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}

@article{vsidak1967rectangular,
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}

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}

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}

@article{moon2015linear,
	title        = {Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects},
	author       = {Moon, Hyungsik Roger and Weidner, Martin},
	year         = 2015,
	journal      = {Econometrica},
	publisher    = {Wiley Online Library},
	volume       = 83,
	number       = 4,
	pages        = {1543--1579},
	doi          = {https://doi.org/10.3982/ECTA9382},
	url          = {https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA9382},
	keywords     = {Panel data, interactive fixed effects, factor models, perturbation theory of linear operators, random matrix theory},
	eprint       = {https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA9382},
	abstract     = {In this paper, we study the least squares (LS) estimator in a linear panel regression model with unknown number of factors appearing as interactive fixed effects. Assuming that the number of factors used in estimation is larger than the true number of factors in the data, we establish the limiting distribution of the LS estimator for the regression coefficients as the number of time periods and the number of cross-sectional units jointly go to infinity. The main result of the paper is that under certain assumptions, the limiting distribution of the LS estimator is independent of the number of factors used in the estimation as long as this number is not underestimated. The important practical implication of this result is that for inference on the regression coefficients, one does not necessarily need to estimate the number of interactive fixed effects consistently.}
}
@article{Mazumder2010SpectralRA,
	title        = {Spectral Regularization Algorithms for Learning Large Incomplete Matrices},
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	year         = 2010,
	journal      = {Journal of Machine Learning Research},
	volume       = 11,
	pages        = {2287--2322}
}
@article{bai2002determining,
	title        = {Determining the Number of Factors in Approximate Factor Models},
	author       = {Bai, Jushan and Ng, Serena},
	year         = 2002,
	journal      = {Econometrica},
	volume       = 70,
	number       = 1,
	pages        = {191--221},
	doi          = {https://doi.org/10.1111/1468-0262.00273},
	url          = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1468-0262.00273},
	keywords     = {factor analysis, asset pricing, principal components, model selection},
	eprint       = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/1468-0262.00273},
	abstract     = {In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors (r), which is an unresolved issue in the rapidly growing literature on multifactor models. We first establish the convergence rate for the factor estimates that will allow for consistent estimation of r. We then propose some panel criteria and show that the number of factors can be consistently estimated using the criteria. The theory is developed under the framework of large cross-sections (N) and large time dimensions (T). No restriction is imposed on the relation between N and T. Simulations show that the proposed criteria have good finite sample properties in many configurations of the panel data encountered in practice.}
}
@article{kleinberg2015prediction,
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}
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}
@article{wooldridge2023simple,
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}
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	publisher    = {Wiley Online Library}
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	volume       = 104,
	number       = 485,
	pages        = {166--176}
}
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	year         = 2008,
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	year         = 2023,
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	organization = {PMLR}
}
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	publisher    = {JMLR. org},
	volume       = 16,
	number       = 1,
	pages        = {3367--3402}
}
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	author       = {Stock, James H and Yogo, Motohiro},
	year         = 2002,
	publisher    = {National Bureau of Economic Research Cambridge, Mass., USA}
}
@article{chatterjee2015matrix,
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	author       = {Chatterjee, Sourav},
	year         = 2015
}
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	volume       = 9,
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}
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}
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	publisher    = {National Bureau of Economic Research Cambridge, Mass., USA}
}
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@article{sudhir2015peter,
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	publisher    = {INFORMS},
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@article{krauth2016bounding,
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	author       = {Krauth, Brian},
	year         = 2016,
	journal      = {Journal of Econometric Methods},
	publisher    = {De Gruyter},
	volume       = 5,
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	journal      = {Journal of political economy},
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	volume       = 113,
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	volume       = 34,
	number       = 3,
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@article{rosenbaum2015two,
	title        = {Two R packages for sensitivity analysis in observational studies},
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	year         = 2015,
	journal      = {Observational Studies},
	publisher    = {University of Pennsylvania Press},
	volume       = 1,
	number       = 2,
	pages        = {1--17}
}
@incollection{hodges2011estimates,
	title        = {Estimates of location based on rank tests},
	author       = {Hodges Jr, Joseph L and Lehmann, Erich L},
	year         = 2011,
	booktitle    = {Selected Works of EL Lehmann},
	publisher    = {Springer},
	pages        = {287--300}
}
@book{hollander2013nonparametric,
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	author       = {Hollander, Myles and Wolfe, Douglas A and Chicken, Eric},
	year         = 2013,
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}
@article{barrios2012clustering,
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	number       = 498,
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	publisher    = {Sage Publications Sage CA: Los Angeles, CA},
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@article{simonsohn2015better,
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	year         = 2015,
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}
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	publisher    = {American Psychological Association},
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	number       = 2,
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	month        = Jan,
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	doi          = {},
	url          = {https://ideas.repec.org/p/arx/papers/2301.11358.html},
	institution  = {arXiv.org},
	type         = {Papers},
	abstract     = {The present paper proposes a new treatment effects estimator that is valid when the number of time periods is small, and the parallel trends condition holds conditional on covariates and unobserved heterogeneity in the form of interactive fixed effects. The estimator also allow the control variables to be affected by treatment and it enables estimation of the resulting indirect effect on the outcome variable. The asymptotic properties of the estimator are established and their accuracy in small samples is investigated using Monte Carlo simulations. The empirical usefulness of the estimator is illustrated using as an example the effect of increased trade competition on firm markups in China.},
	keywords     = {}
}
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