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1 Introduction For the past 40 years, researchers ( Bergstrom 1976 ; Chan et al. 1992 ; Czella, Karolyi, and Ronchetti 2007 ; Donnet and Samson 2013 ; Hansen 1982 ; Hansen and Scheinkman 1995 ; Jeisman 2005 ; Steiger et al. 2005 ; Ozaki 1985 ; Ozaki et al. 1992 ; Phillips 1959 ; Robinson 1976 ; Shoji and Ozaki 1997 ; Shoji 2013 ; Singer 1993 , 2004 ) have given a lot of attention for estimating continuous-time dynamic models from discrete time data sets. The Generalized Method of Moments (GMM) developed by Hansen (1982) and its extensions

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In this article, we consider a generalized method moments (GMM) estimator to estimate treatment effects defined through estimation equations using an observational data set from a complex survey. We demonstrate that the proposed estimator, which incorporates both sampling probabilities and semiparametrically estimated self-selection probabilities, gives consistent estimates of treatment effects. The asymptotic normality of the proposed estimator is established in the finite population framework, and its variance estimation is discussed. In simulations, we evaluate our proposed estimator and its variance estimator based on the asymptotic distribution. We also apply the method to estimate the effects of different choices of health insurance types on healthcare spending using data from the Chinese General Social Survey. The results from our simulations and the empirical study show that ignoring the sampling design weights might lead to misleading conclusions.

-run variance and volatility of volatility parameters based on the generalized method of moments (GMM). Recently, similar approaches for the estimations are studied in Bollerslev, Gibson, and Zhou (2011) and Garcia et al. (2011) for stochastic volatility models and Da Fonseca and Zaatour (2014) for the tick-structure of price movements. We have an additional high-frequency quantity compared with the existing estimations of stochastic volatility models, the realized third moment, which is especially useful to estimate the leverage parameter in stochastic volatility models

Development Economics . 8(4), pp. 581-601. DOI: 10.1017/S1355770X0300317. HANSEN, L. P. (1982) Large Sample Properties of Generalized Method of Moments Estimators. Econometrica . 50(4), pp. 1029-1054. DOI: 10.2307/1912775. HERYÁN, T. & TZEREMES, P. G. (2017). The bank lending channel of monetary policy in EU countries during the global financial crisis. Economic Modelling. 67, pp. 10-22. DOI: 10.1016/j.econmod.2016.07.017. IMERS, G. W. (2002) Generalized Method of Moments and Empirical Likelihood. Journal of Business and Economic Statistics. 20(4), pp. 493-506, DOI: 10

expectancy played a significant and positive role in economic growth. Foreign aid had a non-linear impact (negative impact of high aid flows) upon economic growth. KEYWORDS: foreign aid, economic growth, panel data, generalized method of moments ∗The author wishes to thank Editor Jannett Highfill and an anonymous referee for providing useful comments and suggestions that contributed to the improvement of this paper. Any errors are solely the responsibility of mine. 1. Introduction There are numerous studies that have studied the relationship of economic growth with

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This study assesses the competitive environment and the determinants of the Sub-Saharan Africa commercial banking sectors. We used the Lerner index that is generally acknowledged as the best at estimating the bank level competition and the Generalised Method of Moments (GMM) to study 440 commercial banks for the period 2006 to 2015. We found a monopolistic competitive banking market. We also observed that competition is driven by the level of bank capital including some bank specific variables. Hence, we concluded that the banking market of the SSA region is contestable and competitive. As such, we recommend, among other things, that policy makers should device measures to ensure an ongoing competitive banking environment while stimulating other economic variables to complement this feat.

have passed since Hansen (1982) introduced the Generalized Method of Moments (GMM) estimation technique. Even though the asymptotic properties of GMM are well known, its application to small samples presents several problems that are well documented in the literature. Beginning with the work by Tauchen (1986), several Monte Carlo experiments were conducted to assess the small sample properties of GMM in different setups. The general consensus of these exercises appears to be that:1 • Regardless of the method used for choosing the weighting matrix, inference based on

Monte Carlo methods, that is naturally more computer intensive than the approach retained here. We will however perform a Monte Carlo exercise comparing the spectral moment based method versus a special case of indirect inference, that is a simulated method of moment one. For further discussion on the estimation strategies of Wishart-based models, see Gouriéroux (2006). Finally, as the characteristic function is known in a closed form expression in the WASC case, we have the key ingredient to apply the continuum generalized method of moments, C-GMM in the sequel

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This study aims to examine the effect of exchange rate fluctuations and credit supply on the dividend repatriation policy of foreign subsidiaries of U.S. multinational corporations (MNCs) around the world. The difference generalised method of moments (GMM) estimator was applied to estimate the dynamic dividend repatriation model. The results suggest that the appreciation of host-country currency against the USD leads to higher dividend repatriation by the foreign subsidiaries of U.S. MNCs. Moreover, results reveal that higher availability of private credit in the host country results in lower dividend repatriation by the U.S. MNCs’ foreign subsidiaries.

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After a quick review of superpositions of OU (supOU) processes, integrated supOU processes and the supOU stochastic volatility model we estimate these processes by using the generalized method of moments (GMM). We show that the GMM approach yields consistent estimators and that it works very well in practice. Moreover, we discuss the influence of long memory effects.