Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

By using asymptotic expansion techniques approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Earlier results on bias approximation in first-order stable dynamic panel data models are extended to higher-order dynamic models with general disturbance covariance structure. The focus is on estimation of both short- and long-run coefficients. The results show that proper modelling of the disturbance covariance structure is indispensable. The bias approximations are used to construct bias corrected estimators which are then applied to quarterly data from 14 European Union countries. Money demand functions for M1, M2 and M3 are estimated for the EU area as a whole for the period 1991:I-1995:IV. The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, bias correction can be substantial underlining the importance of more refined estimation techniques. Also the efficiency gains by exploiting the heteroscedasticity and cross-correlation patterns between countries are considerable.

The weak instrument problem of the system GMM estimator in dynamic panel data models

The system GMM estimator for dynamic panel data models combines moment conditions for the model in first differences with moment conditions for the model in levels. It has been shown to improve on the GMM estimator in the first differenced model in terms of bias and root mean squared error. However, we show in this paper that in the covariance stationary panel data AR(1) model the expected values of the concentration parameters in the differenced and levels equations for the crosssection at time t are the same when the variances of the individual heterogeneity and idiosyncratic errors are the same. This indicates a weak instrument problem also for the equation in levels. We show that the 2SLS biases relative to that of the OLS biases are then similar for the equations in differences and levels, as are the size distortions of the Wald tests. These results are shown in a Monte Carlo study to extend to the panel data system GMM estimator.Dynamic panel data, system GMM, weak instruments

A comparison of bias approximations for the 2SLS estimator

We consider the bias of the 2SLS estimator in the linear instrumental variables regression with one endogenous regressor only. By using asymptotic expansion techniques we approximate 2SLS coefficient estimation bias under various scenarios regarding the number and strength of instruments. The resulting approximation encompasses existing bias approximations, which are valid in particular cases only. Simulations show that the developed approximation gives an accurate description of the 2SLS bias in case of either weak or many instruments or both.

The impact of customer-specific marketing expenses on customer retention and customer profitability

We study the effects of customer-specific marketing expenses on customer retention and customer profitability in a business-to-business setting. Using data from a company providing hygiene services, we look at the impact of a hitherto unstudied type of expense targeted at individual customer relationships: the offering of free equipment to customers. The data allow tracking the activities performed in more than 4,500 customer relationships over a period of 4 years. Retention rates are higher for customers targeted with free equipment, but this effect results from an interaction with customer size. First-order dynamic panel data analyses show that the impact of targeted marketing expenses on customer dollar profit is positive for large customers, but there is no effect for smaller customers. Thus, targeted marketing expenses seem to be a tool for relationship maintenance rather than customer development: they help in retaining large customers that generate more profit, but they do not seem to work in developing new customers into larger, more profitable ones