Estimation and inference under economic restrictions

Estimation of economic relationships often requires imposition of constraints such as positivity or monotonicity on each observation. Methods to impose such constraints, however, vary depending upon the estimation technique employed. We describe a general methodology to impose (observation-specific) constraints for the class of linear regression estimators using a method known as constraint weighted bootstrapping. While this method has received attention in the nonparametric regression literature, we show how it can be applied for both parametric and nonparametric estimators. A benefit of this method is that imposing numerous constraints simultaneously can be performed seamlessly. We apply this method to Norwegian dairy farm data to estimate both unconstrained and constrained parametric and nonparametric models

Heterogeneity and Strategic Choices: The Case of Stock Repurchases

Strategic decisions are fundamentally tough choices. Theory suggests that managers are likely to display bounded rationality. Empirics on the other hand assume rationality in choice behavior. Recognizing this inherent disconnect between theory and empirics, we try to account for behavioral biases using a theoretically consistent choice model. The traditional approach to modeling strategic choice has been to use discrete choice models and make inference on the conditional mean effects. We argue that the conditional mean effect does not capture behavioral biases. The focus should be on the conditional variance. Explicitly modeling the conditional variance (in the discrete choice framework) provides us with valuable information on individual level variation in decision-making. We demonstrate the effect of ignoring the role of variance in choice modeling in the context of firm’s decisions to conduct open market repurchases. We show that when taking into account the heterogeneity in choices, manager’s choices of conducting open market repurchases displays considerable heterogeneity and that not accounting for such heterogeneity might lead to wrong conclusions on the mean effects

A Flexible Approach to Parametric Inference in Nonlinear Time Series Models

Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible parametric model that accommodates virtually any of these specifications—and does so in a simple way that allows for straightforward Bayesian inference. The basic idea underlying our model is that it adds two concepts to a standard state space framework. These ideas are ordering and distance. By ordering the data in different ways, we can accommodate a wide range of nonlinear time series models. By allowing the state equation variances to depend on the distance between observations, the parameters can evolve in a wide variety of ways, allowing for models that exhibit abrupt change as well as those that permit a gradual evolution of parameters. We show how our model will (approximately) nest almost every popular model in the regime-switching and structural break literatures. Bayesian econometric methods for inference in this model are developed. Because we stay within a state space framework, these methods are relatively straightforward and draw on the existing literature. We use artificial data to show the advantages of our approach and then provide two empirical illustrations involving the modeling of real GDP growth

On the Restrictiveness of Separability: The Significance of Energy in German Manufacturing

Any researcher would certainly agree with Hamermesh?s (1993:34) intuition about separability that the ease of substitution between any two production factors should be unaffected by a third factor that is separable from the others. This paper emphasizes that such a notion of separability needs to be more restrictive than the classical separability concept is.We thus coin the notion of strict separability that implies the classical concept. By applying both separability concepts in a translog approach to German manufacturing data (1978?1990), we focus on the empirical question of whether the omission of energy affects the conclusions about the ease of substitution among nonenergy factors. We find ample empirical evidence to doubt the assumption that energy is separable from all other production factors even in the relatively mild form of classical separability. At least under separability aspects, therefore, energy appears to be an indispensable production factor

The role of employees for post-entry firm growth

While the majority of existing studies on the determinants of post-entry firm growth focus on the role of the founders or on the impact of firm-specific characteristics like size, age or industry affiliation, a possible impact of the characteristics of a start-up's workforce on post-entry growth has been widely neglected in the literature so far. Based upon a comprehensive panel dataset of establishments in Germany, this paper contributes to fill this gap and examines the role of the initial employment structure with respect to qualification, age, gender and nationality for post-entry employment growth measured both in terms of employees and in terms of fulltime equivalents. Moreover, it is analyzed whether the use of flexible work forms like regular part-time and / or marginal employment in the year of foundation affects post-entry growth. Our empirical results confirm that in particular the initial qualification structure of a start-up's employees matters for post-entry growth. Establishments using flexible work forms show higher post-entry growth with respect to total hours worked, but a significantly lower growth with respect to the number of employees