| 1 |
Guggenberger, P. and Smith, R. (2006). Generalized empirical likelihood tests in time series models with potential identification failure, Manuscript, UCLA
|
| 2 |
Hannan, E. J. (1970). Multiple Time Series, Wiley, New York
|
| 3 |
Kitamura, Y. and Stutzer, M. (1997). An information-theoretic alternative to generalized method of moments estimation, Econometrica, 65, 861-874
DOI
ScienceOn
|
| 4 |
Kleibergen, F. (2005). Testing parameters in GMM without assuming that they are identified, Econo-metrica, 73, 1103-1123
DOI
ScienceOn
|
| 5 |
Otsu, T. (2006). Generalized empirical likelihood inference for nonlinear and time series models under weak identification, Econometric Theory, 22, 513-527
DOI
ScienceOn
|
| 6 |
Priestley, M. B. (1981). Spectral Analysis and Time Series, Academic Press, New York
|
| 7 |
Smith, R. J. (2005). Automatic positive semidefinite HAC covariance matrix and GMM estimation, Econometric Theory, 21, 158-170
DOI
ScienceOn
|
| 8 |
Stock, J. H. and Wright, J. (2000). GMM with weak instruments, Econometrica, 68, 1055-1096
DOI
ScienceOn
|
| 9 |
Sun, Y. X., Phillips, P. C. B. and Jin, S. (2005). Optimal bandwidth selection in heteroskedasticity-autocorrelation robust testing, Econometrica
DOI
ScienceOn
|
| 10 |
Tripathi, G. and Kitamura, Y. (2003). Testing conditional moment restrictions, The Annals of Statistics, 31, 2059-2095
DOI
ScienceOn
|
| 11 |
Velasco, C. and Robinson, P. (2001). Edgeworth expansions for spectral density estimates and stu-dentized sample mean, Econometric Theory, 17, 497-539
DOI
ScienceOn
|
| 12 |
Andrews, D. W. K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix esti-mation, Econometrica, 59, 817-858
DOI
ScienceOn
|
webmaster
