Abutaliev, A. & Anatolyev, S. (2013) Asymptotic variance under many instruments: numerical computations. Economics Letters
118(2), 272–274.
Adamczak, R., Latala, R., Litvak, A.E., Oleszkiewicz, K., Pajor, A., & Tomczak-Jaegermann, N. (2014) A short proof of Paouris’ inequality. Canadian Mathematical Bulletin
57, 3–8.
Anatolyev, S. (2012) Inference in regression models with many regressors. Journal of Econometrics
170(2), 368–382.
Anatolyev, S. (2013) Instrumental variables estimation and inference in the presence of many exogenous regressors. Econometrics Journal
16(1), 27–72.
Anatolyev, S. & Gospodinov, N. (2011) Specification testing in models with many instruments. Econometric Theory
27, 427–441.
Angrist, J. & Krueger, A. (1991) Does compulsory school attendance affect schooling and earnings?
Quarterly Journal of Economics
106, 979–1014.
Anderson, T.W., Kunimoto, N., & Matsushita, Y. (2010) On the asymptotic optimality of the LIML estimator with possibly many instruments. Journal of Econometrics
157, 191–204.
Anderson, T.W., Kunimoto, N., & Matsushita, Y. (2011) On finite sample properties of alternative estimators of coefficients in a structural equation with many instruments. Journal of Econometrics
165, 58–69.
Bagnoli, M. & Bergstrom, T. (2005) Log-concave probability and its applications. Economic Theory
26, 445–469.
Bai, Z. & Silverstein, J. (2010) Spectral analysis of large dimensional random matrices, 2nd edition. Springer.
Bekker, P.A. (1994) Alternative approximations to the distributions of instrumental variable estimators. Econometrica
62, 657–681.
Bekker, P.A. & Crudu, F. (2015) Jackknife instrumental variable estimation with heteroskedasticity. Journal of Econometrics
185(2), 332–342.
Bekker, P.A. & van der Ploeg, J. (2005) Instrumental variable estimation based on grouped data. Statistica Neerlandica
59, 239–267.
Borell, C. (1975) Convex set functions in d-space. Periodica Mathematica Hungarica
6(2), 111–136.
Calhoun, G. (2012) Hypothesis testing in linear regression when k / n is large. Journal of Econometrics
165(2), 368–382.
Davidson, R. & MacKinnon, J.G. (2006) The case against JIVE. Journal of Applied Econometrics
21(6), 827–833.
Hahn, J. & Inoue, A. (2002) A Monte Carlo comparison of various asymptotic approximations to the distribution of instrumental variables estimators. Econometric Reviews
21, 309–336.
Hansen, C., Hausman, J., & Newey, W.K. (2008) Estimation with many instrumental variables. Journal of Business & Economics Statistics
26, 398–422.
Hausman, J.A., Newey, W.K., Woutersen, T., Chao, J.C., & Swanson, N.R. (2012) Instrumental variable estimation with heteroskedasticity and many instruments. Quantitative Economics
3, 211–255.
Kolesár, M. (2015) Minimum distance approach to inference with many instruments. manuscript. Princeton University.
Kunimoto, N. (2012) An optimal modification of the LIML estimation for many instruments and persistent heteroscedasticity. Annals of the Institute of Statistical Mathematics
64(5), 881–910.
Ledoux, M. (2001) The concentration of measure phenomenon. Mathematical Surveys and Monographs, 89. American Mathematical Society.
Lee, Y. & Okui, R. (2012) Hahn–Hausman test as a specification test. Journal of Econometrics
167(1), 133–139.
Meurant, G. (1992) A review on the inverse of symmetric tridiagonal and block tridiagonal matrices. SIAM Journal on Matrix Analysis and Applications
13(3), 707–728.
Pastur, L. & Shcherbina, M. (2011) Eigenvalue Distribution of Large Random Matrices. Mathematical Surveys and Monographs, 171. American Mathematical Society.
Saumard, A. & Wellner, J.A. (2014) Log-concavity and strong log-concavity: A review. Statistics Surveys
8, 45–114.
Sherman, J. & Morrison, W.J. (1950) Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. Annals of Mathematical Statistics
21, 124–127.
van Hasselt, M. (2010) Many instruments asymptotic approximations under nonnormal error distributions. Econometric Theory
26, 633–645.
Wang, W. & Kaffo, M. (2016) Bootstrap inference for instrumental variable models with many weak instruments. Journal of Econometrics
192(1), 231–268.
Yaskov, P.A. (2014) Lower bounds on the smallest eigenvalue of a sample covariance matrix. Electronic Communications in Probability 19, article 83.
Yaskov, P.A. (2016) Controlling the least eigenvalue of a random Gram matrix. Linear Algebra and its Applications
504, 108–123.