Efficient GMM estimation with singular system of moment conditions

References

• Abadir, K. M., & Magnus, J. R. (2005). Matrix algebra. New York: Cambridge University Press.
   Google Scholar
• Alessi, L., Barigozzi, M., & Capasso, M. (2011). Non-fundamentalness in structural econometric models: A review. International Statistical Review, 79, 16–47. doi: 10.1111/j.1751-5823.2011.00131.x
   Web of Science ®Google Scholar
• Bapat, R. B. (2012). Linear algebra and linear models (3rd ed.). London: Springer-Verlag.
   Google Scholar
• Barten, A. (1969). Maximum likelihood estimation of a complete system of demand equations. European Economic Review, 1, 7–73. doi: 10.1016/0014-2921(69)90017-8
   Web of Science ®Google Scholar
• Barten, A. (1977). The system of consumer demand function approach: A review. Econometrica, 45, 23–51. doi: 10.2307/1913286
   Web of Science ®Google Scholar
• Berndt, E. R., & Christensen, L. R. (1974). Testing for the existence of a consistent aggregate index of labor inputs. American Economics Review, 64, 391–403.
   Web of Science ®Google Scholar
• Bierens, H. J. (2007). Econometric analysis of linearized singular dynamic stochastic general equilibrium models. Journal of Econometrics, 136, 595–627. doi: 10.1016/j.jeconom.2005.11.008
   Web of Science ®Google Scholar
• Bierens, H. J., & Swanson, N. R. (2000). The econometric consequences of the ceteris paribus condition in economic theory. Journal of Econometrics, 95, 223–253. doi: 10.1016/S0304-4076(99)00038-X
   Web of Science ®Google Scholar
• Biørn, E. (2000). Panel data with measurement errors: Instrumental variables and GMM procedures combining levels and differences. Econometric Reviews, 19(4), 391–424. doi: 10.1080/07474930008800480
   Google Scholar
• Biørn, E., & Klette, T. (1998). Panel data with errors-in-variables: Essential and redundant orthogonal conditions in GMM-estimation. Economics Letters, 59, 275–282. doi: 10.1016/S0165-1765(98)00053-6
   Web of Science ®Google Scholar
• Chen, Y., Hong, C., & Riley, R. D. (2014). An alternative pseudolikelihood method for multivariate random-effects meta-analysis. Statistics in Medicine, 34(3), 361–380. doi: 10.1002/sim.6350
   Web of Science ®Google Scholar
• Dhrymes, P. J. (1962). On devising unbiased estimators for the parameters of a Cobb–Douglas production function. Econometrica, 30, 297–304. doi: 10.2307/1910218
   Web of Science ®Google Scholar
• Dhrymes, P. J., & Schwarz, S. (1987). On the existence of generalized inverse estimators in a singular system of equations. Journal of Forecasting, 6, 181–192. doi: 10.1002/for.3980060304
   Web of Science ®Google Scholar
• Fampa, M., & Lee, J. (2018). On sparse reflexive generalized inverses. Operations Research Letters, 46(6), 605–610. doi: 10.1016/j.orl.2018.09.005
   Web of Science ®Google Scholar
• Hall, A. (2005). Generalized method of moments. Oxford University Press: New York.
   Google Scholar
• Hansen, L. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50, 1029–1054. doi: 10.2307/1912775
   Web of Science ®Google Scholar
• Hansen, B., & West, K. (2002). Generalized method of moments and macroeconomics. Journal of Business & Economic Statistics, 20, 460–469. doi: 10.1198/073500102288618603
   Web of Science ®Google Scholar
• Haupt, H., & Oberhofer, W. (2006). Generalized adding-up in systems of regression equations. Economics Letters, 92, 263–269. doi: 10.1016/j.econlet.2006.03.001
   Web of Science ®Google Scholar
• Ireland, P. N. (2004). A method for taking models to the data. Journal of Economic Dynamics and Control, 28, 1205–1226. doi: 10.1016/S0165-1889(03)00080-0
   Web of Science ®Google Scholar
• Kreijger, R. G., & Neudecker, H. (1977). Exact linear restrictions on parameters in the general linear model with a singular covariance matrix. Journal of the American Statistical Association, 72, 430–432. doi: 10.1080/01621459.1977.10481014
   Web of Science ®Google Scholar
• Lai, H. (2008). Maximum likelihood estimation of singular systems of equations. Economics Letters, 99, 51–54. doi: 10.1016/j.econlet.2007.05.027
   Web of Science ®Google Scholar
• Leeper, E. M., Walker, T. B., & Yang, S.-C. S. (2013). Fiscal foresight and information flows. Econometrica, 81, 1115–1145. doi: 10.3982/ECTA8337
   Web of Science ®Google Scholar
• Mountford, A., & Uhlig, H. (2009). What are the effects of fiscal policy shocks?. Journal of Applied Econometrics, 24, 960–992. doi: 10.1002/jae.1079
   Web of Science ®Google Scholar
• Penrose, R. (1955). A generalized inverse for matrices. Mathematical Proceedings of the Cambridge Philosophical Society, 51, 406–413. doi: 10.1017/S0305004100030401
   Google Scholar
• Rao, C. R. (1972). Alternative econometric models of sales advertising relationships. Journal of Marketing Research, 9, 171–181. doi: 10.1177/002224377200900209
   Web of Science ®Google Scholar
• Rao, C. R., & Mitra, S. K. (1971). Generalized inverse of matrices and its applications. John Wiley & Sons, Inc.: New York.
   Google Scholar
• Riley, R., Abrams, K., Lambert, P., Sutton, A., & Thompson, J. (2007). An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes. Statistics in Medicine, 26(1), 78–97. doi: 10.1002/sim.2524
   PubMed Web of Science ®Google Scholar
• Schneeweiss, H. (2014). The linear GMM model with singular covariance matrix due to the elimination of a nuisance parameter. Technical Report 165, Dept. Statistics, Univ. Munich.
   Google Scholar
• Stewart, G. W. (1969). On the continuity of the generalized inverse. SIAM Journal of Applied Mathematics, 17, 33–45. doi: 10.1137/0117004
   Web of Science ®Google Scholar
• Theil, H. (1971). Principle of econometrics. John Wiley & Sons: New York.
   Google Scholar
• Velasco, C., & Lobato, I. N. (2018). Frequency domain minimum distance inference for possibly noninvertible and noncausal ARMA models. The Annals of Statistics, 46(2), 555–579. doi: 10.1214/17-AOS1560
   Web of Science ®Google Scholar
• Wansbeek, T. J. (2001). GMM Estimation in panel data models with measurement error. Journal of Econometrics, 104, 259–268. doi: 10.1016/S0304-4076(01)00079-3
   Web of Science ®Google Scholar
• Weiss, D (1968). Determinants of market share. Journal of Marketing Research, 5, 290–295. doi: 10.1177/002224376800500307
   Web of Science ®Google Scholar
• White, H. (1986). Instrumental variables analogs of generalized least squares estimators. Advances in Statistical Analysis and Statistical Computing, 1, 173–227.
   Google Scholar
• Xiao, Z. (2008). Generalized inverses in GMM estimation with redundant moment conditions. In Topics in Generalized Method of Moments Estimation with Application to Panel Data with Measurement Error, University of Wisconsin-Madison PhD thesis.
   Google Scholar
• Xiao, Z., Shao, J., & Palta, M. (2010a). GMM in linear regression for longitudinal data with multiple covariates measured with error. Journal of Applied Statistics, 37, 791–805. doi: 10.1080/02664760902890005
   Web of Science ®Google Scholar
• Xiao, Z., Shao, J., & Palta, M. (2010b). Instrumental variable and GMM estimation of panel data with measurement error. Statistica Sinica, 20(4), 1725–1747.
   Web of Science ®Google Scholar
• Xiao, Z., Shao, J., Xu, R., & Palta, M. (2007). Efficiency of GMM estimation in panel data models with measurement error. Sankhya: The Indian Journal of Statistics, 69, 101–118.
   Google Scholar
• Xu, L., Fampa, M., & Lee, J. (2019). Aspects of symmetry for sparse reflexive generalized inverses. arXiv:1903.05744v1[math.OC].
   Google Scholar