Michaela Kesina

Bayesian spatial bivariate panel probit estimation

This paper formulates and analyzes Bayesian model variants for the analysis of systems of spatial panel data with binary dependent variables. The paper focuses on cases where latent variables of cross-sectional units in an equation of the system contemporaneously depend on the values of the same and, eventually, other latent variables of other cross-sectional units. Moreover, the paper discusses cases where time-invariant effects are exogenous versus endogenous. Such models may have numerous applications in industrial economics, public economics, or international economics. The paper illustrates that the performance of Bayesian estimation methods for such models is supportive of their use with even relatively small panel data sets.

Contagious Energy Prices

We develop an empirical model of the determinants of end‐use energy prices in 28 Organisation for Economic Co‐operation and Development countries. For this, we construct a unique panel data‐set and formulate a spatial panel econometric model with fixed country effects for the period of 1980–2009. A special feature of the proposed empirical model is that cross‐country interdependence in energy prices surfaces through two channels, a metric of similarity towards energy policy and a metric of trade in energy goods. We estimate the parameters of that model and conduct several comparative static experiments. We find that higher income per capita and lower efficiency in energy distribution tend to raise energy prices whereas higher net imports of energy and more abundant energy resource endowments reduce them. We also find that bilateral trade in energy goods is an important channel of transmission of shocks in energy prices across countries. The estimation results suggest that countries that use the same types of resources to generate energy are likely to transmit energy price shocks to each other through competition effects.

Small Sample Properties and Pretest Estimation of a Spatial Hausman–Taylor Model

This paper considers a Hausman and Taylor (1981) panel data model that exhibits a Cliff and Ord (1973) spatial error structure. We analyze the small sample properties of a generalized moments estimation approach for that model. This spatial Hausman-Taylor estimator allows for endogeneity of the time-varying and time-invariant variables with the individual effects. For this model, the spatial effects estimator is known to be consistent, but its disadvantage is that it wipes out the effects of time-invariant variables, which are important for most empirical studies. Monte Carlo results show that the spatial Hausman-Taylor estimator performs well in small samples. Key Words: Hausman-Taylor estimator; Spatial random effects; Small sample properties JEL No. C23, 31

Estimation of Some Nonlinear Panel Data Models With Both Time-Varying and Time-Invariant Explanatory Variables

The so-called “fixed effects” approach to the estimation of panel data models suffers from the limitation that it is not possible to estimate the coefficients on explanatory variables that are time-invariant. This is in contrast to a “random effects” approach, which achieves this by making much stronger assumptions on the relationship between the explanatory variables and the individual-specific effect. In a linear model, it is possible to obtain the best of both worlds by making random effects-type assumptions on the time-invariant explanatory variables while maintaining the flexibility of a fixed effects approach when it comes to the time-varying covariates. This article attempts to do the same for some popular nonlinear models.