We review, and extend, one of the classic dynamic models of conflict in economics by Richardson (1919) and Boulding (1962). Restrictions on parameters are relaxed to account for alliances and for peace-keeping, yielding new dynamic patterns of conflict. In addition, we explore an incrementalist...

The Israeli-Palestinian conflict constitutes a prominent example of a long-lasting political conflict which has major consequences for the livelihoods of the people on both sides. The agricultural sectors of the Palestinian and Israeli economies are tightly connected. However, various security...

Disregarding spatial dependence can invalidate methods for analyzing cross-sectional and panel data. We discuss ongoing work on developing methods that allow for, test for, or estimate, spatial dependence. Much of the stress is on nonparametric and semiparametric methods

The central limit theorem for nonparametric kernel estimates of a smooth trend, with linearly-generated errors, indicates asymptotic independence and homoscedasticity across fixed points, irrespective of whether disturbances have short memory, long memory, or antipersistence. However, the...

This paper considers the estimation of binary choice panel data models with discrete endogenous regressors. We present a switching probit model which accounts for selectivity bias as well as for other forms of time invariant unobserved heterogeneity. Individual effects are allowed to be...

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...