Nonlinear Panel Data Models with Expected a Posteriori Values of Correlated Random Effects

We develop a two step estimation procedure to estimate nonlinear panel data models. Our approach combines the “correlated random effect” and the “control function” approach to handel endogeneity of regressors that are correlated with both the unobserved heterogeneity as well as the idiosyncratic component. The novelty here lies in integrating out the unobserved heterogeneity on which the structural equations are conditioned. The integration is performed with respect to the posterior distribution of the individual effects obtained from the first stage reduced form estimation. Our framework suggests separate tests for correlation between unobserved heterogeneity and the covariates, and correlation between idiosyncratic component and the covariates. Average partial effects (APEs) of covariates are also easily obtained.

Productivity implications of R&D, innovation, and capital accumulation for incumbents and entrants: the case of Estonia

In this paper, using Estonian Community Innovation Survey data, we study the role of R&D, capital accumulation, and innovation output on pro- ductivity for entrants and incumbents. We find that the impact of R&D invest- ment on labour productivity is larger for the entrants compared to the incum- bents. Entrants are found to be more productive and more heterogeneous in their total factor productivity (TFP) than the incumbents. Moreover, entrants who innovate are on average, in terms of TFP, 25% more productive than the entrants who do not, while the corresponding figure for the incumbents is 7%. In addition, it is mostly the incumbents who benefit from within-industry knowledge that is produced outside their own firm. Finally, for both entrants and incumbents, em- bodied technological change through capital accumulation is found to be more effective in generating productivity growth than R&D expenditure

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

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