Spatial econometric models allow for interactions among variables through the specification
of a spatial weight matrix. Practitioners often face the risk of misspecification of such a matrix …

Modeling 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 …

We apply local linear regression and sieve estimation technique to estimate functional
coefficients and an unknown spatial weighting function, respectively, via a nonparametric …

We analyze the spatial relationships of economic output dynamics in European regions from
2000 to 2019 using dynamic spatial autoregressive growth models. In contrast to previous …

Spatial models are a recent but growing addition to econometric research. They are
characterized by an attention to the location of observations (ie, ordered spatial locations) …

The dynamic general nesting spatial econometric model for spatial panels with common
factors is the most advanced model currently available. It accounts for local spatial …

In this study, we suggest using information criteria for nested and non-nested model
selection problems for the matrix exponential spatial specifications (MESS) under both …

This paper examines the suitability of Google Trends data for the modeling and forecasting
of interregional migration in Russia. Monthly migration data, search volume data, and macro …

Spatial regression models are widely used in numerous areas, including detecting and
predicting traffic volume, air pollution, and housing prices. Unlike conventional regression …

Spatial GARCH models, like all other spatial econometric models, require the definition of a
suitable weight matrix. This matrix implies a certain structure for spatial interactions. GARCH …