GMM Estimation with persistent panel data: an application to production functions

Blundell, Richard; Bond, Stephen

doi:10.1080/07474930008800475

Cited by 1,471 publications

(969 citation statements)

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“…Their extended system GMM estimator not only improves the precision but also reduces the finite sample bias. These features of their procedure are confirmed by Blundell and Bond [25] and Blundell et al [26], who conclude that the system GMM estimator can overcome much of the failure to obtain consistent estimates in dynamic panel models.…”

Section: An Overview Of the Autoregressive Models And Dynamic Panel Datasupporting

confidence: 61%

Contribution of Education and Innovation to Productivity among Mexican Regions: A Dynamic Panel Data Analysis

Germán–Soto¹,

Flores²

2015

TEL

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A dynamic panel data (DPD) model is estimated to assess the contribution of the average schooling years, the education expenditure and the inventive coefficient-as an approximation for innovation-to the increased productivity of the Mexican states. The potential difficulties of endogeneity and serial correlation are controlled by adopting system General Method of Moments (GMM) procedures. The findings are compatible with the theory. The importance of the lags is confirmed and the positive and significant impacts on productivity tend to vary according to the income level and the geographical location of the regions. Innovation is an important contributor to northern, central and richer states' productivity, but education expenditure is important for the poorer states and scholarly attainment stands out in the southern states. The analysis emerging from the model concludes that these regional differences should be seen as a potential opportunity for designing customized policies capable of increasing the productivity and not as a weakness.

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Section: An Overview Of the Autoregressive Models And Dynamic Panel Datasupporting

confidence: 61%

Contribution of Education and Innovation to Productivity among Mexican Regions: A Dynamic Panel Data Analysis

Germán–Soto¹,

Flores²

2015

TEL

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“…To avoid the problem of the correlation between y i,t-1 and v i in equation (3) instrumental variables 9 are introduced. This estimator showed better properties than the Arellano and Bond estimator and all other estimators in numerous researches (Blundell & Bond, 1998, 2000Bond, 2002;Bun & Windemeijer, 2007;Soto, 2009).…”

Section: Methodsmentioning

confidence: 99%

Determinants of manufacturing industry exports in European Union member states: a panel data analysis

Sertić

Vučković

Perić

2015

Economic Research-Ekonomska Istraživanja

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This paper aims to provide analysis on the determinants of export performance on the extensive data-set of the 27 European Union member states' total manufacturing and high tech manufacturing industry. Hence, this paper adds to the existing empirical work by specifying an export performance equation not only as a function of income and price, as is traditionally done, but also industrial production and labour cost. For that purpose, dynamic panel data models are estimated by utilising the system GMM estimator for the period from 2000 to 2011. The obtained results indicate that both industrial production and domestic demand have a positive and statistically significant impact on total and high tech manufacturing exports. On the other hand, it is proven that foreign demand also has an impact on total manufacturing exports. Thus, the paper's contribution is reflected in the acknowledgement that a stable macroeconomic environment (contained in the significance of a dummy variable for the economic crisis in both models), boosting production capacity and domestic demand, is essential for better export performance and the competitiveness of the manufacturing industry in an increasingly competitive global economic climate. Finally, from the perspective of policy-making, the paper concludes that recovery in the manufacturing industry could be the much needed push from crisis to economic development.

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“…Lora and Olivera (2009) and Lora (2009) apply a first-difference GMM estimator to analyse the vulnerability of social expenditure to several fiscal variables, using as instruments the lagged levels of the explanatory variables. However, as Blundell and Bond (2000) point out, when the lagged values of the series used as instruments are weakly correlated with the first differences of the endogenous variables, then the first-differenced GMM estimator results are expected to be downward biased in the direction within the groups. This problem is even more severe when series are highly persistent and the time period is small (Yasar et al 2006).…”

Section: Methodsmentioning

confidence: 99%

“…This problem is even more severe when series are highly persistent and the time period is small (Yasar et al 2006). For this reason, Arellano and Bover (1995) and Blundell andBond (1998, 2000) recommend the use of the system GMM estimator, which combines the moment conditions defined for the first-differenced equation with the moment conditions defined for the level equation. It uses the lagged levels of the series as instruments for the first-difference equation and the lagged first-differences of the series as instruments for the level equation.…”

Section: Methodsmentioning

confidence: 99%

Determinants of social spending in Latin America during and after the Washington consensus: a dynamic panel error-correction model analysis

Martín-Mayoral

Sastre

2017

Lat Am Econ Rev

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This paper examines the determinants of social spending in Latin America during the period 1990-2012 and how they differed between the years of the Washington Consensus (1990Consensus ( -2000 and the period that followed (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). Special attention is also paid to the evaluation of convergence towards a common upper-bounded steady state (absolute beta convergence) or to specific steady states conditioned by their country's specific determinants (conditional beta convergence). We estimate a panel error-correction version of an autoregressive distributed lag model to identify the long-term relationships between social expenditure and its determinants. Generalised methods of moments estimators are used to control the endogeneity of the regressors. Results indicate that Latin American social spending follows a conditional beta convergence process over the Washington consensus period that was mainly driven by structural differences in fiscal burdens and external debt, while during the second period it was explained by conjunctural differences in the fiscal burden, GDP per capita and the growth of trade and capital openness.

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GMM Estimation with persistent panel data: an application to production functions

Cited by 1,471 publications

References 7 publications

Contribution of Education and Innovation to Productivity among Mexican Regions: A Dynamic Panel Data Analysis

Contribution of Education and Innovation to Productivity among Mexican Regions: A Dynamic Panel Data Analysis

Determinants of manufacturing industry exports in European Union member states: a panel data analysis

Determinants of social spending in Latin America during and after the Washington consensus: a dynamic panel error-correction model analysis

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