Improving the multi-dimensional comparison of simulation results: a spatial visualization approach
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Results from simulation experiments are important in applied spatial econometrics to, for instance, assess the performance of spatial estimators and tests for finite samples. However, the traditional tabular and graphical formats for displaying simulation results in the literature have several disadvantages. These include loss of results, lack of intuitive synthesis, and difficulty in comparing results across multiple dimensions. We propose to address these challenges through a spatial visualization approach. This approach visualizes model precision and bias as well as the size and power of tests in map format. The advantage of this spatial approach is that these maps can display all results succinctly, enable an intuitive interpretation, and compare results efficiently across multiple dimensions of a simulation experiment. Due to the respective strengths of tables, graphs and maps, we propose this spatial approach as a supplement to traditional tabular and graphical display formats.
KeywordsSpatial visualization Monte Carlo simulation experiments Spatial econometrics
JEL ClassificationY1 C5
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- Anselin, L.: Spatial econometrics. Kluwer Academic, Dordrecht (1988) Google Scholar
- Anselin, L., Arribas-Bel, D.: Spatial fixed effects and spatial dependence. Working Paper, GeoDa Center for Geospatial Analysis and Computation (2011) Google Scholar
- Anselin, L., Rey, S.: The performance of tests for spatial dependence in a linear regression. Technical Report, National Center for Geographic Information and Analysis (NCGIA), University of California, Santa Barbara (1990) Google Scholar
- Baltagi, B.H., Egger, P., Pfaffermayr, M.: A Monte Carlo study for pure and pretest estimators of a panel data model with spatially autocorrelated disturbances. Ann Econ Stat 87/88(3), 11–38 (2007) Google Scholar
- Florax, R., de Graaff, T.: The performance of diagnostic tests for spatial dependence in linear regression models: a meta-analysis of simulation studies. In: Anselin, L., Florax, R., Rey, S. (eds.) Advances in Spatial Econometrics: Methodology, Tools and Applications, pp. 29–65. Springer, Berlin (2004) Google Scholar
- Tufte, E.R.: The Visual Display of Quantitative Information, 2nd edn. Graphics Press, Nuneaton (2001) Google Scholar
- Wilk, M.B., Gnanadesikan, R.: Probability plotting methods for the analysis of data. Biometrika 33(1), 1–17 (1968) Google Scholar