This paper examines the use of count data-based outcome variables such as corporate patents in empirical corporate finance research. We demonstrate that the common practice of regressing the log of one plus the count on covariates ("LOG1PLUS" regression) produces biased and inconsistent...

We investigate the misappropriation of R&D subsidies and evaluate its consequences for policy effectiveness. Using Chinese firm-level data for 2001-2011, we identify that 42% of grantees misused R&D subsidies, accounting for 53% of total R&D subsidies. Misappropriation leads to a substantial...

At the macroeconomic level, the persistence of technological change allows sustainable growth. But do the innovations come from the same set of firms or from a continuous renewal of innovators? On this point, the assumptions underlying the endogenous growth models differ and innovation...

In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension...

I study inverse probability weighted M-estimation under a general missing data scheme. The cases covered that do not previously appear in the literature include M-estimation with missing data due to a censored survival time, propensity score estimation of the average treatment effect for linear...

Die Anonymisierung von sensiblen Individualdaten führt zu einem Konflikt zwischen dem Ziel der Minimierung des Reidentifikationsrisikos und der Qualität ökonometrischer Schätzungen. Der durch Anonymisierung bedingte Verlust an Effizienz und/oder der Konsistenz eines Schätzers wirft die...

This paper presents a generalized moments (GM) approach to estimating an R-th order spatial regressive process in a panel data error component model. We derive moment conditions to estimate the parameters of the higher order spatial regressive process and the optimal weighting matrix required to...

This paper generalizes the approach to estimating a first-order spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) in a cross-section with heteroskedastic innovations by Kelejian and Prucha (2008) to the case of spatial autoregressive models with spatial...

One important goal of this study is to develop a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first...

In this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step...