Modelling Spatial Regimes in Farms Technologies

We exploit the information derived from geographical coordinates to endogenously identify spatial regimes in technologies that are the result of a variety of complex, dynamic interactions among site-specific environmental variables and farmer decision making about technology, which are often not observed at the farm level. Controlling for unobserved heterogeneity is a fundamental challenge in empirical research, as failing to do so can produce model misspecification and preclude causal inference. In this article, we adopt a two-step procedure to deal with unobserved spatial heterogeneity, while accounting for spatial dependence in a cross-sectional setting. The first step of the procedure takes explicitly unobserved spatial heterogeneity into account to endogenously identify subsets of farms that follow a similar local production econometric model, i.e. spatial production regimes. The second step consists in the specification of a spatial autoregressive model with autoregressive disturbances and spatial regimes. The method is applied to two regional samples of olive growing farms in Italy. The main finding is that the identification of spatial regimes can help drawing a more detailed picture of the production environment and provide more accurate information to guide extension services and policy makers

SPATIAL LIMITED DEPENDENT VARIABLE MODELS: A REVIEW FOCUSED ON SPECIFICATION, ESTIMATION, AND HEALTH ECONOMICS APPLICATIONS

none2noModeling individual choices is one of the main aim in microeconometrics. Discrete choice models have been widely used to describe economic agents' utility functions and most of them play a paramount role in applied health economics. On the other hand, spatial econometrics collects a series of econometric tools, which are particularly useful when we deal with spatially distributed data sets. Accounting for spatial dependence can avoid inconsistency problems of the commonly used statistical estimators. However, the complex structure of spatial dependence in most of the nonlinear models still precludes a large diffusion of these spatial techniques. The purpose of this paper is then twofold. The former is to review the main methodological problems and their different solutions in spatial nonlinear modeling. The latter is to review their applications to health issues, especially those appeared in the last few years, by highlighting the main reasons why spatial discrete neighboring effects should be considered and suggesting possible future lines of development in this emerging field. Particular attention has been paid to cross-sectional spatial discrete choice modeling. However, discussions on the main methodological advancements in other spatial limited dependent variable models and spatial panel data models are also included.mixedAnna Gloria Billé; Giuseppe ArbiaBille', ANNA GLORIA; Arbia, Giusepp

Impact of COVID-19 on financial returns: a spatial dynamic panel data model with random effects

Using a dataset including financial market returns and volatility proxies for several countries, we analyzed the impact of Covid-19 deaths on the financial economy. From the modeling perspective, we consider a spatial panel data model for returns and a spatial dynamic panel data for volatilities. Proper marginal effects are calculated to exploit information on short- and long-term effects. A Chow test is used to identify the existence of a structural break in each series. Our empirical evidence shows that in the first weeks of the Covid-19 outbreak, until mid-March 2020, the identified break date, the spatial effect of Covid-19 deaths was statistically significant, leading to a contraction in returns and an increase in risk. Moreover, the effects disappeared in the remaining months as the financial markets moved back to pre-crisis levels, causing a decrease in the overall risk. Our evidence supports the behavioral impact of the pandemic on financial markets