The long memory autoregressive distributed lag model and its application on Congressional approval

被引:4
作者
Tsay, Wen-Jen [1 ]
机构
[1] Acad Sinica, Inst Econ, Taipei 115, Taiwan
关键词
Autoregressive distributed lag model; Stochastic linear difference equation; Long memory; Lagged dependent variable; Instrumental variables; MOVING-AVERAGE; REGRESSION; DYNAMICS; INFERENCE; OPINION;
D O I
10.1016/j.electstud.2009.06.004
中图分类号
D0 [政治学、政治理论];
学科分类号
0302 ; 030201 ;
摘要
This paper considers the instrumental variables (IV) estimation of the autoregressive distributed lag (ADL) model consisting of fractionally integrated regressors and errors, while allowing for part of the regressors to be endogenous The idea of Liviatan (1963) and that of Tsay (2007) are combined to construct consistent and asymptotically normally distributed multiple-differenced two-stage-least-squares (MD-TSLS) and MD generalized method of moments (MD-GMM) estimators for the long memory ADL model The simulations show that the performance of the MD-GMM estimator is especially excellent even though the sample size is 100. The IV estimators are applied to the data of Durr, Gilmour, and Wolbrecht (1997) oil Congressional approval As compared to the 0.08 estimate of the long-run effect of presidential approval oil Congressional approval based oil the scalar ADL model of De Boef and Keele (2008), a stronger Support for the divided party government hypothesis IS found for a class of the vector ADL model which generates a corresponding long-run impact equal to 0 26 or higher (C) 2009 Elsevier Ltd. All rights reserved
引用
收藏
页码:128 / 143
页数:16
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