Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series...

Although economists have long been aware of Jensen's inequality, many econometric applications have neglected an important implication of it: the standard practice of interpreting the parameters of log-linearized models estimated by ordinary least squares as elasticities can be highly misleading...

Let r (x, z) be a function that, along with its derivatives, can be consistently estimated nonparametrically. This paper discusses identification and consistent estimation of the unknown functions H, M, G and F, where r (x, z) = H [M (x, z)] and M (x, z) = G(x) + F (z). An estimation algorithm...

This paper investigates whether the geographic distribution of manufacturing activities depends on the size of plants. Using Italian data, we find, as in Kim [Kim, S., 1995. Expansion of markets and the geographic concentration of economic activities: the trends in U.S. regional manufacturing...

In order to value non-market goods, economists estimate individuals’ willingness to pay (WTP) for these goods using revealed or stated preference methods. We compare these conventional approaches with subjective well-being (SWB), which is based on individuals’ ratings of their happiness or...

This paper uses a difference-in-difference style estimation strategy to test separately the impact of competition from public sector and private sector hospitals on the efficiency of public hospitals. Our identification strategy takes advantage of the phased introduction of a recent set of...

We establish valid theoretical and empirical Edgeworth expansions for density-weighted averaged derivative estimates of semiparametric index models.

In a panel data model with fixed effects, possible cross-sectional dependence is investigated in a spatial autoregressive setting. An Edgeworth expansion is developed for the maximum likelihood estimate of the spatial correlation coefficient. The expansion is used to develop more accurate...

We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial aspect can be interpreted quite generally, in either a geographical sense, or employing notions of economic distance, or when parametric modelling arises in part from a common factor or other...

We provide a general class of tests for correlation in time series, spatial, spatiotemporal and cross-sectional data. We motivate our focus by reviewing how computational and theoretical difficulties of point estimation mount as one moves from regularly-spaced time series data, through forms of...