms from three waves of the Community Innovation Survey. The results provide evidence of robust unidirectional causality from innovation to productivity and of stronger persistence in productivity than in innovation.

In this paper we examine the importance of financial and other obstacles to innovation in the Netherlands using statistical information from the CIS 3.5 innovation survey. We report results on the effect of these obstacles on the firms' decision to abandon, prematurely stop, seriously slow down, or not to start an innovative project. These results are compared with those from other studies in the Netherlands and other countries. We end with a discussion of policy measures that have been taken to overcome, or at least attenuate these obstacles, such as R&D tax incentives, venture capital financing and policy mix packages.

The paper studies the degree of homogeneity of innovative behavior in order to determine empirically an industry classification of Dutch manufacturing that can be used for policy purposes. We use a twolimit tobit model with sample selection, which explains the decisions by business enterprises to innovate and the impact these decisions have on the share of innovative sales. The model is estimated for eleven industries based on the Dutch Standard Industrial Classification (SBI 1993). A likelihood ratio test (LR) is then performed to test for equality of the parameters across industries. We find that Dutch manufacturing consists of three groups of industries in terms of innovative behavior, a hightech group, a low-tech group and the industry of wood, where firms seem to have a rather different innovative behavior from the remaining industries. The same pattern shows up in the three Dutch Community Innovation Surveys.