In this paper, we estimate a health care demand function for 18 OECD countries for the period 1972-1995. We consider a demand side approach where health expenditure depend on per capita GDP and the relative price of health care. We use panel data unit root and stationarity tests to characterize our data. Then, we test cointegration between our variables with Kao[16] panel data cointegration tes...

Asset prices observed in financial markets combine equilibrium prices and market microstructure noise. In this paper, we study how to tell apart large shifts in equilibrium prices from noise using high frequency data. We propose a new nonparametric test which allows us to asymptotically remove the noise from observable price data and to discover jumps in fundamental asset values. We provide its...

This chapter gives an account of the recent literature on estimating models for panel count data. Specifically, the treatment of unobserved individual heterogeneity that is correlated with the explanatory variables and the presence of explanatory variables that are not strictly exogenous are central. Moment conditions are discussed for these type of problems that enable estimation of the parame...

This paper derives explicit expressions for the asymptotic variances of the maximum likelihood and continuously updated GMM estimators under potentially misspecified models. The proposed misspecification-robust variance estimators allow the researcher to conduct valid inference on the model parameters even when the model is rejected by the data. Although the results for the maximum likelihood e...

Article history: This paper tests for beta-convergence and sigma-convergence in the corporate governancemodels, using a samAccepted 12 July 2012 ple of corporate governance ratings for 198 European corporations listed on the FTSE Eurofirst 300 index. A piecewise linear regression is deployed to select amodel and the Poisson pseudo-maximum likelihood estimator JEL classification: is also applied...

The outcome of popular unit root tests depends heavily on the initial condition, i.e. on the difference between the initial observation and the deterministic component. In some applications it is difficult to rule out small or large values of the initial condition a priori, so this dependence can be quite difficult to deal with in practice. We explore a number of methods for constructing unit r...

Following Lancaster (2002), we propose a strategy to solve the incidental parameter problem. The method is demonstrated under a simple panel Poisson count model. We also extend the strategy to accomodate cases when information orthogonality is unavailable, such as the linear AR(p) panel model. For the AR(p) model, there exists a correction function to fix the incidental parameter problem when t...

We suggest a non-parametric heteroscedasticity and autocorrelation consistent (HAC) estimator of the variance–covariance (VC) matrix for a vector of sample moments within a spatial context. We demonstrate consistency under a set of assumptions that should be satisfied by a wide class of spatial models. We allow for more than one measure of distance, each of which may be measured with error. Mon...

The conventional Sargan (1958) / Hansen (1982) test of overidentifying restrictions and the Tilting Parameter test of Imbens, Spady and Johnson (1998) are compared in the context of the AR(1) dynamic panel data model using Monte Carlo experiments. Interestingly, the size properties of the former are found to be superior in this setting. Nevertheless, the Sargan / Hansen test is found to have no...

We develop a test for the linear no cointegration null hypothesis in a threshold vector error correction model. We adopt a sup-Wald type test and derive its null asymptotic distribution. A residual-based bootstrap is proposed, and the first-order consistency of the bootstrap is established. A set of Monte Carlo simulations shows that the bootstrap corrects size distortion of asymptotic distribu...