Description Details Author(s) References See Also
The PartialNetwork package implements instrumental variables (IV) and Bayesian estimators for the linear-in-mean SAR model (e.g. Bramoullé et al., 2009) when
the distribution of the network is available, but not the network itself. To make the computations faster PartialNetwork uses C++
through the Rcpp package (Eddelbuettel et al., 2011).
Two main functions are provided to estimate the linear-in-mean SAR model using only the distribution of the network. The function
sim.IV
generates valid instruments using the distribution of the network (see Propositions 1 and 2 in Boucher and Houndetoungan (2020)). Once the instruments are constructed,
one can estimate the model using standard IV estimators. We recommend the function ivreg
from the package AER (Kleiber et al., 2020). The function mcmcSAR performs a Bayesian estimation based on an adaptive MCMC (Atchadé and Rosenthal, 2005). In that case,
the distribution of the network acts as prior distribution for the network.
The package PartialNetwork also implements a network formation model based on Aggregate Relational Data (McCormick and Zheng, 2015; Breza et al., 2017). This part of the package
relies on the functions rvMF, dvMF and logCpvMF partly implemented in C++, but using code from movMF (Hornik and Grün, 2014).
Maintainer: Elysée Aristide Houndetoungan ariel92and@gmail.com
Authors:
Vincent Boucher vincent.boucher@ecn.ulaval.ca
Atchadé, Y. F., & Rosenthal, J. S. (2005). On adaptive markov chain monte carlo algorithms. Bernoulli, 11(5), 815-828. https://projecteuclid.org/euclid.bj/1130077595.
Boucher, V., & Houndetoungan, A. (2020). Estimating peer effects using partial network data.
Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of econometrics, 150(1), 41-55. https://www.sciencedirect.com/science/article/abs/pii/S0304407609000335.
Breza, E., Chandrasekhar, A. G., McCormick, T. H., & Pan, M. (2020). Using aggregated relational data to feasibly identify network structure without network data. American Economic Review, forthcoming., https://arxiv.org/abs/1703.04157
Eddelbuettel, D., François, R., Allaire, J., Ushey, K., Kou, Q., Russel, N., ... & Bates, D. (2011),
Rcpp: Seamless R and C++
integration. Journal of Statistical Software, 40(8), 1-18.
http://www.jstatsoft.org/v40/i08/.
Hornik, K., & Grün, B. (2014). movMF: An R package for fitting mixtures of von Mises-Fisher distributions. Journal of Statistical Software, 58(10), 1-31. https://epub.wu.ac.at/4893/.
Kleiber, C., Zeileis, A., & Zeileis, M. A. (2020). Package ‘AER’. R package version 1.2, 4. https://cran.r-project.org/package=AER.
Mardia, K. V. (2014). Statistics of directional data. Academic press. https://www.elsevier.com/books/statistics-of-directional-data/mardia/978-0-12-471150-1.
McCormick, T. H., & Zheng, T. (2015). Latent surface models for networks using Aggregated Relational Data. Journal of the American Statistical Association, 110(512), 1684-1695. https://www.tandfonline.com/doi/abs/10.1080/01621459.2014.991395.
Wood, A. T. (1994). Simulation of the von Mises Fisher distribution. Communications in statistics-simulation and computation, 23(1), 157-164. https://www.tandfonline.com/doi/abs/10.1080/03610919408813161.
Useful links:
Report bugs at https://github.com/ahoundetoungan/PartialNetwork/issues
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.