Denis Kojevnikov, Vadim Marmer, Kyungchul Song
arXiv 4 Mar 2019 · Econometrics · publishedJournal of Econometrics (2020) · 36 citations (OpenAlex)
arXiv:1903.01059 · PDF · DOI · OpenAlex · Extracted main text
This paper is concerned with cross-sectional dependence arising because observations are interconnected through an observed network. Following Doukhan and Louhichi (1999), we measure the strength of dependence by covariances of nonlinearly transformed variables. We provide a law of large numbers and central limit theorem for network dependent variables. We also provide a method of calculating standard errors robust to general forms of network dependence. For that purpose, we rely on a network heteroskedasticity and autocorrelation consistent (HAC) variance estimator, and show its consistency. The results rely on conditions characterized by tradeoffs between the rate of decay of dependence across a network and network's denseness. Our approach can accommodate data generated by network formation models, random fields on graphs, conditional dependency graphs, and large functional-causal systems of equations.
appendix boundary found by appendix_command · 48% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Doukhan, P., Louhichi, S (1999) A new weak dependence condition and applications to moment inequalities | 1.000 | 5 | 3 | 100% |
| 2 | Jenish, N., Prucha, I. R (2009) Central limit theorems and uniform laws of large numbers for arrays of random fields | 0.843 | 4 | 3 | 75% |
| 3 | Conley, T. G (1999) GMM estimation with cross-sectional dependence | 0.811 | 4 | 2 | 100% |
| 4 | Borg, I., Groenen, P. J. F (2005) Modern Multidimensional Scaling | 0.644 | 2 | 2 | 100% |
| 5 | Kelejian, H. H., Prucha, I. R (2007) HAC estimation in a spatial framework | 0.644 | 2 | 2 | 100% |
| 6 | Kim, M. S., Sun, Y (2011) Spatial heteroskedasticity and autocorrelation consistent estimation of covariance matrix | 0.644 | 2 | 2 | 100% |
| 7 | Stein, C (1972) A bound for the error in the normal approximation to the distribution of a sum of dependent random variables | 0.644 | 2 | 2 | 100% |
| 8 | Chung, F., Lu, L (2001) The diameter of sparse random graphs | 0.511 | 3 | 2 | 33% |
| 9 | Johnsson, I., Moon, H. R (2019) Estimation of peer effects in endogenous social networks: Control function approach, Review of Economics and Statistics, forthco… | 0.511 | 2 | 1 | 100% |
| 10 | Andrews, D. W. K (1991) Heteroskedasticity and autocorrelation consistent covariance matrix estimation | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.