Hayakawa, Kazuhiko Unit root test for short panels with serially correlated errors. (English) Zbl 1422.62205 Commun. Stat., Theory Methods 46, No. 8, 3891-3900 (2017). Summary: This paper proposes a unit root test for short panels with serially correlated errors. The proposed test is based on the instrumental variables (IVs) and the generalized method of moments (GMM) estimators. An advantage of the new test over other tests is that it allows for an ARMA-type serial correlation. A Monte Carlo simulation shows that the new test has good finite sample properties. Several methods to estimate the lag orders of the ARMA structure are briefly discussed. MSC: 62M07 Non-Markovian processes: hypothesis testing 62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH) Keywords:GMM; instrumental variables; panel data; unit root test; generalized method of moments; ARMA structure PDFBibTeX XMLCite \textit{K. Hayakawa}, Commun. Stat., Theory Methods 46, No. 8, 3891--3900 (2017; Zbl 1422.62205) Full Text: DOI References: [1] DOI: 10.1016/S0304-4076(00)00077-4 · Zbl 0967.62095 · doi:10.1016/S0304-4076(00)00077-4 [2] DOI: 10.1111/j.1468-0084.1987.mp49004006.x · doi:10.1111/j.1468-0084.1987.mp49004006.x [3] DOI: 10.1016/0304-4076(94)01642-D · Zbl 0831.62099 · doi:10.1016/0304-4076(94)01642-D [4] DOI: 10.1016/S0304-4076(98)00009-8 · Zbl 0943.62112 · doi:10.1016/S0304-4076(98)00009-8 [5] DOI: 10.1257/aer.98.5.1887 · doi:10.1257/aer.98.5.1887 [6] DOI: 10.1080/00036849400000081 · doi:10.1080/00036849400000081 [7] DOI: 10.1017/CBO9780511811241 · Zbl 1156.62092 · doi:10.1017/CBO9780511811241 [8] DOI: 10.1017/S0266466608090099 · Zbl 1231.62028 · doi:10.1017/S0266466608090099 [9] DOI: 10.1016/S0167-7187(00)00085-0 · doi:10.1016/S0167-7187(00)00085-0 [10] DOI: 10.1093/biomet/76.1.49 · Zbl 0678.62083 · doi:10.1093/biomet/76.1.49 [11] Hall A., Generalized Method of Moments (2005) · Zbl 1076.62118 [12] DOI: 10.1016/S0304-4076(98)00076-1 · Zbl 1041.62526 · doi:10.1016/S0304-4076(98)00076-1 [13] DOI: 10.1016/S0304-4076(03)00092-7 · Zbl 1041.62075 · doi:10.1016/S0304-4076(03)00092-7 [14] DOI: 10.1016/j.jeconom.2008.03.001 · Zbl 1419.62513 · doi:10.1016/j.jeconom.2008.03.001 [15] DOI: 10.1017/S0266466608090531 · Zbl 1286.62075 · doi:10.1017/S0266466608090531 [16] DOI: 10.2307/2527063 · Zbl 0795.62103 · doi:10.2307/2527063 [17] DOI: 10.1016/S0304-4076(01)00098-7 · Zbl 1020.62079 · doi:10.1016/S0304-4076(01)00098-7 [18] DOI: 10.1093/biomet/82.3.660 · doi:10.1093/biomet/82.3.660 [19] DOI: 10.1111/j.1468-0262.2004.00476.x · Zbl 1142.91672 · doi:10.1111/j.1468-0262.2004.00476.x [20] Newey W.K., Handbook of Econometrics 4 pp 2113– (1994) [21] DOI: 10.1016/0304-4076(91)90067-N · Zbl 0803.62099 · doi:10.1016/0304-4076(91)90067-N [22] DOI: 10.1093/rfs/hhn053 · doi:10.1093/rfs/hhn053 [23] Tibshirani R.J., J. Royal Stat. Soc. Ser. B 58 pp 267– (1996) [24] DOI: 10.1016/j.jspi.2005.11.004 · Zbl 1098.62113 · doi:10.1016/j.jspi.2005.11.004 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.