Misspecified heteroskedasticity in the panel probit model: A small sample comparison of GMM and SML estimators

This paper compares generalized method of moments (GMM) and simulated maximum likelihood (SML) approaches to the estimation of the panel probit model. Both techniques circumvent multiple integration of joint density functions without the need to restrict the error term variance- covariance matrix of the latent normal regression model. Particular attention is paid to a three-stage GMM estimator based on nonparametric estimation of the optimal instruments for given conditional moment functions. Monte Carlo experiments are carried out which focus on the small sample consequences of misspecification of the error term variance-covariance matrix. The correctly specified experiment reveals the asymptotic efficiency advantages of SML. The GMM estimators outperform SML in the presence of misspecification in terms of multiplicative heteroskedasticity. This holds in particular for the three-stage GMM estimator. Allowing for heteroskedasticity over time increases the robustness with respect to misspecification in terms of ultiplicative heteroskedasticity. An application to the product innovation activities of German manufacturing firms is presented.

Inverse Probability Weighted Generalised Empirical Likelihood Estimators: Firm Size and R&D Revisited

The inverse probability weighted Generalised Empirical Likelihood (IPW-GEL) estimator is proposed for the estimation of the parameters of a vector of possibly non-linear unconditional moment functions in the presence of conditionally independent sample selection or attrition.The estimator is applied to the estimation of the firm size elasticity of product and process R&D expenditures using a panel of German manufacturing firms, which is affected by attrition and selection into R&D activities.IPW-GEL and IPW-GMM estimators are compared in this application as well as identification assumptions based on independent and conditionally independent sample selection.The results are similar in all specifications.research and development;generalised emperical likelihood;inverse probability weighting;propensity score;conditional independence;missing at random;selection;attrition

Horizontal and Vertical R&D Cooperation

This paper introduces a second, vertically related industry into the usual one-industry oligopoly framework of cooperative R&D investment between firms operating on the same product market. R&D efforts are affected by intra- and inter-industry R&D spillovers. Horizontal and vertical R&D cooperation scenarios are compared to R&D competition. It turns out that vertical R&D cooperation is usually the only stable equilibrium in the sense that no firm has an incentive to chose any other R&D scenario. Empirical implications concerning the relationship between R&D intensities and R&D spillovers are derived and empirical evi-dence is given using data of German manufacturing firms.

Accounting for Nonresponse Heterogeneity in Panel Data

The paper proposes a technique for the estimation of possibly nonlinear panel data models in the presence of heterogeneous unit nonresponse. Attrition or unit nonresponse in panel data usually renders parameter estimators inconsistent unless the unavailable information is missing completely at random. For moment based estimators this problem can be expressed in terms of the impossibility to construct the sample equivalents of the population moments of interest. However, if the attrition process is conditionally mean independent of the variables of interest then the sample equivalents of the population moments can be recovered by weighting the moment functions with the conditional response probability (or propensity score). The latter is usually unknown and has to be estimated. In the presence of nonresponse heterogeneity the propensity score can be estimated by conventional parametric estimation methods like the multinomial logit or probit model. The technique proposed in this paper leads to a moment estimator which simultaneously exploits the weighted moment functions of interest and the score function of the multinomial choice model. The use of simulated moments is discussed for applications with many nonresponse reasons. An applications of the estimator to firm level data is presented where the variables of interest are R&D investments related to product and process innovations.

Finite Sample Properties of One-step, Two-step and Bootstrap Empirical Likelihood Approaches to Efficient GMM Estimation

This paper compares conventional GMM estimators to empirical likelihood based GMM estimators which employ a semiparametric efficient estimate of the unknown distribution function of the data. One-step, two-step and bootstrap empirical likelihood and conventional GMM estimators are considered which are efficient for a given set of moment conditions. The estimators are subject to a Monte Carlo investigation using a specification which exploits sequential conditional moment restrictions for binary panel data with multipli-cative latent effects. Among other findings the experiments show that the one-step and two-step estimators yield coverage rates of confidence intervals below their nominal coverage probabilities. The bootstrap methods improve upon this result.

Circumventing multiple integration: A comparison of GMM and SML estimators for the panel probit model

The paper compares two approaches to the estimation of panel probit models: the Generalized Method of Moments (GMM) and the Simulated Maximum Likelihood (SML) technique. Both have in common that they circumvent multiple integrations of joint density functions without the need to impose restrictive variance-covariance specifications on the error term distribution. Particular attention is paid to a three-stage GMM estimator based on nonparametric estimation of optimal instruments. A Monte Carlo study reveals slight efficiency gains from SML when the underlying model is correctly specified. GMM turns out to be more robust than SML when heteroskedasticity over time is ignored as well as in the presence of multiplicative heteroskedasticity. An application to the product innovation activities of German manufacturing firms is presented

Inverse Probability Weighted Generalised Empirical Likelihood Estimators:Firm Size and R&D Revisited

The inverse probability weighted Generalised Empirical Likelihood (IPW-GEL) estimator is proposed for the estimation of the parameters of a vector of possibly non-linear unconditional moment functions in the presence of conditionally independent sample selection or attrition.The estimator is applied to the estimation of the firm size elasticity of product and process R&D expenditures using a panel of German manufacturing firms, which is affected by attrition and selection into R&D activities.IPW-GEL and IPW-GMM estimators are compared in this application as well as identification assumptions based on independent and conditionally independent sample selection.The results are similar in all specifications.

Tree-based decompositions of graphs on surfaces and applications to the traveling salesman problem

The tree-width and branch-width of a graph are two well-studied examples of parameters that measure how well a given graph can be decomposed into a tree structure. In this thesis we give several results and applications concerning these concepts, in particular if the graph is embedded on a surface. In the first part of this thesis we develop a geometric description of tangles in graphs embedded on a fixed surface (tangles are the obstructions for low branch-width), generalizing a result of Robertson and Seymour. We use this result to establish a relationship between the branch-width of an embedded graph and the carving-width of an associated graph, generalizing a result for the plane of Seymour and Thomas. We also discuss how these results relate to the polynomial-time algorithm to determine the branch-width of planar graphs of Seymour and Thomas, and explain why their method does not generalize to surfaces other than the sphere. We also prove a result concerning the class C_2k of minor-minimal graphs of branch-width 2k in the plane, for an integer k at least 2. We show that applying a certain construction to a class of graphs in the projective plane yields a subclass of C_2k, but also show that not all members of C_2k arise in this way if k is at least 3. The last part of the thesis is concerned with applications of graphs of bounded tree-width to the Traveling Salesman Problem (TSP). We first show how one can solve the separation problem for comb inequalities (with an arbitrary number of teeth) in linear time if the tree-width is bounded. In the second part, we modify an algorithm of Letchford et al. using tree-decompositions to obtain a practical method for separating a different class of TSP inequalities, called simple DP constraints, and study their effectiveness for solving TSP instances.Ph.D.Committee Chair: Thomas, Robin; Committee Co-Chair: Cook, William J.; Committee Member: Dvorak, Zdenek; Committee Member: Parker, Robert G.; Committee Member: Yu, Xingxin

How Deep is the Annuity Market Participation Puzzle?

Using UK microeconomic data, we analyze the empirical determinants of voluntary annuity market demand. We find that annuity market participation increases with financial wealth, life expectancy and education and decreases with other pension income and a possible bequest motive for surviving spouses. We then show that these empirically-motivated determinants of annuity market participation have the same, quantitatively important, effects in a life-cycle model of annuity and life insurance demand, saving and portfolio choice. Moreover, reasonable preference parameters predict annuity demand levels comparable to the data. For stockholders, a relatively strong bequest motive is sufficient to simultaneously generate balanced portfolios and low annuity demand.Annuities, portfolio choice, life insurance, bequest motive