Search results

This paper proposes a new class of estimators for the autoregressive coefficient of a dynamic panel data model with random individual effects and nonstationary initial condition. The new estimators we introduce are weighted averages of the well-known first difference (FD) GMM/IV estimator and the pooled ordinary least squares (POLS) estimator. The proposed procedure seeks to exploit the differing strengths of the FD GMM/IV estimator relative to the pooled OLS estimator. In particular, the latter is inconsistent in the stationary case but is consistent and asymptotically normal with a faster rate of convergence than the former when the underlying panel autoregressive process has a unit root. By averaging the two estimators in an appropriate way, we are able to construct a class of estimators which are consistent and asymptotically standard normal, when suitably standardized, in both the stationary and the unit root case. The results of our simulation study also show that our proposed estimator has favorable finite sample properties when compared to a number of existing estimators.

This paper investigates how relative environmental regulation influences the flow of foreign direct investment (FDI), and thereby assesses the pollution haven hypothesis (PHH). In this field, conflicting results exist, partly due to the mere consideration of absolute environmental regulation or the inadequate consideration of endogeneity. Concerning these, we study China’s inward FDI from 26 developed countries and 12 developing countries over 1996–2009, and collect four different environmental regulation indicators at relative values of CO₂, SO₂, PM10, and an environmental regulation index. Using an instrumental variable approach, we find strong PHH evidence no matter for the subsample of FDI from developed countries or the one from developing countries. Moreover, we show how such results can be masked if failing to consider the endogeneity.

This paper is concerned with estimation and inference for difference-in-difference regressions with errors that exhibit high serial dependence, including near unit roots, unit roots, and linear trends. We propose a couple of solutions based on a parametric formulation of the error covariance. First stage estimates of autoregressive structures are obtained by using the Han, Phillips, and Sul (2011, 2013) X-differencing transformation. The X-differencing method is simple to implement and is unbiased in large N settings. Compared to similar parametric methods, the approach is computationally simple and requires fewer restrictions on the permissible parameter space of the error process. Simulations suggest that our methods perform well in the finite sample across a wide range of panel dimensions and dependence structures.

Purpose – To estimate the impact of diabetic drug adherence on hospitalizations, emergency room (ER) visits, and hospital costs.

Methods – It is often difficult to measure the impact of drug adherence on hospitalizations since both adherence and hospitalizations may be correlated with unobservable patient severity. We control for such unobservables using propensity score methods and instrumental variables for adherence such as drug coinsurance levels and direct-to-consumer advertising.

Findings – We find a significant bias due to unobservable severity in that patients with more severe health are more apt to comply with medications. Thus, the relationship between adherence and hospitalization will be underestimated if one does not control for unobservable severity. Overall, we find that increasing diabetic drug adherence from 50% to 100% reduces the hospitalization rate by 23.3% from 15% to 11.5%. ER visits reduce by 46.2% from 17.3% to 9.3%. Although such an increase in adherence increases diabetic drug spending by $776 a year per diabetic, the cost savings for averted hospitalizations and ER visits are $886 per diabetic, a cost offset of $1.14 per $1.00 spent on diabetic drugs.

Originality – Most of the drug cost-offset literature focuses only on the impact of cost-sharing and drug spending on cost-offsets, making it impossible to back-out the empirical impact of actual drug adherence on cost-offsets. In this chapter, we estimate the direct impact of adherence on hospitalizations and costs.

Principal component (PC) techniques are commonly used to improve the small sample properties of the linear instrumental variables (IV) estimator. Carrasco (2012) argue that PC type methods provide a natural ranking of instruments with which to reduce the size of the instrument set. This chapter shows how reducing the size of the instrument based on PC methods can lead to poor small sample properties of IV estimators. A new approach to ordering instruments termed ‘normalized principal components’ (NPCs) is introduced to overcome this problem. A simulation study shows the favourable small samples properties of IV estimators using NPC, methods to reduce the size of the instrument relative to PC. Using NPC we provide evidence that the IV setup in Angrist and Krueger (1992) may not suffer the weak instrument problem.