Jacqueline Pradel

THE COMMERCIAL SUCCESS OF INNOVATIONS : AN ECONOMETRIC ANALYSIS AT THE FIRM LEVEL IN FRENCH MANUFACTURING

This paper offers some empirical evidence on how do product and process innovations affect manufacturing sales and exports. Accounting for differences in technological opportunities between industries, and the “market pull” or “technology push” nature of firms’ innovations, we explain the share of new products in total sales and exports by the innovation types firms have implemented. The data come from the French Ministry of Industry’s Innovation survey 1986–1990. The left-hand variables are the sales revenues and the export revenues of the firms that are attributable to products introduced within the last 5 years. Given that the survey reports only interval data, our estimates were obtained by maximum likelihood on the ordered probit model. The following results are obtained: (i) The contribution of products less than 5 years old is lower to overall exports than to total sales but the innovative content is stronger in exports than in domestic sales; (ii) The greater the underlying technological opportunities, the less successful are product imitations; (iii) Both domestic sales and exports are mostly made of product improvements; (iv) Firm size has a positive effect on innovation output only when the technological opportunity is strong.

Direct test of the rational expectation hypothesis

In this paper we examine the possibility of testing the rational expectation hypothesis from simultaneous observations of some variables y and of their expectation y∗. The test procedures are related to the sort of data available, generally obtained from tendency surveys. We distinguish between qualitative and quantitative data, we define in each case the notion of rational expectation. Then it is possible to characterize the rational expectation hypothesis and to develop adapted test procedures.

Some theoretical results for generalized ridge regression estimators

Abstract We examine some interpretations and theoretical properties of the ridge regression estimators. As such we (i) interpret the GRR estimator as an OLS one based on transformed explanatory variables; (ii) compare the GRR and OLS estimators using the confidence regions; (iii) prove the optimality of the OLS estimator for estimating the signs of the parameters;and (iv) compare the necessary and sufficient condition for the transformed explanatory variables to be less collinear than the initial ones with the condition for improving the OLS estimator.