pdynmc version 0.9.1

Minor update of version 0.9.0 that fixes a bug in the estimation function, adjusts matrix calculations to achieve minor speed improvements, and robustifies general linear hypothesis testing functionality.

pdynmc

• fix bug that appeared when deriving moment conditions from the explanatory variables besides the lagged dependent variable (thanks to Massimo Giannini for pointing this out).
• adjust matrix calculations to achieve minor speed improvements
• adjust helper functions that allow limiting the lag range

wald.fct

• Robustify wald.fct by using generalized inverse in inversion of covariance matrix.

pdynmc version 0.9.0

Update of version 0.8.0 that includes visualizations for fitted model objects (coefficient-range plots for two-step and iterated estimation and plots of fitted values vs. residuals) and panel data structure

functions for exploratory analysis of panel data added

• data.info: Returns information on structure of a balanced/unbalanced panel data set
• strucUPD.plot: Visualizes structure of unbalanced panel data

generic functions added

• ninst: Returns the number of instruments of a fitted model
• optmIn: Returns input parameters used in numeric optimization of a fitted model
• wmat: Returns weighting matrix of a fitted model

methods added

• case.names.pdynmc: Returns variable names of cross-sectional and longitudinal identifiers of a fitted model
• coef.pdynmc: Returns coefficient estimates of a fitted model
• dummy.coef.pdynmc: Returns time dummy coefficient estimates of a fitted model
• model.matrix.pdynmc: Returns instrument matrix of a fitted model
• ninst.pdynmc: Returns instrument count of a fitted model
• nobs.pdynmc: Returns number of cross-sectional and longitudinal observations of a fitted model
• optimIn.pdynmc: Returns input parameters used in numeric optimization of a fitted model
• plot.pdynmc: Plot methods for fitted model; returns plot of fitted values vs. residuals (default) or coefficient range across iterations of the estimation procedure (two-step or iterated estimation)
• print.pdynmc: Print fitted model object in console
• variable.names.pdynmc: Returns vector with names of explanatory variables of a fitted model