during the 1960’s, many as was firmly supported by the historical founders of econometrics, had hoped that econometrics would provide a sound scientific foundation for econometrics in which each element of specification would be determined primarily on the basis of economic theory. however, due to misusing of econometrics and also wide usage of the so called cookbook econometrics, many research...

The classical approach to estimate spatial models lays on the choice of a spatial weights matrix that reflects the interactions among locations. The two main alternative proposals to specify the structure of these weights may be called exogenous and endogenous procedures. In this paper, we propose an intermediate solution by using entropy econometrics. The basic idea is that, once an exogenous ...

This paper summarizes my previous work in Lin (2010), in which I use spatial econometrics to analyze air pollution externalities. In Lin (2010), state-by-state source-receptor transfer coefficients that can be used as a basis for a locationdifferentiated permit system are estimated. Results affirm the importance of regional transport in determining local ozone air quality, although owing to non...

Spatial econometrics has few studies on multivariate ordinal responses. This study proposes a dynamic spatial ordered probit (DMSOP) model, which is the first attempt to capture temporal and dependencies simultaneously for The parameters are calculated using Bayesian inference based Markov chain Monte Carlo sampling. DMSOP model performs effectively with simulated data. Furthermore, applied two...

The journal Computational Statistics and Data Analysis aims to have regular issues on computational econometrics. Of particular interest are papers in important areas of econometric applications where both computational techniques and numerical methods have a major impact. The goal is to provide sources of information about the most recent developments in computational econometrics that are cur...

Due to criticisms of potential identification issues within spatial panel data models, this study contributes to the literature by comparing forecasts of U.S. state-level carbon dioxide emissions against empirical reality using panel data models with and without spatial effects. From a policy standpoint, understanding how to predict emissions is important for designing climate change mitigation...

An estimation framework and a user-friendly software implementation are described for maximum likelihood estimation of panel data models with random effects, a spatially lagged dependent variable and spatially and serially correlated errors. This specification extends static panel data models in the direction of serial error correlation, allowing richer modelling possibilities and more thorough...

In spatial econometrics, the typical alternative of spatial autocorrelation is expressed in the form of a spatial autorregressive process. While the bulk of the literature is devoted to specification tests and estimation methods for this model, alternatives have been suggested as well. In this paper, we consider an alternative that takes the form of the spatial error components formulation prop...