Dataset accompanying the publication "Introduction to cross-section spatial econometric models with applications in R". This paper introduces the spatial component in cross-section econometric estimations and specifically, the spatial dependence effect inherent in some of the variables involved in the modelling process. First, the spatial structure of the data from thematic maps is observed and Moran's spatial autocorrelation indicators are presented. Subsequently, the spatial weights matrix is built under different specifications. Finally, several modelling specification strategies are shown and the interpretation of the estimated coefficients. The theoretical concepts are illustrated with examples and their corresponding R software codes. This code and databases are available in this repository. Exploratory Spatial Data Analysis (ESDA) and spatial econometrics.

Multifactor error structures utilize factor analysis to deal with complex cross-sectional dependence in Time-Series Cross-Sectional data caused by cross-level interactions. The multifactor error structure specification is a generalization of the fixed-effects model. This paper extends the existing multifactor error models from panel econometrics to multilevel modeling, from linear setups to generalized linear models with the probit and logistic links, and from assuming serial independence to modeling the error dynamics with an autoregressive process. I develop Markov Chain Monte Carlo algorithms mixed with a rejection sampling scheme to estimate the multilevel multifactor error structure model with a p-th order autoregressive process in linear, probit, and logistic specifications. I conduct several Monte Carlo studies to compare the performance of alternative specifications and approaches with varying degrees of data complication and different sample sizes. The Monte Carlo studies provide guidance on when and how to apply the proposed model. An empirical application sovereign default demonstrates how the proposed approach can accommodate a complex pattern of cross-sectional dependence and helps answer research questions related to units' sensitivity or vulnerability to systemic shocks.

The pooled cross-sectional dataset (1359 firms in total) available for download has some specific features:

• The dataset includes data from wave 1 (2005-2007) and wave 2 (2008-2010) of the Nigerian innovation surveys.
• The year variable identifies the different survey waves. Wave 1 was completed in 2008 and wave 2, in 2011.
• The service variable sorts the observations broadly into manufacturing and services.
• The id variable identifies each unique firm. Repeatedness was ignored because repeated cases are only about 2.5%.
• As much as possible, variables have been matched across the two waves.
• Due to coding changes and some inconsistencies in the survey instrument, a few variables could not be matched.
• Any variable that could not be matched is retained in its original form.
• Some of the variables have notes attached to them. The notes are consistent with what is in the accompanying codebook.xls
• Item numbering on the questionnaire for the two waves are not consistent. Thus, rather than use question numbers for variable names – as is commonly done – intuitive variable names and labels (defined in detail in the accompanying codebook.xls) are used.
• Definitions of main concepts can be found in the accompanying codebook.xls.
• It is strongly recommended that users thoroughly familiarize themselves with the accompanying codebook as well as the questionnaires for each of the waves before applying the dataset. This is crucial especially because of the skip patterns. While everything was done to ensure that the skip patterns were all correctly established, there can be no guarantee of perfection.
• It is also strongly recommended that users be familiar with the nature of innovation surveys as this will help in understanding how to treat the data for analysis. The Oslo Manual, which is freely available online, is a very useful resource.

To have a feel of the sectoral distribution of the sample, type in Stata: tab service year

This data includes 8193 rural households from 19 coastal districts of Bangladesh. The data is extracted from the Household Income and Expenditure Survey (HIES) 2016-2017, which is a cross-sectional dataset with 46,076 observations.