A geocomputational notebook to monitor subnational human development using Python. Besides exploratory data analysis, the notebook introduces geospatial mapping, spatial dependence, and spatial inequality.
Learn to map and monitor regional development across Bolivia with Python. The notebook shows how to explore the data, draw maps, and detect spatial patterns of inequality.
Summary We present pydynpd, a Python package which implements all the features in dynamic panel model with GMM (general method of moments). These features include: (1) difference and system GMM, (2) one-step, two-step, and iterative estimators, (3) robust standard errors including the one suggested by [@windmeijer2005finite], (4) Hansen over-identification test, (5) Arellano-Bond test for autocorrelation, (6) time dummies, (7) allows users to collapse instruments to reduce instrument proliferation issue, and (8) a simple grammar for model specification.