There are only very limited applications of a **panel** framework in the estimation of the gravity equation and one of the very few exceptions is Rose (2004). Egger (2000) suggests that the proper econometric specification of the gravity model in most applications would be one of fixed country and time effects. These fixed effects are due to the omitted variables specific to cross-sectional units (Hsiao, 1986). They can be trade policy measures including tariff and non-tariff barriers and export driving or impeding "environmental" variables. They are not random but deterministically associated with certain historical, political, geographical and other factors (Egger 2000). However, Baldwin (1994) employs a random effects model and Matyas (1997, 1998) does not give preference to the fixed over random effects model or vice versa. Following the discussion of Baltagi (2001) and Greene (2000), this study employs the Hausman test to decide statistically whether a random or fixed effects model would be more appropriate for our **data** set.

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infrastructure itself and associated ICTs. Indirect impacts come from all aspects of economic activity affected by broadband and which drive economic growth (Collins et.al. (2007)). Several studies have examined how firm level productivity is impacted by broadband (See Holt and Jamison (2009) for an extensive review of this literature). A key theme of this literature is that broadband enables the emergence of new business **models**, processes and innovation. This in turn reduces costs, increases productivity and improves firm efficiency. A study by The Allen Consulting Group (2002) reports findings from a survey of Australian businesses on the cost savings derived from using broadband Internet. The survey results indicate that businesses experienced cost savings of around 6.3 per cent from the use of broadband internet compared to 1.5 per cent from the use of dial up internet. The study claims that the reported average cost savings would result in an overall productivity gain of around 0.32 per cent for Australian businesses. These findings are consistent with a more recent survey by the Australian Industry Group (2008) reported that over 93 per cent of the firms surveyed indicated that broadband had a positive impact on their productivity and efficiency.

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Under this general structure, we can have three alternative **models**. On one extreme, we can have dynamic fixed effect estimators (DFE) where intercepts are allowed to vary across the countries and all other parameters and error variances are constrained to be the same. At the other extreme, one can estimate separate equations for each group and calculate the mean of the estimates to get a glimpse of the over-all picture. This is called mean group estimator (MG). Pesaran and Smith (1995) showed that MG gives consistent estimates of the averages of parameters. The intermediate alternative is pooled mean group (PMG) estimator, suggested by Pesaran and Shin (1999). It allows intercepts, short-run coefficients and error variances to differ freely across the countries but the long run coefficients are constrained to be the same; that means, ψ i = ψ and π i = π for all i in equation (1) while θ i , λ ij etc of

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Following the two steps of Engle and Granger (1987) to expose Granger causality among the variables in both the long-run and short-run, we employed a **panel**- based vector error-correction (PVEC) model. To this end, we first estimated Eq. (1) through the FMOLS estimator and obtained the residuals to define the first-lagged residuals as the error-correction term. We then estimated the following dynamic error-correction **models** (12a–12c) through the pooled mean group (PMG) estimator proposed by Pesaran et al. (1999). In general, the GMM estimator, developed by Arellano - Bond (1991), is used in **panel** causality tests. However, the GMM estimator requires pooling of individuals and allows only the intercepts to differ across countries. However, the PMG estimator allows for variation in intercept, slope coefficients, and error variance across cross-sectional units, and therefore takes heterogeneity among cross-section members of the **panel** into account. The PMG estimator, however, does not allow for cross-sectional dependence. Therefore, we transform the variables by time demeaning the **data** (see Salim et al. 2014).

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For the **panel** **data** analysis, the **data** set consists of i = 1,…..,N cross sections (number of groups), and several points of time series for each group t = 1,….,T(i), or a cross section of N time series each of length T(i). **Panel** **data** analysis can be divided into fixed effects (FE) and random effects (RE) **models** 1 . FE model is also known as least squares with group dummy variables. In the FE model, variation across groups (individuals) or time is confined in shifts of the regression function; i.e., changes in the intercepts. On the other hand, the RE model treats the individual effects as a random component of the error term. The RE model assumes a structure on the error term, and a feasible generalized least-square technique determines the parameters. The major drawback of the RE model is the assumption that the unobserved individual effect is uncorrelated with the observed regressors. GLS estimation yields biased and inconsistent parameters in the presence of such correlation. The RE model is appropriate when correlated omitted variables are not an issue. The estimation technique that best fit the **data** were chosen based on Likelihood ratio, Breush and Pagan’s LM test, and Hausman’s Chi-squared statistics. The FE model turned out to be the best specification.

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Another important supply condition, that is considered to be promoting labour- intensive and export-oriented FDI, is the human capital both in terms of quality and availability. In this the capital stock created by investing over and above the depreciated capital, expands the productivity potentials of a firm or a country and enables FDI growth enhancing effects (De Mello, 1997). This, however, presupposes a minimum human-capital efficiency level and assumes that further training is attainable. However, empirical literature concerning the impact of educational level on inbound FDI appears to be counter-intuitive. Cheng & Kwan (2000), for example, argue that none of the education variables (expressed as percentage of population with primarily and high education) has a positive and significant effect on FDI, while Cheng & Zhao (1995) report similar results. Guntlach, (1995) argues that the poor explanatory power of human capital accumulation is attributed to the fact that education creates externalities and spillover effects in production, which are hard to capture using standard set of variables. More explanatory power can be achieved by identifying the role of human capital augmentation, rather than human capital accumulation, which may be poor explanatory variable in growth **models** because the crucial role of educational variables is difficult to be captured in using standard growth accounting.

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Since there is no correlation between the units, first-generation tests assuming there is no correlation will be used to determine the stability of the series of variables. Levin, Lin ve Chu-LLC (2002), Im, Peseran ve Shin-IPS (2003), Extended Dickey Fuller-ADF focused Fisher (Mandala, Wu 1999), Fisher Philips ve Perron-PP (Choi 2001) ve Breitung-BRG (2000) unit root tests were performed. According to the unit root test results (Appendix 1); while the GINI variable is stationary in the fixed trendy model LLC and Fisher Philips and Perron (PP) tests, it is not stationary in Im, IPS and Breitung (BRG) tests. It is stationary all over the trendy-trendless **models**. Also it is stationary in all tests that are calculated by taking the first-order differences of the GINI variable in short there is no unit root in series.

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This study uses **panel** **data** to examine for the first time ever the short- and long-run effects of ICT use and economic growth on electricity consumption in **OECD** countries for the period of 1985- 2012. It employs a battery of powerful econometric techniques including non-conventional **panel** unit root test that accounts for the presence of cross-sectional dependence, **panel** cointegration test, the Pooled Mean Group regression (PMG) method and recently introduced Dumetrescu-Hurlin (DH) causality test. The **panel** unit root test confirms that all the series in the study are first- difference stationary even in the presence of cross-sectional dependence indicating cointegrating relationship between the variables. **Panel** Pedroni cointegration test results confirm the cointegrating relationship between the variables in both **models** using two different indicators of ICT use. Estimation results suggest a highly positive significant relationship between ICT use and electricity consumption and between electricity consumption and economic growth both in the short- and the long-run. The findings are robust across both **models**. Also causality results suggest that electricity consumption causes economic growth. Both mobile and Internet use cause electricity consumption and economic growth.

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**PANEL** **DATA** **MODELS** WITH INTERACTIVE EFFECTS 3 Similarly, we allow common regressors, which do not vary across individuals, such as prices and policy variables. The corresponding regression coefficients are individual-dependent so that individuals respond differently to policy or price changes. In our view, this is a sensible way to incorporate time-invariant and common regressors. For example, wages associated with education and with gender are more likely to change over time rather than remain constant. In our analysis, time invariant regressors are treated as the components of λ i that are observable, and common regressors as the components of f t that

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The rest of the paper is organized as follows. The next section presents the model, describes our methodology, and gives asymptotic properties of our estimators. We first consider **panel** **data** **models** with only individual effects, then we extend our methodology to treat two-way effects **models**. Section 3 presents some Monte Carlo evidence on how our estimator behaves in the finite sample setting. All mathematical proofs are provided in the appendix.

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The econometrics literatures focus on three types of GMM estimators when studying the DPD **models**. The First is first-difference GMM (DIF) estimator which presented by Arellano and Bond [4], and the second is level GMM (LEV) estimator which presented by Arellano and Bover[5], while the third is system GMM (SYS) estimator which presented byBlundell and Bond [6]. Since the SYS estimator combines moment conditions of DIF and LEV estimators, and it is generally known that using many instruments can improve the efficiency of various GMM estimators (Arellano and Bover[5]; Ahn and Schmidt [2]; Blundell and Bond [6]). Therefore, the SYS estimator is more efficient than DIF and LEV estimators. Despite the substantial efficiency gain, using many instruments has two important drawbacks: increased bias and unreliable inference (Newey and Smith [10]; Hayakawa [8]). Moreover, the SYS estimator does not always work well; Bun and Kiviet [7] showed that the bias of SYS estimator becomes large when the autoregressive parameter is close to unity and/or when the ratio of the variance of the individual effect to that of the error term departs from unity.

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This study uses Susenas **data** set covering more than 200,000 households with a fair coverage of both rural and urban households. The Susenas is collected annually by the Indonesian bureau of statistics known as Bapenas. Household level **data** is collected every year on several variables such as health, education, income, expenditure etc. This study conducts a **panel** regression using household level expenditure **data** aggregated into 33 provinces. 302 observations are used. Provincial import and export **data** are collated from several annual trade bulletins in other to construct a measure of globalization. It is worth mentioning that provinces like Aceh, Papua and Maluku have not been covered in some rounds of **data** collection. This gives rise to missing observations

This study employs **panel** **data** estimation **models** to investigate the determinants of household savings in South Africa over the period 2008 – 2012. The novelty of some **panel** **data** **models** is their power to overcome the problems of endogeneity bias, in addition to controlling for unobserved heterogeneity across households. The study used the three waves of the new unique and rich first national representative longitudinal survey, the National Income Dynamics Study (NIDS), which tracks changes in individuals’ livelihoods over time. The distinctiveness of NIDS **data** is that it is available in a **panel** format and can be used to investigate the structure and impact of different aspects of socio- economic factors on household savings. The results of this study reveal that household savings in South Africa are strongly driven by income, age structure, education achievement and employment status. Yet the causal nexus between savings and the household size was found to be negative, a sign that larger families compromise households savings prospects.

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Sahin et al (2011) provide a comprehensive summary of the HTP. They track the year to year performance of 352 general hospitals over 2005-8, using Malmquist analysis. We adopt a narrower focus and concentrate on efficiency and quality of care issues in one segment of the health sector, namely provincial hospitals during 2009. We choose to focus on such hospitals for two reasons. First, as pointed out by **OECD** (2008, p 11- 12) prior to 2003 “there were regional and urban-rural disparities in utilization of health care services, and accessing health services in rural areas was significantly harder and more expensive” . Lack of personnel was an important problem whe reby “12% rural health centers and did not have doctors and two- thirds of rural health posts did not have midwives ” (**OECD** , 2008 p37). The HTP, via increasing the number of health personnel by 100,000 and enforcing the requirement for newly trained doctors to serve in rural areas, has brought about significant improvements in the distribution of both physicians and nurses. Nevertheless, significant disparities remain (**OECD**, 2008, p74). Therefore wringing out inefficiencies in small town and rural settings is more urgent compared to large urban centers.

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**OECD** Europe countries, constructed by the International Energy Administration. As discussed earlier, the price differences arise due to the shale gas production boom, which has been widely unanticipated. The price differences cannot be arbitraged away directly, due to the inherent physical properties of natural gas discussed in the previous section. Pure trade theory predicts that the U.S. would export natural gas indirectly through value added in the form of processed goods for which trade costs relative to the value of the good are sufficiently lower. Using the price gap as an interaction term makes it particularly easy to interpret the coefficients. In the main tables, we focus on US exports to all countries. However, we also restrict the analysis to **OECD** countries where we do have trading country natural gas price **data**. While the **OECD** countries are only 28 countries out of a total of 233 destinations, they account for more than 62 % of the value of all US exports in 2005. 19 For this subset, the interaction term in the above

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Complex designs are often used to select the sample which is followed over time in a **panel** survey. We consider some parametric **models** for **panel** **data** and discuss methods of estimating the model parameters which allow for complex schemes. We incorporate survey weights into alternative point estimation procedures. We also consider variance estimation using linearization methods to allow for complex sampling, and indicate connections with established asymptotically distribution free (ADF) methods. The behaviour of the proposed inference procedures are assessed in a simulation study, based upon **data** from the British Household **Panel** Survey. There appear to be some advantages of using the weighted maximum likelihood (ML) point estimation method compared to the weighted ADF method. Variance estimation methods that allow for clustering tend to lead to improvements in terms of bias. However, the variance estimator for the weighted ML estimator performs better than the ADF variance estimators.

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The nexus between external openness and economic performance has been extensively studied by both theoretical and empirical economists through a variety of different analytical frameworks and statistical methods. In the neoclassical literature, in the spirit of Solow (1956) and Swan (1956) growth **models**, the extent to which FDI and trade affect growth was limited. With diminishing returns to capital, external openness may affect only the level of income but not its steady state rate of growth. Thus, in these **models** the impact of increased external openness is confined only to the short run, the magnitude and duration of which depend on the transitional dynamics to the steady state growth path determined by the exogenously given rate of technological progress.

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In the model, the issue of autocorrelation, variable variance and correlation between units was tried to be solved by the method of Driscoll-Kraay for correction of standard errors. When it is considered that the time dimension T is large, Driscoll and Kraay (1998) show that standard nonparametric time series covariance matrix estimators can be developed to be robust for all forms of spatial and periodic correlation. The methodology of Driscoll and Kraay makes a correction of Newey-West type for horizontal sectional averages. The standard error estimates corrected in this way guarantee the consistency of covariance matrix estimators independently of horizontal section N. Under the hypothesis that there is heteroskedasticity, autocorrelation and correlation between units, a consistent estimate can be made according to t statistics calculated with resistant standard errors in the **panel** **data** model (Tatoğlu, 2012: 266-269).

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In microeconomics, **panel** **data** often exhibits a large-N, small-T feature, which raises the necessity to investigate the asymptotics under fixed-T setup. This section addresses this concern. It is well known that the within group estimators for dynamic **panel** **data** mod- els are inconsistent due to the incidental parameters issue, see, e.g., Anderson and Hsiao (1981). So we only consider the static model. We note, however, that the within-group estimators with some carefully designed bias correction method would have remarkable finite sample performance in dynamic **panel** **models** even it is inconsistent, see Dhaene and Jochmans (2015). So the ML estimators, which reduces to the within group estima- tors, are still useful in practical application in this viewpoint.

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Generally speaking, Bayesian analysis starts with a prior belief and updates it with **data**. It is desirable to ensure that the prior belief does not dominate the posterior inference asymptotically. Namely, as more and more **data** have been observed, one would have weighed more on the **data** and less on prior, and the eect from the prior would have ultimately been washed out. For pure Bayesians who have dierent prior beliefs, the asymptotic properties make sure that they will eventually agree on similar predictive distributions (Blackwell and Dubins, 1962; Diaconis and Freedman, 1986). For frequentists who perceive that there is an unknown true **data** generating process, the asymptotic properties act as frequentist justication for the Bayesian analysisas the sample size increases, the updated posterior recovers the unknown truth. Moreover, the conditions for posterior consistency provide guidance in choosing better-behaved priors.

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