Spatial effects in data analysis are important, especially in economics. Researchers have developed new methods to account for these effects in regression models. They found that traditional techniques for time series data may not work well for spatial data. By using cross-correlation statistics, they can detect spatial patterns in data. This helps to improve the accuracy of regression models when analyzing data that is spread out in space. The researchers also discuss different strategies for setting up these models and provide information on software that can help with spatial data analysis.