Package: SDPDmod 0.0.5

SDPDmod: Spatial Dynamic Panel Data Modeling

Spatial model calculation for static and dynamic panel data models, weights matrix creation and Bayesian model comparison. Bayesian model comparison methods were described by 'LeSage' (2014) <doi:10.1016/j.spasta.2014.02.002>. The 'Lee'-'Yu' transformation approach is described in 'Yu', 'De Jong' and 'Lee' (2008) <doi:10.1016/j.jeconom.2008.08.002>, 'Lee' and 'Yu' (2010) <doi:10.1016/j.jeconom.2009.08.001> and 'Lee' and 'Yu' (2010) <doi:10.1017/S0266466609100099>.

Authors:Rozeta Simonovska [aut, cre]

# Install 'SDPDmod' in R:

install.packages('SDPDmod', repos = c('https://rozetasimonovska.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

On CRAN:

Last updated 10 months agofrom:89b390490c. Checks:8 OK. Indexed: yes.

Last update: 2024-04-12
Started: 2022-07-14

Last update: 2023-04-13
Started: 2022-07-14

Help page	Topics
Bayesian log-marginal posterior probabilities for spatial panel models	blmpSDPD
Extract coefficents from model of class SDPDm	coef.SDPDm
Double-Power Distance Weights Matrix	DDistMat
Distance weights matrix (Inverse distance, Exponential distance or Double-Distance matrix)	DistWMat
Maximum eigenvalue normalization	eignor
Exponential distance matrix	ExpDistMat
Distance between the centroids of NUTS3 regions in Germany	gN3dist
Impacts for 'SDPDm' objects	impactsSDPDm
Inverse distance matrix	InvDistMat
Is the matrix row-normalized	isrownor
m nearest neighbors based on a distance matrix	mNearestN
1st to m-th order neighbors matrix	mOrdNbr
Print for class blmpSDPD	print.blmpSDPD
print for class SDPDm	print.SDPDm
Print summary for class impactsSDPDm	print.summary.impactsSDPDm
Print of summary for class SDPDm	print.summary.SDPDm
Row-normalization	rownor
Spatial dynamic panel data lag model with fixed effects maximum likelihood estimation.	SDPDm
Shared boundary matrix	SharedBMat
Summary for class impactsSDPDm	summary.impactsSDPDm
Summary for class SDPDm	summary.SDPDm
Spatial weights matrix of 46 USA states	usa46