Journal articles on the topic 'Dynamic panel GMM method'
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1
Shina, Arya Fendha Ibnu. "ESTIMASI PARAMETER PADA SISTEM MODEL PERSAMAAN SIMULTAN DATA PANEL DINAMIS DENGAN METODE 2 SLS GMM-AB." MEDIA STATISTIKA 11, no. 2 (December 30, 2018): 79–91. http://dx.doi.org/10.14710/medstat.11.2.79-91.
Abstract:
Single equation models ignore interdependencies or two-way relationships between response variables. The simultaneous equation model accommodates this two-way relationship form. Two Stage Least Square Generalized Methods of Moment Arellano and Bond (2 SLS GMM-AB) is used to estimate the parameters in the simultaneous system model of dynamic panel data if each structural equation is exactly identified or over identified. In the simultaneous equation system model with dynamic panel data, each structural equation and reduced form is a dynamic panel data regression equation. Estimation of structural equations and reduced form using Ordinary Least Square (OLS) resulted biased and inconsistent estimators. Arellano and Bond GMM method (GMM AB) estimator produces unbiased, consistent, and efficient estimators.The purpose of this paper is to explain the steps of 2 SLS GMM-AB method to estimate parameter in simultaneous equation model with dynamic panel data. Keywords:2 SLS GMM-AB, Arellano and Bond estimator, Dynamic Panel Data, Simultaneous Equations
2
Abonazel, Mohamed. "Bias correction methods for dynamic panel data models with fixed effects." International Journal of Applied Mathematical Research 6, no. 2 (May 24, 2017): 58. http://dx.doi.org/10.14419/ijamr.v6i2.7774.
Abstract:
This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.
3
Han, Chirok, Peter C. B. Phillips, and Donggyu Sul. "X-DIFFERENCING AND DYNAMIC PANEL MODEL ESTIMATION." Econometric Theory 30, no. 1 (August 7, 2013): 201–51. http://dx.doi.org/10.1017/s0266466613000170.
Abstract:
This paper introduces a new estimation method for dynamic panel models with fixed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X-differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting “panel fully aggregated” estimator (PFAE) is obtained by pooled least squares on the system of X-differenced equations. The method is simple to implement, consistent for all parameter values, including unit root cases, and has strong asymptotic and finite sample performance characteristics that dominate other procedures, such as bias corrected least squares, generalized method of moments (GMM), and system GMM methods. The asymptotic theory holds as long as the cross section (n) or time series (T) sample size is large, regardless of then/Tratio, which makes the approach appealing for practical work. In the time series AR(1) case (n= 1), the FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the autoregressive coefficient is at or near unity and the same limit distribution as the MLE in the stationary case, so the advantages of the approach continue to hold for fixed and even smalln. Some simulation results are reported, giving comparisons with other dynamic panel estimation methods.
4
Szarek, Joanna, and Jakub Piecuch. "Dynamic panel model in bioeconomy modeling." Agricultural Economics (Zemědělská ekonomika) 68, No. 1 (January 25, 2022): 20–27. http://dx.doi.org/10.17221/156/2021-agricecon.
Abstract:
Currently, technological development is driven by the search for solutions to prevent climate change and environmental degradation, increase energy efficiency, and meet societal needs in relation to avoiding conflict while navigating the implementation of current and future needs. Many of the solutions come from the rapid development of the bioeconomy. The aim of this article is to determine the impact of bioeconomy variables on economic growth in 27 EU countries. The research goal of the paper is based on the estimation of dynamic panel models using the generalized method of moments (GMM). The following set of variables used in the dynamic panel model had a positive impact on economic growth in the EU-27 countries: greenhouse gases by sector: agriculture, circular material use rate, recycling rate of packaging waste by type of packaging – plastic packaging, recycling rate of packaging waste by type of packaging – wooden packaging. Three variables were shown to have a negative impact on economic growth, namely: recycling rate of municipal waste, recycling rate of e-waste, trade-in recyclable raw materials – exports extra-EU.
5
Phillips, Peter C. B. "Dynamic Panel Modeling of Climate Change." Econometrics 8, no. 3 (July 28, 2020): 30. http://dx.doi.org/10.3390/econometrics8030030.
Abstract:
We discuss some conceptual and practical issues that arise from the presence of global energy balance effects on station level adjustment mechanisms in dynamic panel regressions with climate data. The paper provides asymptotic analyses, observational data computations, and Monte Carlo simulations to assess the use of various estimation methodologies, including standard dynamic panel regression and cointegration techniques that have been used in earlier research. The findings reveal massive bias in system GMM estimation of the dynamic panel regression parameters, which arise from fixed effect heterogeneity across individual station level observations. Difference GMM and Within Group (WG) estimation have little bias and WG estimation is recommended for practical implementation of dynamic panel regression with highly disaggregated climate data. Intriguingly, from an econometric perspective and importantly for global policy analysis, it is shown that in this model despite the substantial differences between the estimates of the regression model parameters, estimates of global transient climate sensitivity (of temperature to a doubling of atmospheric CO2) are robust to the estimation method employed and to the specific nature of the trending mechanism in global temperature, radiation, and CO2.
6
Ahmad, Nur Aminah, Georgina M. Tinungki, and Nurtiti Sunusi. "Estimation of Dynamic Panel Data Regression Parameters Using Generalized Methods of Moment." Jurnal Matematika, Statistika dan Komputasi 18, no. 3 (May 15, 2022): 484–91. http://dx.doi.org/10.20956/j.v18i3.20574.
Abstract:
Panel data is a combination of cross section and time series. There are two panel data models, namely static and dynamic panel data. Because seeing the advantages of the dynamic panel data model which is able to overcome endogeneity problems related to the use of the dependent variable lag where in the static panel data model the use of the dependent variable lag causes the estimation results to be biased and inconsistent, so the author examines the dynamic panel data regression model. In the dynamic data model there is a lag of the dependent variable, this variable is correlated with error. Thus, estimation using OLS will result in a biased and inconsistent estimator. To overcome this, the dynamic panel data model can be estimated using the GMM Blundell-Bond approach. Based on the discussion, the parameter estimation formula for dynamic panel data regression using the Blundell-Bond GMM approach is as follows:
7
Soukaina, Khadhraoui, and Sami Hammami. "IMPACT OF BUDGET DEFICIT ON MACROECONOMICS VARIABLES: DATA FROM EUROZONE COUNTRIES (1990-2016)." EUrASEANs: journal on global socio-economic dynamics, no. 3(40) (May 18, 2023): 7–15. http://dx.doi.org/10.35678/2539-5645.3(40).2023.7-15.
Abstract:
This study aims at examining the interactions between budget deficit and macroeconomic variables namely: budget deficit openness, GDP per capita growth, Gross fixed capital formation, and inflation rate. To test this analysis, we have used the generalized method of moments (GMM) system of macroeconomic data from 1990 to 2016 in six countries of the Eurozone such as France, Spain, Portugal, Greece, Ireland, and Cyprus. For this study, static and dynamic panel estimation techniques are used with the help of the OLS, GLS Fixed and Random effect for static panels, and the GMM to estimate our dynamic panel data model, which also considers the lag level of the budget deficit. The GMM panel model results indicate that openness has a significant negative impact on the budget deficit; the coefficient of gross fixed capital formation has a significant and positive impact on the budget deficit. The GDP per capita has a significant negative impact on the budget deficit and the INF has a significant and positive impact on the budget deficit.
8
Gørgens, Tue, and Allan H. Würtz. "Threshold Regression with Endogeneity for Short Panels." Econometrics 7, no. 2 (May 22, 2019): 23. http://dx.doi.org/10.3390/econometrics7020023.
Abstract:
This paper considers the estimation of dynamic threshold regression models with fixed effects using short panel data. We examine a two-step method, where the threshold parameter is estimated nonparametrically at the N-rate and the remaining parameters are estimated by GMM at the N -rate. We provide simulation results that illustrate advantages of the new method in comparison with pure GMM estimation. The simulations also highlight the importance of the choice of instruments in GMM estimation.
9
Tinungki, Georgina Maria, Robiyanto Robiyanto, and Powell Gian Hartono. "The Effect of COVID-19 Pandemic on Corporate Dividend Policy in Indonesia: The Static and Dynamic Panel Data Approaches." Economies 10, no. 1 (January 1, 2022): 11. http://dx.doi.org/10.3390/economies10010011.
Abstract:
This research examines the effect of the crisis due to the COVID-19 pandemic on dividend policy in Indonesia. The purposive sampling method was used to collect data from corporates listed on the IDX from 2014 to 2020 and analyzed using static and dynamic panel data approaches. The fixed-effect models (FEM) were selected for the static panel data regression. Meanwhile, the first difference-generalized method of moments (FD-GMM) and system-generalized method of moments (SYS-GMM) were used for determine the robustness of the estimated dynamic panel data. The results showed that the crisis due to the pandemic led to higher dividend distribution on SYS-GMM. Furthermore, companies maintained the dividend level as a positive signal for investors which lifted the sluggish trade condition in the capital market. Profitability and previous year dividends positively affect dividend policy robustly. Furthermore, the results showed that age affects dividend policy on FD-GMM. Financial leverage has a robust effect, and firm size has an effect on FD-GMM in different directions, while investment opportunity does not affect dividend policy. Statistically, the FEM selected that violates the best linear unbiased estimation was proven to form parameters that were not much different from the estimates produced by the dynamic model, both from the coefficient of influence direction and significance, and the omitted variable bias occurs as evidenced in the robust test with dynamic model was solved. This research is also used as a reference for considering investors’ investment decisions in the new normal condition. Therefore, dividend policy can be considered as a positive signal to investors with the ability to stock trading activities in the capital market.
10
Sthembiso Msomi, Thabiso, and Odunayo Magret Olarewaju. "Dynamic panel investigation of the determinants of South African commercial banks’ operational efficiency." Banks and Bank Systems 17, no. 4 (November 2, 2022): 35–49. http://dx.doi.org/10.21511/bbs.17(4).2022.04.
Abstract:
Like any other business, commercial banks are greatly affected by the micro and macro-environment that operate in, no matter how large they are. Capital adequacy ratio, credit risk, money supply, inflation, the exchange rate, and the national gross domestic product have been noted to be the key determinants of bank operational efficiency. This research study looked at the operational efficiency of four large South African banks, namely, Standard Bank, Absa, Nedbank, and First National Bank. A quantitative, descriptive, correlation design was employed, and the System-Generalized Method of Moments (SYS-GMM) techniques were used and revealed that operational efficiency was positively correlated with capital adequacy ratio, credit risk, inflation, and exchange rate, and negatively correlated with profitability, money supply and GDP. SYS-GMM estimates show that capital adequacy ratio, credit risk, inflation and exchange rate positively influenced operational efficiency, while profitability, money supply (M3) and GDP had a negative influence. Thus, it is concluded that bank management should decrease administrative costs, evaluate customers’ creditworthiness before issuing loans, raise bank size as operational conditions require, boost intermediation, and anticipate inflation to operate more efficiently.
11
Hu, Yi, Dongmei Guo, Ying Deng, and Shouyang Wang. "Estimation of Nonlinear Dynamic Panel Data Models with Individual Effects." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/672610.
Abstract:
This paper suggests a generalized method of moments (GMM) based estimation for dynamic panel data models with individual specific fixed effects and threshold effects simultaneously. We extend Hansen’s (Hansen, 1999) original setup to models including endogenous regressors, specifically, lagged dependent variables. To address the problem of endogeneity of these nonlinear dynamic panel data models, we prove that the orthogonality conditions proposed by Arellano and Bond (1991) are valid. The threshold and slope parameters are estimated by GMM, and asymptotic distribution of the slope parameters is derived. Finite sample performance of the estimation is investigated through Monte Carlo simulations. It shows that the threshold and slope parameter can be estimated accurately and also the finite sample distribution of slope parameters is well approximated by the asymptotic distribution.
12
Han, Chirok, and Peter C. B. Phillips. "GMM ESTIMATION FOR DYNAMIC PANELS WITH FIXED EFFECTS AND STRONG INSTRUMENTS AT UNITY." Econometric Theory 26, no. 1 (June 19, 2009): 119–51. http://dx.doi.org/10.1017/s026646660909063x.
Abstract:
This paper develops new estimation and inference procedures for dynamic panel data models with fixed effects and incidental trends. A simple consistent GMM estimation method is proposed that avoids the weak moment condition problem that is known to affect conventional GMM estimation when the autoregressive coefficient (ρ) is near unity. In both panel and time series cases, the estimator has standard Gaussian asymptotics for all values of ρ ∈ (−1, 1] irrespective of how the composite cross-section and time series sample sizes pass to infinity. Simulations reveal that the estimator has little bias even in very small samples. The approach is applied to panel unit root testing.
13
UTAMI, NI PUTU MEILING, I. WAYAN SUMARJAYA, and I. GUSTI AYU MADE SRINADI. "MEMODELKAN RASIO KETERSEDIAAN BERAS MENGGUNAKAN REGRESI DATA PANEL DINAMIS." E-Jurnal Matematika 8, no. 3 (August 31, 2019): 199. http://dx.doi.org/10.24843/mtk.2019.v08.i03.p253.
Abstract:
The purpose of this research is to model and to determine the significant factor(s) that influence the ratio of rice availability at Province of East Java from 2007 to 2017 by applying dynamic panel data analysis. Independent variables of this research are land productivity, harvest area, and total population. The estimation method used are the first-difference GMM and system GMM. The best model to model the ratio of rice availability at Province of East Java is first-difference GMM and the independent variables which significant influence the ratio of rice availability at Province of East Java from 2007 to 2017 are lag ratio of rice availability, land productivity, harvest area, and total population.
14
Dańska-Borsiak, Barbara. "Zastosowania panelowych modeli dynamicznych w badaniach mikroekonomicznych i makroekonomicznych." Przegląd Statystyczny. Statistical Review 2009, no. 2 (June 30, 2009): 25–41. http://dx.doi.org/10.59139/ps.2009.02.2.
Abstract:
Econometric models based on panel data, in which the presence of unobservable, constant over time, group-specific effects is assumed are called panel data models. The constancy over time of the group effects causes some methodological complications in the case of dynamic models. In this paper the main ideas of the two methods, which are most often used for estimation of dynamic panel data models are presented. The methods are: first-differenced GMM and system GMM. The main goal of this paper is to present some examples of applications of dynamic panel data models in micro and macroeconomic analyses. Special interest is in showing the consequences of using different methods according to the type of data – macro or micro.
15
Allison, Paul D., Richard Williams, and Enrique Moral-Benito. "Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects." Socius: Sociological Research for a Dynamic World 3 (January 1, 2017): 237802311771057. http://dx.doi.org/10.1177/2378023117710578.
Abstract:
Panel data make it possible both to control for unobserved confounders and allow for lagged, reciprocal causation. Trying to do both at the same time, however, leads to serious estimation difficulties. In the econometric literature, these problems have been solved by using lagged instrumental variables together with the generalized method of moments (GMM). Here we show that the same problems can be solved by maximum likelihood (ML) estimation implemented with standard software packages for structural equation modeling (SEM). Monte Carlo simulations show that the ML-SEM method is less biased and more efficient than the GMM method under a wide range of conditions. ML-SEM also makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models.
16
Olaniyi, Clement. "Asymmetric information phenomenon in the link between CEO pay and firm performance." Journal of Economic Studies 46, no. 2 (March 4, 2019): 306–23. http://dx.doi.org/10.1108/jes-11-2017-0319.
Abstract:
PurposeThe purpose of this paper is to examine the asymmetric behavior between CEO pay and firm performance in Nigeria.Design/methodology/approachThe study adopts a two-step dynamic panel generalized method of moments (GMM) to reveal asymmetric responses of CEO pay to positive and negative shocks in firm performance.FindingsThe research outcomes of a two-step dynamic panel GMM) adopted reveal asymmetric responses of CEO pay to positive and negative shocks in firm performance. This implies that CEOs are handsomely compensated for good performance, but not punished for poor performance.Originality/valueThe study, therefore, suggests that CEO pay fails to serve as an internal corporate governance mechanism to alleviate agency problem in Nigeria’s listed firms.
17
Hsiao, Cheng, and Qiankun Zhou. "JIVE FOR PANEL DYNAMIC SIMULTANEOUS EQUATIONS MODELS." Econometric Theory 34, no. 6 (November 2, 2017): 1325–69. http://dx.doi.org/10.1017/s0266466617000421.
Abstract:
We consider the method of moments estimation of a structural equation in a panel dynamic simultaneous equations model under different sample size combinations of cross-sectional dimension, N, and time series dimension, T. Two types of linear transformation to remove the individual-specific effects from the model, first difference and forward orthogonal demeaning, are considered. We show that the Alvarez and Arellano (2003) type GMM estimator under both transformations is consistent only if ${T \over N} \to 0$ as $\left( {N,T} \right) \to \infty $. However, it is asymptotically biased if ${{{T^3}} \over N} \to \kappa \ne 0 < \infty$ as $\left( {N,T} \right) \to \infty $. Since the validity of statistical inference depends critically on whether an estimator is asymptotically unbiased, we suggest a jackknife bias reduction method and derive its limiting distribution. Monte Carlo studies are conducted to demonstrate the importance of using an asymptotically unbiased estimator to obtain valid statistical inference.
18
Oguzoglu, Umut, and Thanasis Stengos. "Can Dynamic Panel Data Explain the Finance-Growth Link? An Empirical Likelihood Approach." Review of Economic Analysis 3, no. 2 (September 30, 2011): 129–48. http://dx.doi.org/10.15353/rea.v3i2.1459.
Abstract:
The short run effect of the financial intermediary development on economic growth is analyzed using an unbalanced panel of 77 countries covering 35 years. Empirical Likelihood (EL) estimation is used and compared to more conventional GMM methods that weight moment conditions equally over the sample. However, if a part of the data is associated with only weak instruments, GMM estimators are subject to considerable small sample bias. EL appropriately re-weights the moment restrictions to deal with that problem. Using EL, we obtain more robust estimates of the effect of financial intermediation on economic growth than GMM.
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Hayakawa, Kazuhiko. "THE ASYMPTOTIC PROPERTIES OF THE SYSTEM GMM ESTIMATOR IN DYNAMIC PANEL DATA MODELS WHEN BOTH N AND T ARE LARGE." Econometric Theory 31, no. 3 (September 15, 2014): 647–67. http://dx.doi.org/10.1017/s0266466614000449.
Abstract:
In this paper, we derive the asymptotic properties of the system generalized method of moments (GMM) estimator in dynamic panel data models with individual and time effects when both N and T, the dimensions of cross-section and time series, are large. Specifically, we show that the two-step system GMM estimator is consistent when a suboptimal weighting matrix where off-diagonal blocks are set to zero is used. Such consistency results theoretically support the use of the system GMM estimator in large N and T contexts even though it was originally developed for large N and small T panels. Simulation results indicate that the large N and large T asymptotic results approximate the finite sample behavior reasonably well unless persistency of data is strong and/or the variance ratio of individual effects to the disturbances is large.
20
Aini, Hanifah Nur, Dwi Ispriyanti, and Suparti Suparti. "ANALISIS REGRESI FAKTOR PANEL DINAMIS BLUNDELL-BOND DENGAN ESTIMASI SYSTEM-GENERALIZED METHOD OF MOMENT PADA SAHAM FARMASI DI BEI." Jurnal Gaussian 11, no. 3 (January 3, 2023): 447–57. http://dx.doi.org/10.14710/j.gauss.11.3.447-457.
Abstract:
The pharmaceutical sector has become a concern during the Covid-19 pandemic because of the large use of drugs. Companies need to improve financial performance to increase their share prices and investors need analysis to predict future stock prices. This study aims to analyze the influence of stock prices on 10 pharmaceutical companies on the Indonesia Stock Exchange during the third quarter of 2020 to the third quarter of 2021. Based on previous research, the factors that are thought to have an effect on changes in stock prices are internal financial ratios (ROA, ROE, NPM, GPM, EPS, PER, BV, PBV, DAR, DER, CR, QR, Cash Asset Ratio) and external inflation, exchange rates, interest rates. The method used in this research is dynamic panel factor regression analysis with GMM (Generalized Method of Moment) estimation. Factor analysis to reduce the independent variables to form a factor score which is then entered into the regression. The regression model was obtained from the comparison of Arellano-Bond GMM and Blundell-Bond System. The GMM system is the development of Arellano-Bond which will produce more efficient estimates when the sample time series is short. The results of the study were obtained 3 factor scores with a total variance of 81.757% from the elimination of 6 variables that had MSA <0.5. The best model is the Blundell-Bond Twostep System which fulfills the model assumptions with RMSE 803.276.
21
Uspri, Betty, Syafruddin Karimi, Indrawari Indrawari, and Endrizal Ridwan. "The effect of inflation on income inequality: Evidence from a non-linear dynamic panel data analysis in indonesia." Decision Science Letters 12, no. 3 (2023): 639–48. http://dx.doi.org/10.5267/j.dsl.2023.4.001.
Abstract:
This research investigates the impact of inflation on income inequality in Indonesia. This study is part of a comprehensive examination investigating which monetary policy can be utilized to lessen inequality. As a central bank objective, inflation can influence the distribution of income, wealth, and endogenous consumption, hence defining inequality. This study employed dynamic panel data analysis for linear autoregressive data using the generalized method of moments (GMM) for both first differences GMM (FD-GMM or AB-GMM) and system GMM (Sys-GMM or BB-GMM) with regional data from 58 cities in 2010-2020. The Arellano-Bond estimator reveals a positive and statistically significant association between inflation and inequality. When inflation rises, the purchasing power of the poor will decline, while the wealthiest will benefit as their non-cash assets proliferate. This study finds, indirectly, that Indonesia’s monetary policy can play a crucial role in lowering income distribution gaps. As one of the nations with an inflation-targeting framework, the Indonesian Central Bank can target the inflation rate by considering inequality. The ITF becomes the most effective monetary policy for stabilizing prices and promoting economic stability. The ITF reduces income inequality by reducing inflation rates. The study also finds that, similar to other emerging nations, economic growth in Indonesia exacerbates inequality. Poverty can be reduced by increased economic growth, but the positive impact of development on the wealthy is significantly more significant than on the poor. Therefore, economic expansion increases inequality.
22
Kruiniger, Hugo. "GMM ESTIMATION AND INFERENCE IN DYNAMIC PANEL DATA MODELS WITH PERSISTENT DATA." Econometric Theory 25, no. 5 (October 2009): 1348–91. http://dx.doi.org/10.1017/s0266466608090531.
Abstract:
In this paper we consider generalized method of moments–based (GMM-based) estimation and inference for the panel AR(1) model when the data are persistent and the time dimension of the panel is fixed. We find that the nature of the weak instruments problem of the Arellano–Bond (Arellano and Bond, 1991,Review of Economic Studies58, 277–297) estimator depends on the distributional properties of the initial observations. Subsequently, we derive local asymptotic approximations to the finite-sample distributions of the Arellano–Bond estimator and the System estimator, respectively, under a variety of distributional assumptions about the initial observations and discuss the implications of the results we obtain for doing inference. We also propose two Lagrange multiplier–type (LM-type) panel unit root tests.
23
Muritala, Adewale T., Adeniyi M. Ijaiya, Ahmed O. Adekunle, Ibraheem K. Nageri, and A. Bolaji Yinus. "Impact of Oil Prices on Stock Market Development in Selected Oil Exporting Sub-Saharan African Countries." Financial Internet Quarterly 16, no. 2 (June 1, 2020): 1–13. http://dx.doi.org/10.2478/fiqf-2020-0008.
Abstract:
Abstract This study examines the dynamic impacts of oil prices on stock market development in four oil exporting sub-Saharan African countries in the period of 1989-2015. The Arbitrage Pricing Theory (APT) is used as the theoretical framework where stock market prices are hypothesized to be fully reflective of all available information. Static panel data (Pooled OLS, panel Fixed Effect Model, panel Random Effect Model) and dynamic panel model of Generalized Method of Moments (GMM) were employed in the estimation. The estimation of the static panel model shows that oil prices, exchange rates, gross domestic product, inflation and the corruption index have a positive and significant impact on stock market development. However, there is a slight improvement from the estimation of the GMM dynamic panel model which confirmed that oil prices, exchange rates, gross domestic product, investment, inflation and the corruption index have a positive and significant impact on stock market development. The study therefore recommends that investors in selected the Sub-Sahara Africa (SSA) stock market need to be cognizant of the varying impacts of macroeconomic indicators, particularly those that have been found to exert strong influence on stock returns like oil prices, exchange rates, inflation and the corruption index.
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Liu, Yue Xi, and Zhen Bo Zhang. "An Application of GMM Method in the Test of Relationship between Urbanization and Circulation Economic Growth." Advanced Materials Research 712-715 (June 2013): 3207–10. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.3207.
Abstract:
To explore the impact of urbanization and economic growth on the development of circulation industry, this paper uses GMM method to estimate dynamic panel data model, based on panel data at provincial-level from 2001 to 2010 in China, after testing the endogeneity of urbanization and economic growth. The findings indicate that regional economic development, labor input and fixed investment has significant positive effect on output of circulation, while lagged output of circulation and level of urbanization has no significant effect on it.
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Gu, Wenbo, and Fangyuan Kang. "Inference of Dynamic Spatial Panel Data Model and Its Application in Carbon Emission Analysis." Journal of Physics: Conference Series 2747, no. 1 (May 1, 2024): 012028. http://dx.doi.org/10.1088/1742-6596/2747/1/012028.
Abstract:
Abstract The Spatial Dynamic Panel Data (SDPD) model is a widely used statistical model in the fields of economics and social sciences and has been the subject of extensive research by many scholars in recent years. Existing methods for parameter estimation primarily focus on improvements to the Generalized Method of Moments (GMM) and Quasi-Maximum Likelihood (QML). In this paper, we employ the method of Empirical Likelihood (EL) for statistical inference of the dynamic spatial panel data model and obtain confidence regions for the parameters. Through numerical simulations, we present the performance of the confidence regions obtained using the Empirical Likelihood (EL) and the Asymptotically Normal (NA) methods under finite samples and compare the two approaches. Finally, we analyze carbon using the suggested model and techniques.
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Tsaurai, Kunofiwa. "Impact of intellectual property rights on foreign direct investment in Africa." Investment Management and Financial Innovations 21, no. 2 (May 21, 2024): 265–75. http://dx.doi.org/10.21511/imfi.21(2).2024.21.
Abstract:
The study investigated the impact of intellectual property rights on foreign direct investment (FDI) in selected African countries (Burkina Faso, Ivory Coast, Nigeria, Cameroon, Mali, Kenya, Burundi, Central African Republic, Rwanda, Senegal, Zimbabwe, and Tanzania). The purpose of the study is to develop property rights policies that encourages FDI in African countries. How FDI is influenced by the combination of trade openness and intellectual property rights was also examined using the same data set and econometric methods such as the dynamic generalized method of moments (GMM), fixed effects, and pooled ordinary least squares (OLS). Panel data ranging from 2005 to 2019 were used for the purposes of this study. A 1% increase in intellectual property rights led to a 22.73% increase in FDI inflows under the dynamic GMM and a 45.55% increase in FDI inflows under the random effects. These results show that intellectual property rights significantly enhanced FDI under the random effects and dynamic GMM. FDI was insignificantly enhanced by intellectual property rights under the pooled OLS and fixed effects methods. A 1% increase in complementarity between intellectual property rights and trade openness (complementarity term) pushed up FDI inflows by 17.78% under the dynamic GMM, whilst a 1% increase in the complementarity term increased FDI inflows by 16.72% under the fixed effects. In other words, dynamic GMM and fixed effects approaches show that the complementarity component significantly improved FDI inflows. The paper recommends implementing the best property rights strategies to improve FDI inflows into African countries. AcknowledgmentThe author appreciates the moral support from the University of South Africa, his employer.
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Fazal, Snober, and Ali Azam. "Moderating Role of Governance in Growth-Technology-Environment Nexus: Evidence from Developing Economies." iRASD Journal of Economics 5, no. 2 (June 30, 2023): 486–508. http://dx.doi.org/10.52131/joe.2023.0502.0141.
Abstract:
Competition among economies to attain maximum economic growth has led to environmental degradation. At the same time, technological innovations are empirically validated for having a role in achieving sustainable development. Good governance could make appropriate measures to design frameworks, develop instruments, and set targets to protect the environment. The current study intends to check the nexus among technological innovations, energy consumption, economic growth, and environmental degradation for a panel of 40 developing countries. The panel data of developing economies for a period of 25 years (from 1996 to 2020) was collected. The relationship was theoretically and econometrically modeled and finally analyzed using the dynamic panel Generalized Method of Moments (GMM). The empirical results of the dynamic panel GMM technique reveal significant and insightful relationships between the variables and their impact on environmental degradation (CO2 emissions). Technological innovations and renewable energy consumption negatively affect CO2 emissions, while economic growth, financial development, and globalization have positive effects. By moderating the growth-technology-environment nexus, governance enhances the positive impact of technological innovations and renewable energy consumption while mitigating the adverse effects of economic growth on CO2 emissions. These findings have important implications for policymakers addressing environmental issues and promoting sustainable development.
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Thrikawala, Sujani, Stuart Locke, and Krishna Reddy. "Dynamic endogeneity and corporate governance-performance relationship." Journal of Economic Studies 44, no. 5 (October 9, 2017): 727–44. http://dx.doi.org/10.1108/jes-12-2015-0220.
Abstract:
Purpose The purpose of this paper is to examine the relationship between corporate governance (CG) and microfinance institution (MFI) performance, using a dynamic panel generalised method of moments (GMM) estimator to mitigate the serious issues with endogeneity. Design/methodology/approach Inconsistent findings and a general lack of empirical results for the microfinance industry leave an unclear message regarding the impacts of CG on MFI performance, especially in emerging economies. The authors use GMM estimation techniques to examine whether CG has an influence on MFI performance. Findings This study confirms that the MFIs’ contemporaneous performance and CG characteristics are statistically significantly positively linked with their past performance. This study finds statistically significant governance effects on MFI performance, including the presence of international directors and/or donor representatives on the board, client representatives on the board, percentage of non-executive directors and the quality of the national governance system. Practical implications These findings provide some insights for policy-makers and practitioners to develop suitable policies and guidelines to streamline MFIs’ operations in emerging countries. Moreover, national and international investors and donors may use these finding as a benchmark for their investment and funding decisions. Originality/value This paper is the first to estimate the CG and performance relationship of MFIs in a dynamic framework by applying the GMM estimation method. This approach improves upon traditional estimation methods by controlling the likely sources of endogeneity. Further, this paper examines whether quality of national-level governance characteristics is related to performance measures of profitability and outreach of MFIs.
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Dang, Rey, L’Hocine Houanti, Nhu-Tuyen Lê, and Jean-Michel Sahut. "Does Board Composition Influence CSR Disclosure? Evidence from Dynamic Panel Analysis." La Responsabilité Sociale de L’entreprise comme système ordonné dans un environnement chaotique 25, no. 2 (May 27, 2021): 52–69. http://dx.doi.org/10.7202/1077784ar.
Abstract:
Given the importance of corporate social responsibility (CSR) and corporate governance, this study examines the association between board composition and CSR disclosure on a sample of S&P 500 firms over the period from 2004 to 2015. Unlike existing studies, we control for potential sources of endogeneity using a system-generalized method of moments (system GMM) estimator. In doing so, we find no evidence that board size, board independence or CEO duality has any significant influence on CSR disclosure. Rather, our results suggest that, when the problem of endogeneity is correctly taken into account, the link between board composition and CSR disclosure is neutral.
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Patin, Jeanne-Claire, Matiur Rahman, and Muhammad Mustafa. "Impact of Total Asset Turnover Ratios on Equity Returns: Dynamic Panel Data Analyses." Journal of Accounting, Business and Management (JABM) 27, no. 1 (May 1, 2020): 19. http://dx.doi.org/10.31966/jabminternational.v27i1.559.
Abstract:
This paper is an empirical exploration of the impact of total asset turnover ratios on stock returns of 1961 US public firms in different types of industries from 2001 to 2015. Stock prices are significantly influenced by operating performance of a company in efficiently utilizing its assets. For that matter, operating efficiency (as measured by total asset turnover ratio) plays a role in portfolio investment decisions. Pedroni’s heterogeneous panel co-integration procedures, associated bivariate error-correction model (ECM), dynamic ordinary least squares (DOLS) and generalized method of moments (GMM) are applied. Both stock returns and total asset turnover ratios in levels are nonstationary with I (1) behavior. Subsequently, both variables are found cointegrated. The panel ECM estimates suggest convergence of variables toward long-run equilibrium at moderate pace with short-run interactive positive feedback effects. Again, both DOLS and GMM estimates reveal short-run contemporaneous positive effects of total asset turnover ratios on stock returns in levels. In view of the findings of this study, firms should strive to improve operating efficiency, among others, to enhance competitiveness and thereby to boost their stock prices for rewarding shareholders.
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Patin, Jeanne-Claire, Matiur Rahman, and Muhammad Mustafa. "Impact of Total Asset Turnover Ratios on Equity Returns: Dynamic Panel Data Analyses." Journal of Accounting, Business and Management (JABM) 27, no. 2 (October 23, 2020): 19. http://dx.doi.org/10.31966/jabminternational.v27i2.689.
Abstract:
This paper is an empirical exploration of the impact of total asset turnover ratios on stock returns of 1961 US public firms in different types of industries from 2001 to 2015. Stock prices are significantly influenced by operating performance of a company in efficiently utilizing its assets. For that matter, operating efficiency (as measured by total asset turnover ratio) plays a role in portfolio investment decisions. Pedroni’s heterogeneous panel co-integration procedures, associated bivariate error-correction model (ECM), dynamic ordinary least squares (DOLS) and generalized method of moments (GMM) are applied. Both stock returns and total asset turnover ratios in levels are nonstationary with I (1) behavior. Subsequently, both variables are found cointegrated. The panel ECM estimates suggest convergence of variables toward long-run equilibrium at moderate pace with short-run interactive positive feedback effects. Again, both DOLS and GMM estimates reveal short-run contemporaneous positive effects of total asset turnover ratios on stock returns in levels. In view of the findings of this study, firms should strive to improve operating efficiency, among others, to enhance competitiveness and thereby to boost their stock prices for rewarding shareholders.
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Salamh, Mustafa, and Liqun Wang. "Second-Order Least Squares Method for Dynamic Panel Data Models with Application." Journal of Risk and Financial Management 14, no. 9 (September 1, 2021): 410. http://dx.doi.org/10.3390/jrfm14090410.
Abstract:
Management of financial risks and sound decision making rely on the accurate information and predictive models. Drawing useful information efficiently from big data with complex structures and building accurate models are therefore crucial tasks. Most commonly used methods for statistical inference in dynamic panel data models are based on the differencing transformation of data. However, differencing data may cause substantial loss of information, and therefore the subsequent analysis may fail to capture important features in the original level data. This point is demonstrated by a real data example where we use a semiparametrically efficient estimation method on the level data to reach a more favorable model. In particular, we study a second-order least squares approach which is based on the first two conditional moments of the response variable given the explanatory variables. This estimator is root-N consistent and its asymptotic variance reaches a lower bound semiparametric efficiency. Monte Carlo simulations show that this estimator performs favorably in finite sample situations compared to the first-differenced GMM and the random effects pseudo ML estimators. We also propose a new diagnostic test to check the working moments assumption based on the proposed estimator. A real data application is presented to further demonstrate the usage of this method.
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Syofya, Heppi. "Modeling Analysis of Economic Growth in Asia with a Dynamic Panel Approach Generalized Method of Moment (GMM)." IJEBD (International Journal of Entrepreneurship and Business Development) 5, no. 4 (July 31, 2022): 716–25. http://dx.doi.org/10.29138/ijebd.v5i4.1900.
Abstract:
Purpose: High growth is expected to overcome various economic problems. Therefore, various efforts have been made by the government in synergy with other related institutions as a form of optimization of economic growth. That is why economic growth is an important indicator for the development of a country. This study aims to analyze economic growth and the factors that influence economic growth in Asia during the 2000-2019 period using dynamic panels.
Design/methodology/approach: The independent variables in this study are the human development index (HDI), changes in exports (PEXPORT) and government spending (EXPENGOV) using the GMM system method as a model for modeling economic growth. The GMM model system is the best model with Sargan testing. The data used in this study are in the scope of Asia with the number of observational studies that are 11 (eleven) with a total panel data turned on by the state observation system 209.
Findings: The results show that the human development index (HDI) has a negative and significant effect on economic growth in Asia 2000-2019 period. Meanwhile, the export change variable (PEXPORT) and government expenditure (EXPENGOV) had a positive and significant effect on economic growth in Asia for the 2000-2019 period.
Research limitations/implications: Furthermore, economic growth also strengthened the previous year's economic growth at a significance level of 1%. This shows that economic growth is economic growth, economy, economy, and state government spending.
Originality/value: This paper is an original
Paper type: Research paper
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Richard, Zogo Ekassi. "Is Inequality Slowing Down Africa’s Industrialization?" Journal of Economics and Public Finance 7, no. 4 (July 10, 2021): p31. http://dx.doi.org/10.22158/jepf.v7n4p31.
Abstract:
Africa has also experienced a decline in the level of industrialization for at least three decades. Examining the dynamics of industrialization, and its effect on inequality, therefore remains a strikingly topical issue. This paper assesses the effects of industrial transformation on inequality in Africa over the period 1980-2016. Using a sample of 48 African countries, we estimate a dynamic panel data model using the Generalized Method of Moments in System (GMM-S). Our results show that strong industrialization would reduce inequality in Africa. The robustness of the results is tested using a PSTR (Panel Smooth Transition Regression) model and a PTR (Panel Transition Regression) model. The study recommends that economic, social and environmental disparities be taken into account in the process of industrial transformation on the continent.
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Tinungki, Georgina Maria, Powell Gian Hartono, Robiyanto Robiyanto, Agus Budi Hartono, Jakaria Jakaria, and Lydia Rosintan Simanjuntak. "The COVID-19 Pandemic Impact on Corporate Dividend Policy of Sustainable and Responsible Investment in Indonesia: Static and Dynamic Panel Data Model Comparison." Sustainability 14, no. 10 (May 18, 2022): 6152. http://dx.doi.org/10.3390/su14106152.
Abstract:
This research investigates the impact of crisis due to the COVID-19 pandemic on the dividend policy of green index companies in Indonesia, namely the Sustainable and Responsible Investment (SRI) by Biodiversity (KEHATI) Foundation, or SRI-KEHATI indexed companies. The purposive sampling technique was used to collect data from companies listed from 2014 to 2020, using static and dynamic panel data models. From the several panel data models tested, the static panel data regression with random effects model (REM) and fixed effect model (FEM) uses the least square dummy variable-robust standard error (LSDV-RSE) technique are the best econometric models feasible. The system generalized method of moments (SYS-GMM) is used as a suitable econometric model with a robustness test used to determine static panel data regression. It is reported that SRI-KEHATI indexed companies tend to distribute dividends positively during this crisis, and is also statistically proven robust. This gives a positive signal to the capital market concerning the sluggish trading activity. The market reaction test, using two-approaches, showed that this business did not provide a positive reaction to the capital market, which turned out to be pessimistic. Furthermore, profitability and financial leverage have a robust effect, while dividends from the previous year affect dividend policy on the static panel data model, and firm size affect dividend policy on SYS-GMM. Predictors that proved influential with a direction not in line with the hypothesis were investment opportunities on REM and SYS-GMM, and firm age on SYS-GMM. The parameter estimation that passes the model specification test is feasible, whiles the biased and inconsistency of parameter estimation due to the alleged correlation between ui,t and PYDi,t failed to occur in static panel data regression. The endogeneity issue was resolved by dynamic panel data regression with the strongest corrective effect. This research can be used as a reference for investors to obtain optimal returns on green index companies in the country. An optimal dividend policy can increase the value of the SRI-KEHATI indexed companies; therefore, it can contribute optimally to sustainability and responsibility for social and environmental aspects.
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Gunawan, Ashari, Fadli Agus Triansyah, Reyna Karlina, Gustina Yusuf, Aulia Rachma Dinantika, and Arif Wahyudi. "Econometric Model of Economic Growth In Indonesia Using Dynamic Panel Data Using the FD-GMM Arellano-Bond and SYS-GMM Blundell-Bond Approaches." Kontigensi : Jurnal Ilmiah Manajemen 11, no. 1 (June 17, 2023): 208–13. http://dx.doi.org/10.56457/jimk.v11i1.344.
Abstract:
Economic growth is measured by changes in a country's Gross Domestic Product (GDP) which can be broken down into population and economic elements. This research was conducted to determine the conditions of economic growth in the country of Indonesia with a total of 34 provinces in the 2016-2021 observation period, a total of 204 samples. The data collection technique was carried out by downloading files on the official website of the Central Bureau of Statistics in Indonesia for 2016-2021, while data analysis was carried out using econometric models by comparing the FD-GMM Arellano-Bond and Sys-GMM Blundell-Bond models, then for the second stage determining which model is the best to use in modeling. Data processing in research using Stata software version 17.0. In panel data, economic variables are dynamic, meaning that the value of a variable can be influenced by the value of another variable and the value of the variable concerned, in the previous period, in addition to knowing the short-term and long-term impacts of economic growth. Based on panel data regression estimation, the best model is obtained. -GMM Blundell-Bond). The results of the study revealed that researchers found the results of data processing using the System Generalized Method of Moment (Sys-GMM Blundell-Bond) and FD-GMM ARELLANO-BOND economic growth in Indonesia is influenced by the human development index, poverty level, agglomeration, with the impact of elasticity on economic growth short term and long term.
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Ambya, Ambya, and Lies Maria Hamzah. "Indonesian Coal Exports: Dynamic Panel Analysis Approach." International Journal of Energy Economics and Policy 12, no. 1 (January 19, 2022): 390–95. http://dx.doi.org/10.32479/ijeep.11978.
Abstract:
Coal is a mineral fuel commodity considered important as a source ofenergy and is traded among countries. Indonesia is one of the largest coal producing countries in the world. This study aimed to analyse the relationship between the net export volume, GDP per capita of destination countries, real exchange rate, and Indonesian coal export prices. The existence of a causal relationship between exports and economic growth shows that there is a relationship between net exports and future economic growth. Economic growth is an increase in people's per capita income without paying attention to changes in the economic structure.The study uses panel data of 5 biggest coal trading partner countries of Indonesia during the period 2015-2019, by using the dynamic panel analysis method, where a dependent variable is not only determined by the value of independent variables at the research period, but is also determined by the value of previous period. The dynamic panel method is characterized by the lag of the dependent variable which is correlated with the residual among the independent variables. The dynamic panel data regression method can be used to determine the short-term effect,and the long-term effect as well.Based on the estimation results of the Generalized Method of Moment (GMM) Arellano Bond, in the study period the exchange rate and export prices had a significant negative effect on the volume of Indonesian coal exports. GDP per capita has no significant effect on the volume of Indonesia's coal exports.Furthermore, the short-term elasticity approach for the exchange rate is -0.029159 and for the long term is 0.3616521. These results indicate that the calculation of the short-term and long-term elasticity of the exchange rate (ER) is inelastic and negative with different magnitudes. In addition, it explains that in the short term an increase in the exchange rate of 1 percent will reduce net exports in the short term by 2.9 percent.
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Voumik, Liton Chandra, Md Jamsedul Islam, and Asif Raihan. "Electricity Production Sources and CO2 Emission in OECD countries: Static and Dynamic Panel Analysis." Global Sustainability Research 1, no. 2 (November 9, 2022): 12–21. http://dx.doi.org/10.56556/gssr.v1i2.327.
Abstract:
Industrialization, urbanization, population growth, and changes in lifestyle have all contributed to a rise in the OECD countries' risk of global warming. The amount of carbon dioxide (CO2) generated from heat and power sources put out is directly related to how much electricity they make. Finding out which sources are bad for the environment, and which are not is the primary motivation behind this study. The impact of different approaches to energy production on carbon dioxide emissions is analyzed using OECD data. The data is analyzed using Quantile Regression (QR), Generalized Method of Moments (GMM), and Pooled Ordinary Least Squares (OLS). The study found that CO2 emissions were significantly impacted in a positive direction when electricity was generated using coal, oil, or gas. The emissions from coal-fired power plants are the most detrimental. The generation of hydroelectricity and other forms of renewable energy can reduce CO2 emissions in all regression models. The most compelling evidence of a correlation between CO2 emissions and energy sources was uncovered in this study. In order to produce credible findings, the paper used both QR and GMM methods. Important implications for environmental policy are drawn from this article's findings. Both are required to lessen our reliance on fossil fuels and promote the development of renewable energy sources like solar, wind, and hydroelectricity.
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Ismanto, Hadi, Silviana Pebruary, and Dewi Nur Maulidiyah. "Macroeconomic policy and profit rate of a company: A dynamic panel estimation and comparative analysis from Indonesia." Investment Management and Financial Innovations 19, no. 1 (March 31, 2022): 322–33. http://dx.doi.org/10.21511/imfi.19(1).2022.25.
Abstract:
Macroeconomic policy (fiscal and monetary) dynamics are interesting to analyze, especially considering corporate performance. This paper aims to determine the effect of macroeconomic policy on the company’s profit rate. Effectiveness of tax revenue (ETAX), realization of tax revenue (RTAX), Bank of Indonesian rate (BIRT), investment growth (INVG), realization of investments (RINV), infrastructure fund allocation rate (INFR), and realization of infrastructure funds (RINF) are macroeconomic policy variables. This study uses a sample of 256 companies listed on the Indonesia Stock Exchange (IDX) in 2005–2019. This paper employs such methods as GMM, using Wald-test and Sargan’s test. GMM estimator result shows that the instrument of infrastructure fund realization policy (RINF), investment growth (INVG), and investment realization (RINV) affect the company’s profit rate (PROF). Therefore, companies need to pay attention to the government development plans, investment growth, and investment realization, which can improve company performance. The result, government’s development for the 2005–2009 and 2015–2019 periods shows a significant difference in companies’ ability to generate profits. AcknowledgmentsWe would like to thank the Department of Management, Faculty of Economics and Business, Universitas Islam Nahdlatul Ulama Jepara (Unisnu), and the Institute of Research and Community Services (LPPM) Unisnu Jepara Indonesia, which has supported this study.
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Okeke, Joseph Uchenna, and Evelyn Nkiruka Okeke. "Least Squares Dummy Variable in Determination of Dynamic Panel Model Parameters." European Journal of Engineering and Technology Research 1, no. 6 (July 27, 2018): 77–81. http://dx.doi.org/10.24018/ejeng.2016.1.6.197.
Abstract:
This paper investigates the small sample performance of the Least Squares Dummy Variable (LSDV) estimator of the dynamic panel data models for period, T, greater than the cross sections, N and its large sample performance in the direction of T as N remains finite, and compares it with the performance of the instrumental variable- generalize method of moments (IV-GMM) estimators using the properties of root mean squares error(RMSE) of the model , root mean squares error of the autoregressive term ? (RMSE?), the bias of ? (bias?) and the Akaike Information Criterion (AIC) with the motive of ascertaining the usefulness of the LSDV estimator in determining the parameters of a dynamic panel model as T? and finite N, for which it is regarded as consistent.
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Sigue, Moussa. "The Determinants of Global Competitiveness of Economy: A Dynamic Panel Approach Applied to the WAEMU Countries." Applied Finance and Accounting 6, no. 2 (August 5, 2020): 16. http://dx.doi.org/10.11114/afa.v6i2.4964.
Abstract:
This paper examines the determinants of the global competitiveness of the WAEMU economy applying a dynamic panel approach from 2011 to 2017. After providing a synthesis of the both theoretical and empirical debates on the subject, we derived a dynamic panel model which estimation was reached using the system generalized method of moments (GMM). The result of the estimation shows that competitiveness lagged by one period, financial development, GDP per capita, internal absorption and foreign trade taxes positively and significantly affect the global competitiveness of WAEMU countries, while economic openness, the inflation rate and the quality of institutions have a negative and significant contribution.
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Wang, Jia. "Do Economic Development Incentives Crowd Out Public Expenditures in U.S. States?" B.E. Journal of Economic Analysis & Policy 16, no. 1 (January 1, 2016): 513–38. http://dx.doi.org/10.1515/bejeap-2015-0042.
Abstract:
Abstract This paper investigates whether economic development incentives (EDI) crowd out public expenditures in U.S. states. Using EDI data from a new database, this paper employs a two-way fixed effect panel framework and generalized method of moments (GMM) approach to account for dynamic features associated with public expenditures. Potential endogeneity of policy variables and problems with unbalanced panels are also addressed. Results show relatively little effect of incentives spending on most public goods expenditures contemporaneously, with negative repercussions beginning to appear in year one. Findings of this paper carry practical importance for policymakers concerning the efficacy of incentives.
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Yuniasih, Aisyah Fitri, Muhammad Firdaus, and Idqan Fahmi. "Disparitas, Konvergensi, dan Determinan Produktivitas Tenaga Kerja Regional di Indonesia." Jurnal Ekonomi dan Pembangunan Indonesia 14, no. 1 (July 1, 2013): 63–81. http://dx.doi.org/10.21002/jepi.v14i1.447.
Abstract:
AbstractIndonesia has been still experiencing regional economic disparity problems, including in labour productivity. This study employs dynamic panel approach to analyze convergence and to identify determinants of regional labour productivity during the period of 1987-2011. The System Generalized Method of Moments (Sys-GMM) estimation results show that regional convergence process occurs with speed of convergence of 0.06518 per year. Physical capital stock, human capital stock, total trade, and real wage give positive impacts. Therefore, government should prioritize in overcoming labour productivity disparity in Eastern Indonesia in which are more unequal than in Western Indonesia where interventions should be greater for provinces with lower labour productivity.Keywords: Disparity, Convergence, Labour Productivity, Dynamic Panel AbstrakIndonesia masih mengalami masalah terkait dengan disparitas perekonomian regional, termasuk dalam hal produktivitas tenaga kerja. Studi ini menggunakan pendekatan panel dinamis untuk menganalisis konvergensi dan mengidentikasi determinan produktivitas tenaga kerja regional selama periode 1987-2011. Model estimasi System Generalized Method of Moments (Sys-GMM) menunjukkan bahwa proses konvergensi regional terjadi dengan kecepatan konvergensi 0,06518 per tahun. Stok modal fisik, stok modal manusia, total perdagangan, dan upah riil ditemukan memberikan pengaruh positif. Pemerintah harus lebih memprioritaskan untuk mengatasi masalah disparitas produktivitas tenaga kerja di Kawasan Timur Indonesia (KTI) yang lebih timpang dibandingkan Kawasan Barat Indonesia (KBI) di mana intervensi harus lebih fokus terhadap provinsi-provinsi dengan tingkat produktivitas tenaga kerja yang lebih rendah.Kata kunci: Disparitas, Konvergensi, Produktivitas Tenaga Kerja, Panel Dinamis
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Hussain, Sarfraz, Asan Ali Golam Hassan, Abdul Quddus, Muhammad Rafiq, and Van Chien Nguyen. "CASH CONVERSION CYCLE SENSITIVITY BY MODERATING ROLE OF EXCHANGE RATES VOLATILITY ON FIRM’S FINANCIAL PERFORMANCE." Business: Theory and Practice 22, no. 2 (September 7, 2021): 277–89. http://dx.doi.org/10.3846/btp.2021.13147.
Abstract:
The cycle of cash conversion relates to the time spread between the value of cash paid for purchases and the cash receipt from turnover. Using the State Bank of Pakistan data, this study introduces the direct and moderating role of the exchange rate, effective through the efficient execution of the cash conversion cycle between Pakistani 302 manufacturing companies from 1999–2015. Using the fixed effect as the static panel model and system GMM as a dynamic panel, it is observed that the exchange rate plays an authoritative moderating role between the cash conversion cycle and the financial performance. Results of the investigation have shown that in static panel analysis with the cash conversion period, the exchange rate has a positive and substantial moderating effect on return on assets and return on equity whereas that ER has a major negative impact on return on assets and return on equity using dynamic panel data analysis GMM. The issue of endogeneity in the static panel is addressed using the advanced approach of the standard error of the panel correction standard error method that changed the position of the significance of the moderator variable. Observers, therefore, intend to evaluate the fluctuations in the exchange rate as one of the variables of the financial output moderator in the context of current metrics such as asset’s returns, equity’s returns and gain more practical expression within their investigated results.
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Mumtaz, Majid, Wisal Ahmad, and Syed Arshad Ali Shah. "Determinants of Corporate Cash Holdings in Hospitality Sector of France, Spain and United States of America." Global Economics Review V, no. III (September 30, 2020): 55–66. http://dx.doi.org/10.31703/ger.2020(v-iii).06.
Abstract:
This study determines the effect of parameters used for cash holding in hospitality sector (HS) of target countries i-e France, Spain and United State of America for the period of 14 years (2005-2018). The parameters consist of firm size, leverage, capital expenditure, growth opportunity, liquidity, cash flow, cash flow volatility, asset intangibility, dividend payments and stock exchange. Dynamic panel data is employed for empirical estimation i-e Generalized Method of Moments (GMM). System GMM model estimation reveals that leverage, cash flow volatility and asset intangibility influence cash holdings positively while size, capital expenditure, growth opportunities and cash flow affect cash holdings negatively.
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Voumik, Liton Chandra, Md Azharul Islam, Samrat Ray, Nora Yusma Mohamed Yusop, and Abdul Rahim Ridzuan. "CO2 Emissions from Renewable and Non-Renewable Electricity Generation Sources in the G7 Countries: Static and Dynamic Panel Assessment." Energies 16, no. 3 (January 17, 2023): 1044. http://dx.doi.org/10.3390/en16031044.
Abstract:
The threat of global warming has increased due to industrialization, urbanization, population expansion, and changes in lifestyle among the Group of Seven(G7) Carbon dioxide emissions (CO2) directly affect how much electricity can be generated from various sources. This research aims to identify environmental hazards associated with various energy sources. Analyzing the impact of various energy sources on CO2 emissions from electricity and heat production using data from the G7. The data is analyzed using quantile regression (QR), generalized method of moments (GMM), random effects (RE), and fixed effects (FE). Our results indicate a substantial positive impact on CO2 emissions regardless of the technology used to generate coal and gas power. Coal-fired power plants have a larger impact on the environment than other sources of emissions. Also, all coal and gas coefficients are significant in FE, RE, GMM, and QR. Oil coefficients have a negative impact on environmental degradation and are significant for FE, RE, and D-GMM regressions. Hydroelectric and renewable energy production can reduce CO2 emissions in all regression models. Nuclear energy has a beneficial impact on the environment, but the coefficients are only significant for S-GMM and the last quantile. However, the most significant result of this study is the identification of a cause-and-effect relationship between CO2 emissions and energy production. Carbon dioxide (CO2) emissions can be lowered by shifting away from fossil fuels and toward renewable and hydroelectric sources. The research also suggests several renewable and alternative electricity production policies for sustainable energy.
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Yesi, Elisandi, Andrian Huruta, and Basukianto Basukianto. "Analyzing determinants of poverty in Central Java with Generalized Method of Moments." Industrija 51, no. 3-4 (2023): 49–71. http://dx.doi.org/10.5937/industrija51-48280.
Abstract:
The purpose of this study is to examine determinants of poverty in regencies/cities in Central Java. This study examined poverty, investment, savings, and infrastructure as the research variables by focusing on the vicious cycle of poverty. The data was obtained from the Central Bureau of Statistics in Central Java. The data was analyzed using the System-Generalized Method of Moments (SYS-GMM) model using a dynamic panel data model. The results show that both investment and infrastructure negatively and significantly impact poverty. However, saving has a positive and low significant impact on poverty. It was interesting to note that the disparity in savings ownership contributes to the high poverty level. These findings contribute to the government's efforts to alleviate poverty in the regencies/cities in Central Java. Our findings also provide valuable insights into poverty dynamics in Indonesia.
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Gamtessa, Samuel. "Technical Efficiency and Technical Change in Canadian Manufacturing Industries." Economics Research International 2014 (December 31, 2014): 1–8. http://dx.doi.org/10.1155/2014/376486.
Abstract:
This study applies the “true fixed effects” panel stochastic frontier methodology to the Canadian KLEMS data set to estimate technical change and technical efficiency in the Canadian manufacturing sector. To account for the endogeneity of capital inputs as well as the possible problems related to omitted variables, a two-stage residual inclusion method is pursued. The first stage is estimated using the dynamic panel GMM method. The results show that Canadian manufacturing industries experienced significant declines in technical efficiencies during the last ten years. This suggests that the observed slowdown in TFP growth during the recent past is partly due to declining technical efficiency.
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Kiha, Emilia Khristina, and Wiwiek Rindayati. "KONVERGENSI HARGA PANGAN POKOK ANTAR WILAYAH DI INDONESIA." JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN 2, no. 1 (February 4, 2018): 30–46. http://dx.doi.org/10.29244/jekp.2.1.2013.30-46.
Abstract:
In Indonesia, the increase in food prices usually results in the rise in the inflation rate. To cope with this problem, a better food distribution among regionsis absolutely required. This study aimed to describe the dynamics of food prices, to test the convergence level of food prices and to analyze the factors that influence the changes in food prices between regions in Indonesia. The data used were obtained from the Central Agency of Statistics and the Ministry of Agriculture from 2002 to 2010. The method used was analysis of dynamic panel data (First Difference-Generalized Methode Moment/FDGMM). The results of the study showed that all commodities of food prices were convergent, sugar at the highest level and rice at the lowest, while the factors that influence changes in food prices were production rate, Gross Domestic Product (GDP) and population. Keywords: Convergence, Food Prices, GMM Panel Data
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Kiha, Emilia Khristina, and Wiwiek Rindayati. "KONVERGENSI HARGA PANGAN POKOK ANTAR WILAYAH DI INDONESIA." JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN 2, no. 1 (February 4, 2018): 30–46. http://dx.doi.org/10.29244/jekp.2.1.30-46.
Abstract:
In Indonesia, the increase in food prices usually results in the rise in the inflation rate. To cope with this problem, a better food distribution among regionsis absolutely required. This study aimed to describe the dynamics of food prices, to test the convergence level of food prices and to analyze the factors that influence the changes in food prices between regions in Indonesia. The data used were obtained from the Central Agency of Statistics and the Ministry of Agriculture from 2002 to 2010. The method used was analysis of dynamic panel data (First Difference-Generalized Methode Moment/FDGMM). The results of the study showed that all commodities of food prices were convergent, sugar at the highest level and rice at the lowest, while the factors that influence changes in food prices were production rate, Gross Domestic Product (GDP) and population. Keywords: Convergence, Food Prices, GMM Panel Data