Allan Würtz

Testing a Parametric Function Against a Non‐Parametric Alternative in IV and GMM Settings

This paper develops a specification test for functional form for models identified by moment restrictions, including IV and GMM settings. The general framework is one where the moment restrictions are specified as functions of data, a finite-dimensional parameter vector, and a nonparametric real function (an infinite-dimensional parameter vector). The null hypothesis is that the real function is parametric. The test is relatively easy to implement and its asymptotic distribution is known. The test performs well in simulation experiments.

Self-Employment among Immigrants: A Last Resort?

Based on unique register data of male immigrants in Denmark, we investigate whether self-employment is used as a last resort. To identify self-employment as a last resort, we define different types of immigrants as a function of transition probabilities between wage-employment, non-employment and self-employment. The transition probabilities are estimated using discrete competing risks models controlling for unobserved heterogeneity and duration dependence. We find that for certain groups of immigrants a large fraction can be characterized as using self-employment as a last resort.

Conceptualizing and Measuring Transformational and Transactional Leadership

Existing conceptualizations and measures of transformational and transactional leadership have unclear theoretical bases, confound leadership and its effects, and are not necessarily suitable for public organizations. Overcoming these problems is necessary to test how leadership affects performance. Many public administration scholars apply the concepts, emphasizing the need to ensure that the concepts are applicable in both public and private organizations. The article reconceptualizes transformational and transactional leadership and develops and tests revised measures that can be employed on employees and leaders, are robust in terms of repeated use by the same respondents, and are applicable to public and private organizations alike.

On Determining the Importance of a Regressor with Small and Undersized Samples

A problem encountered in, for instance, growth empirics is that the number of explanatory variables is large compared to the number of observations. This makes it infeasible to condition on all variables in order to determine the importance of a variable of interest. We prove identifying assumptions under which the problem is not ill-posed. Under these assumptions, we derive properties of the most commonly used methods: Extreme bounds analysis, Sala-i-Martin’s method, BACE, generalto- specific, minimum t-statistics, BIC and AIC. We propose a new method and show that it has good finite sample properties.

Power of tests in binary response models

Most hypotheses in binary response models are composite. The null hypothesis is usually that one or more slope coefficients are zero. Typically, the sequence of alternatives of interest is one in which the slope coefficients are increasing in absolute value. In this papar, we prove that the power goes to zero for this sequence of alternatives of interest in cases which often occur in practice. (This abstract was borrowed from another version of this item.)

Testing the Semiparametric Box-cox Model with the Bootstrap

This paper considers tests of the transformation parameter of the Box-Cox model when the distribution of the error is unknown. Monte Carlo experiments are carried out to investigate the rejection probabilities of the GMM-based Wald and Lagrange Multiplier (LM) tests when the null hypothesis is true. The results show that the differences between empirical and nominal levels can be large when asymptotic critical values are used. In most cases, the bootstrap reduces the differences between the empirical and nominal levels, and, in many cases, essentially removes the distortions in levels that occur with asymptotic critical values. Experiments are also carried out to investigate the ability of the bootstrap to provide improved finite-sample critical values with Wald tests based on the semiparametric estimation procedure recently developed by Foster, Tian and Wei (2001).

Efficient Estimation of Non-Linear Dynamic Panel Data Models with Application to Smooth Transition Models

This paper explores estimation of a class of non-linear dynamic panel data models with additive unobserved individual-specific effects. The models are specified by moment restrictions. The class includes the panel data AR(p) model and panel smooth transition models. We derive an efficient set of moment restrictions for estimation and apply the results to estimation of panel smooth transition models with fixed effects, where the transition may be determined endogenously. The performance of the GMM estimator, both in terms of estimation precision and forecasting performance, is examined in a Monte Carlo experiment. We find that estimation of the parameters in the transition function can be problematic but that there may be significant benefits in terms of forecast performance.