Spatial Modeling of Fixed Effect and Random Effect with Fast Double Bootstrap Approach
DOI:
https://doi.org/10.21512/comtech.v14i1.8033Keywords:
spatial modeling, fixed effect, random effect, Fast Double Bootstrap (FDB)Abstract
The use of panel data on spatial regression has many advantages. However, testing the spatial dependency and parameter presumption generated in spatial regression of panel data becomes inaccurate when applied to regions with large numbers of small spatial units. One method of overcoming problems of small spatial unit sizes is the bootstrap method. The research aimed to combine cross-section and time-series panel data. The analysis was performed to extract information based on observations modified by the influences of space or location, known as spatial analysis of panels. The influence of location effects on spatial analysis was presented in the form of weighting. The research applied the Fast Double Bootstrap (FDB) method by modeling poverty rates on Flores Island. The results of the Hausman test show the right model, which is a random effect. Meanwhile, spatial dependency testing concludes spatial dependence and poverty modeling in Flores Island, which is more likely to be the Spatial Autoregressive Random (SAR) model. SAR random effect in modeling value has R2 of 77,38% and does not meet the normality assumption. SAR effect in modeling the FDB approach can explain the diversity of poverty rate in the Flores Island with 88,64% and meets residual normality assumptions. The analysis with the FDB approach on spatial panels shows better results than the common spatial panels.
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References
Badan Pusat Statistik Provinsi Nusa Tenggara Timur. (2020). Ringkasan data dan informasi kemiskinan Provinsi Nusa Tenggara Timur 2019. Retrieved from https://ntt.bps.go.id/publication/2020/03/06/e311fb1c494cced6e63de93a/ringkasan-data-daninformasi-kemiskinan-provinsi-nusa-tenggaratimur-2019.html
Canay, I. A., Santos, A., & Shaikh, A. M. (2021). The wild bootstrap with a “small” number of “large” clusters. The Review of Economics and Statistics, 103(2), 346–363.
Choi, J. E., & Shin, D. W. (2020). Block bootstrapping for a panel mean break test. Journal of the Korean Statistical Society, 49(3), 802–821.
Davidson, R., & Trokić, M. (2020). The fast iterated bootstrap. Journal of Econometrics, 218(2), 451–475.
Djogbenou, A. A., MacKinnon, J. G., & Nielsen, M. Ø. (2019). Asymptotic theory and wild bootstrap inference with clustered errors. Journal of Econometrics, 212(2), 393–412.
Du, K., Worthington, A. C., & Zelenyuk, V. (2018). Data envelopment analysis, truncated regression and double-bootstrap for panel data with application to Chinese banking. European Journal of Operational Research, 265(2), 748–764.
Elhorst, J. P. (2017). Spatial panel data analysis. Encyclopedia of GIS, 2, 2050–2058.
Galvao, A. F., Parker, T., & Xiao, Z. (2021). Bootstrap inference for panel data quantile regression. Retrieved from https://arxiv.org/abs/2111.03626
LaFontaine, D. (2021). The history of bootstrapping: Tracing the development of resampling with replacement. The Mathematics Enthusiast, 18(1), 78–99.
Lee, L. F., & Yu, J. (2020). Initial conditions of dynamic panel data models: On within and between equations. The Econometrics Journal, 23(1), 115–136.
Li, L., Hong, X., & Peng, K. (2019). A spatial panel analysis of carbon emissions, economic growth and hightechnology industry in China. Structural Change and Economic Dynamics, 49(June), 83–92.
Liu, S. F., & Yang, Z. (2020). Robust estimation and inference of spatial panel data models with fixed effects. Japanese Journal of Statistics and Data Science, 3, 257–311.
Lütkepohl, H., & Schlaak, T. (2019). Bootstrapping impulse responses of structural vector autoregressive models identified through GARCH. Journal of Economic Dynamics and Control, 101(April), 41–61.
MacKinnon, J. G., Nielsen, M. Ø., & Webb, M. D. (2021). Wild bootstrap and asymptotic inference with multiway clustering. Journal of Business & Economic Statistics, 39(2), 505–519.
MacKinnon, J. G., & Webb, M. D. (2018). The wild bootstrap for few (treated) clusters. The Econometrics Journal, 21(2), 114–135.
Mameli, V., Musio, M., & Ventura, L. (2018). Bootstrap adjustments of signed scoring rule root statistics. Communications in Statistics-Simulation and Computation, 47(4), 1204–1215.
Nieuwland, M. S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., ... & Huettig, F. (2018). Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLIFE, 7, 1–24.
Ou, B., Long, Z., & Li, W. (2019). Bootstrap LM tests for spatial dependence in panel data models with fixed effects. Journal of Systems Science and Information, 7(4), 330–343.
Roodman, D., Nielsen, M. Ø., MacKinnon, J. G., & Webb, M. D. (2019). Fast and wild: Bootstrap inference in Stata using boottest. The Stata Journal, 19(1), 4–60.
Schmidt, P. (2020). Econometrics. CRC Press.
Schuldt, A., Ebeling, A., Kunz, M., Staab, M., Guimarães-Steinicke, C., Bachmann, D., ... & Eisenhauer, N. (2019). Multiple plant diversity components drive consumer communities across ecosystems. Nature Communications, 10, 1–11.
Suparman, Y., & Ginanjar, I. (2021). A fixed effect panel spatial error model in identifying factors of poverty in West Java Province. Journal of Physics: Conference Series, 1776, 1–10.
Suryowati, K., Bekti, R. D., & Faradila, A. (2018). A comparison of weights matrices on computation of dengue spatial autocorrelation. IOP Conference Series: Materials Science and Engineering (Vol. 335, pp. 1–9). IOP Publishing.
Wang, Z., & Lam, N. S. N. (2020). Extending Getis–Ord statistics to account for local space–time autocorrelation in spatial panel data. The Professional Geographer, 72(3), 411–420.
Wang, J., Yamamoto, T., & Liu, K. (2021). Spatial dependence and spillover effects in customized bus demand: Empirical evidence using spatial dynamic panel models. Transport Policy, 105(May), 166–180.
Yang, Z. (2018). Bootstrap LM tests for higher-order spatial effects in spatial linear regression models. Empirical Economics, 55, 35–68.
Yolanda, A. M., & Yunitaningtyas, K. (2019). Spatial data panel analysis for poverty in East Java Province 2012-2017. Journal of Physics: Conference Series, 1265, 1–9.
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