On Bayesian and non-Bayesian estimation of a two-level CES production function for the Dutch manufacturing sector

In this paper maximum-likelihood estimates of the parameters of the two-level CES function, obtained by direct estimation of this function, are given. In addition the authors propose to show how a Bayesian analysis may help to find a solution to the difficulties related with, but not specific to, this particular estimation problem. It is shown that numerical integration of the posterior distribution may give an indication as to which parameter has to be pinpointed and at which value when multi-collinearity precludes unconditional maximization of the likelihood. It is suspected that this approach has a wider field of application

Persistence of Innovation in Dutch Manufacturing: Is it Spurious?

This paper studies the persistence of innovation and the dynamics of innovation output in Dutch manufacturing using firm data from three waves of the Community Innovation Surveys (CIS), pertaining to the periods 1994-1996, 1996-1998, and 1998-2000. We estimate by maximum likelihood a dynamic panel data type 2 tobit model accounting for individual effects and handling the initial conditions problem. We find that there is no evidence of true persistence in achieving technological product or process innovations, while past shares of innovative sales condition, albeit to a small extent, current shares of innovative sales.Economics ;

Innovative Sales, R&D and Total Innovation Expenditures: Panel Evidence on their Dynamics

This paper studies the dynamic relationship between input and output of innovation in Dutch manufacturing using an unbalanced panel of enterprise data from five waves of the Community Innovation Survey during 1994-2004. We estimate by maximum likelihood a dynamic panel data bivariate tobit with double-index sample selection accounting for individual effects. We find persistence of innovation input and innovation output, a lag effect of the former on the latter and a feedback effect of the latter on the former. The lag effect remains significant in the high-tech sector even after four years. Firm and industry effects are also important.innovation, panel bivariate tobit model, innovation expenditures

Persistence of Innovation in Dutch Manufacturing: Is it Spurious?

This paper studies the persistence of innovation and the dynamics of innovation output in Dutch manufacturing using firm data from three waves of the Community Innovation Surveys (CIS), pertaining to the periods 1994-1996, 1996-1998, and 1998-2000. We estimate by maximum likelihood a dynamic panel data type 2 tobit model accounting for individual effects and handling the initial conditions problem. We find that there is no evidence of true persistence in achieving technological product or process innovations, while past shares of innovative sales condition, albeit to a small extent, current shares of innovative sales. Cette étude analyse la persistance et la dynamique de l’innovation dans les entreprises manufacturières néerlandaises à partir des données de trois vagues d’enquêtes communautaires sur l’innovation (ECI), portant sur les périodes 1994-1996, 1996-1998 et 1998-2000. Nous estimons par la méthode du maximum de vraisemblance un modèle tobit de type II dynamique sur données de panel avec effets individuels et traitement explicite des conditions initiales. Nous concluons qu’il n’y a pas de véritable persistance dans le fait d’innover en produits ou en procédés, mais que les observations passées des parts du chiffre d’affaires en produits innovants influencent, quoique faiblement, les données contemporaines de ces parts.dynamic panel data type 2 tobit, innovation, spurious persistence, modèle tobit de type II dynamique, données de panel, persistence, innovation

An Empirically-Based Taxonomy of Dutch Manufacturing: Innovation Policy Implications

The paper studies the degree of homogeneity of innovative behavior inorder to determine empirically an industry classi¯cation of Dutch manu-facturing that can be used for policy purposes. We use a two-limit tobitmodel with sample selection, which explains the decisions by business en-terprises to innovate and the impact these decisions have on the share ofinnovative sales. The model is estimated for eleven industries based on theDutch Standard Industrial Classi¯cation (SBI 1993). A likelihood ratiotest (LR) is then performed to test for equality of the parameters acrossindustries. We ¯nd that Dutch manufacturing consists of three groups ofindustries in terms of innovative behavior, a high-tech group, a low-techgroup and the industry of wood, where ¯rms seem to have a rather di®er-ent innovative behavior from the remaining industries. The same patternshows up in the three Dutch Community Innovation Surveys.none;

Financial Constraints and Other Obstacles: Are they a Threat to Innovation Activity?

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 pakages.Financial Constraints, Innovation, Innovation Policy

The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models

This paper proposes a method to implement maximum likelihood estimation of the dynamic panel data type 2 and 3 tobit models. The likelihood function involves a two-dimensional indefinite integral evaluated using “two-step” Gauss-Hermite quadrature. A Monte Carlo study shows that the quadrature works well infinite sample for a number of evaluation points as small as two. Incorrectly ignoring the individual effects, or the dependence between the initial conditions and the individual effects results in an overestimation of the coefficients of the lagged dependent variables. An application to incremental and radical product innovations by Dutch business firms illustrates the method. Cette étude propose une façon d’utiliser l’estimateur du maximum de vraisemblance pour des données panel et des modèles dynamiques de type tobit 2 ou tobit 3. La fonction de vraisemblance inclut une intégrale double qui est évaluée en utilisant une quadrature Gauss-Hermite à deux étapes. Une étude de Monte Carlo montre que la quadrature donne de bons résultats dans un échantillon fini même avec uniquement deux points d’évaluation. Si on ignore les effets individuels ou la dépendance entre ceux-ci et les conditions initiales, on obtient une estimation biaisée vers le haut des coefficients des variables endogènes retardées. Une application à l’étude des innovations de produit radicales et incrémentales avec des données panel d’entreprises néerlandaises illustre la méthode proposée.panel data, maximum likelihood estimator, dynamic models, sample selection, données panel, maximum de vraisemblance, modéles dynamiques avec sélection

Financial Constraint and R&D Investment: Evidence from CIS

Using direct information on financial constraints from questionnaires, rather than the commonly used balance sheet information, this paper presents evidence that, controlling for traditional factors as size, market share, cooperative arrangement, and expected profitability, financial constraints affect a firm's decision of how much to invest in R&D activities. Apart from these constraints, other hampering factors as market uncertainty and institutional bottlenecks, regulations and organizational rigidities also affect R&D investment. A semiparametric estimator of sample selection is employed to control for potential endogeneity of the regressors. The paper also shows that old firms and firms that belong to a group are less financially constrained when it comes to undertaking R&D activities. For the estimation a semiparametric binary choice model is used.Research and Development, Investment, Financial Risk

Innovative Sales, R&D and Total Innovation Expenditures: Panel Evidence on their Dynamics

This paper studies the dynamic relationship between input and output of innovation in Dutch manufacturing using an unbalanced panel of enterprise data from five waves of the Community Innovation Survey during 1994-2004. We estimate by maximum likelihood a dynamic panel data bivariate tobit with double-index sample selection accounting for individual effects. We find persistence of innovation input and innovation output, a lag effect of the former on the latter and a feedback effect of the latter on the former. The lag effect remains significant in the high-tech sector even after four years. Firm and industry effects are also important. Dans cette étude nous estimons une fonction de production de l’innovation dynamique sur base des données de panel de cinq vagues d’enquêtes d’innovation communautaires (CIS) aux Pays-Bas couvrant la période 1994 à 2004. Nous estimons par maximum de vraisemblance un modèle tobit bivarié avec une double sélection et prise en compte des effets individuels. Nous trouvons une persistence dans l’innovation tant au niveau de l’intrant que de l’extrant et des effets de retard croisés entre les deux. Les retards perdurent au-delà de 4 ans dans le secteur high-tech. Les effets individuels et les effets particuliers aux industries sont également significatifs.innovation production function, panel data, CIS data, bivariate dynamic tobit, Netherlands, fonction de production de l’innovation, données de panel, tobit bivarié dynamique, Pays-Bas