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This paper considers the maximum likelihood estimation of a class of structural vector autoregressive fractionally integrated moving-average (VARFIMA) models. The structural VARFIMA model includes the fractional cointegration model as one of its special cases. We show that the conditional likelihood Durbin-Levinson (CLDL) algorithm of Tsay (2010a) is a fast and reliable approach to estimate the long-run effects as well as the short- and long-term dynamics of a structural VARFIMA process simultaneously. In particular, the computational cost of the CLDL algorithm is much lower than that proposed in Sowell (1989) and Dueker and Startz (1998). We apply the CLDL method to the Congressional approval data of Durr et al. (1997) and find that the long-run effect of economic expectations on Congressional approval is at least 0.5718, which is over twice the estimate of 0.24 found in Table 2 of Box-Steffensmeier and Tomlinson (2000). This paper also tests the divided party government hypothesis with the CLDL algorithm. [Copyright Elsevier Ltd.]

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) on Congressional approval. As compared to the 0.08 estimate of the long-run effect of presidential approval on Congressional approval based on 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. [Copyright Elsevier Ltd.]

This paper considers the estimation and inference problems of a general class of time-series-cross-section (TSCS) models consisting of stationary or nonstationary long memory regressors and errors, while allowing for cross-correlations and serial correlations in cross-section and time dimensions, respectively. Although the applicability of this class of TSCS models is far-reaching, we show that each regression coefficient of these models can be easily tested with the critical values from the standard normal distribution based on the approach proposed in this paper. Furthermore, our approach is built on Robinson's [Robinson, P.M., 1998. Inference-without-smoothing in the presence of nonparametric autocorrelation. Econometrica 66, 1163-1182] long-run variance estimator and thus does not involve the difficult problems of choosing a kernel function or a bandwidth parameter. We also demonstrate that, under various combinations of long memory processes and cross-section dimensions, the finite sample performance of our method for this class of long memory TSCS models is promising even though the time span is only 20. We then apply this method to re-examine the welfare spending studies of Hicks and Swank [Hicks, A., Swank, D., 1992. Politics, institutions and welfare spending in industrialized democraticies, 1960-1982. American Political Science Review 86, 658-674]. The testing results are different from the findings in Hicks and Swank (1992) and those in Beck and Katz [Beck N., Katz, J.N., 1995. What to do (and not to do) with time-series cross-section data. American Political Science Review 89, 634-647], because we find a weak but significant positive voter turnout effects when the number of differencing is equal to 1. [Copyright Elsevier Ltd.]