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Journal articles on the topic 'GMM, Panel Data Models'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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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.

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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

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2

YOUSSEF, AHMED H., AHMED A. EL-SHEIKH, and MOHAMED R. ABONAZEL. "New GMM Estimators for Dynamic Panel Data Models." International Journal of Innovative Research in Science, Engineering and Technology 03, no. 10 (October 15, 2014): 16414–25. http://dx.doi.org/10.15680/ijirset.2014.0310003.

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3

Sarafidis, Vasilis. "Neighbourhood GMM estimation of dynamic panel data models." Computational Statistics & Data Analysis 100 (August 2016): 526–44. http://dx.doi.org/10.1016/j.csda.2015.11.015.

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4

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.

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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.

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5

Taşpınar, Süleyman, Osman Doğan, and Anil K. Bera. "GMM gradient tests for spatial dynamic panel data models." Regional Science and Urban Economics 65 (July 2017): 65–88. http://dx.doi.org/10.1016/j.regsciurbeco.2017.04.008.

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6

Wansbeek, Tom. "GMM estimation in panel data models with measurement error." Journal of Econometrics 104, no. 2 (September 2001): 259–68. http://dx.doi.org/10.1016/s0304-4076(01)00079-3.

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7

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.

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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.

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8

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.

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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.

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9

Ashley, Richard, and Xiaojin Sun. "Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models." Econometrics 4, no. 4 (November 30, 2016): 47. http://dx.doi.org/10.3390/econometrics4040047.

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10

Bond, Stephen, Clive Bowsher, and Frank Windmeijer. "Criterion-based inference for GMM in autoregressive panel data models." Economics Letters 73, no. 3 (December 2001): 379–88. http://dx.doi.org/10.1016/s0165-1765(01)00507-9.

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11

Jin, Fei, Lung-fei Lee, and Jihai Yu. "Sequential and efficient GMM estimation of dynamic short panel data models." Econometric Reviews 40, no. 10 (August 5, 2021): 1007–37. http://dx.doi.org/10.1080/07474938.2021.1889178.

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12

Sato, Yoshihiro, and Måns Söderbom. "GMM estimation of panel data models with time-varying slope coefficients." Applied Economics Letters 24, no. 21 (March 15, 2017): 1511–18. http://dx.doi.org/10.1080/13504851.2017.1302053.

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13

Tran, Kien C., and Efthymios G. Tsionas. "Local GMM Estimation of Semiparametric Panel Data with Smooth Coefficient Models." Econometric Reviews 29, no. 1 (November 11, 2009): 39–61. http://dx.doi.org/10.1080/07474930903327856.

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14

Abonazel, Mohamed R., Mohamed Abdallah, and El-Housainy A. Rady. "On New Two-Step GMM Estimation of the Panel Vector Autoregressive Models with Missing observations." WSEAS TRANSACTIONS ON MATHEMATICS 21 (September 20, 2022): 671–83. http://dx.doi.org/10.37394/23206.2022.21.79.

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Few estimation methods were discussed to handle the missing data problem in the panel data models. However, in the panel vector autoregressive (PVAR) model, there is no estimator to handle this problem. The traditional treatment in the case of incomplete data is to use the generalized method of moment (GMM) estimation based on only available data without imputation of the missing data. Therefore, this paper introduces a new GMM estimation for the PVAR model in case of incomplete data based on the mean imputation. Moreover, we make a Monte Carlo simulation study to study the efficiency of the proposed estimator. We compare between two GMM estimators based on the mean squared error (MSE) and relative bias (RB) criteria. The first is the GMM estimation based on the list-wise (LW) and the second is the GMM estimation using the mean imputation (MI) at multi-missing levels. The results showed that the MI estimator provides more efficiency than the LW estimator.

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15

Kusrini, D. E., Setiawan, B. N. Ruchjana, and H. Kuswanto. "GMM estimation of simultaneous spatial panel data dynamic models with high order models approach." IOP Conference Series: Earth and Environmental Science 243 (April 9, 2019): 012048. http://dx.doi.org/10.1088/1755-1315/243/1/012048.

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16

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.

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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.

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17

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.

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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:

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18

Hayakawa, Kazuhiko. "GMM Estimation of Short Dynamic Panel Data Models with Interactive Fixed Effects." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 42, no. 2 (2012): 109–23. http://dx.doi.org/10.14490/jjss.42.109.

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19

Ahn, Seung Chan, Young Hoon Lee, and Peter Schmidt. "GMM estimation of linear panel data models with time-varying individual effects." Journal of Econometrics 101, no. 2 (April 2001): 219–55. http://dx.doi.org/10.1016/s0304-4076(00)00083-x.

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20

Lee, Lung-fei, and Jihai Yu. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects." Journal of Econometrics 180, no. 2 (June 2014): 174–97. http://dx.doi.org/10.1016/j.jeconom.2014.03.003.

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21

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.

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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.

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22

Halkos, George E. "Environmental Kuznets Curve for sulfur: evidence using GMM estimation and random coefficient panel data models." Environment and Development Economics 8, no. 4 (September 17, 2003): 581–601. http://dx.doi.org/10.1017/s1355770x0300317.

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The purpose of this study is to test empirically the hypothesis of the inverted U-shaped relationship between environmental damage from sulfur emissions and economic growth as expressed by GDP. Using a large database of panel data consisting of 73 OECD and non-OECD countries for 31 years (1960–1990) we apply for the first time random coefficients and Arellano-Bond Generalized Method of Moments (A–B GMM) econometric methods. Our findings indicate that the EKC hypothesis is not rejected in the case of the A–B GMM. On the other hand there is no support for an EKC in the case of using a random coefficients model. Our turning points range from $2805–$6230/c. These results are completely different compared to the results derived using the same database and fixed and random effects models.

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23

Montalvo, Jose G. "GMM Estimation of Count-Panel-Data Models with Fixed Effects and Predetermined Instruments." Journal of Business & Economic Statistics 15, no. 1 (January 1997): 82. http://dx.doi.org/10.2307/1392077.

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24

Montalvo, Jose G. "GMM Estimation of Count-Panel-Data Models With Fixed Effects and Predetermined Instruments." Journal of Business & Economic Statistics 15, no. 1 (January 1997): 82–89. http://dx.doi.org/10.1080/07350015.1997.10524690.

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25

Hayakawa, Kazuhiko. "Alternative over-identifying restriction test in the GMM estimation of panel data models." Econometrics and Statistics 10 (April 2019): 71–95. http://dx.doi.org/10.1016/j.ecosta.2018.06.002.

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26

Sumaira and Robeena Bibi. "Banking sector development and Economic growth in south Asian countries: Dynamic Panel data analysis." Journal of Environmental Science and Economics 1, no. 1 (February 16, 2022): 52–57. http://dx.doi.org/10.56556/jescae.v1i1.10.

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This study explores the contribution of banking sector development to economic growth for a sample of four south Asian countries namely, Sri Lanka, Bangladesh, Pakistan and India. The study employed Fixed Effect (FE), Difference GMM and System GMM models to the data set for the period of 1980 -2017. The findings of the study indicates that bank based financial development index constructed of private sector credit, board money(M2) and domestic credit provided by banks affect economic growth significantly and positively almost in all models. The result approves that bank based financial development contribute to economic growth and augment growth level in the sample countries. The findings conclude that bank based financial development is important in boosting economic growth and suggests sampled countries of this study government’s channels and regulatory authority on further improvement on banking system in order to achieve higher economic growth.

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27

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.

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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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28

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.

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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.

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29

Kiviet, Jan, Milan Pleus, and Rutger Poldermans. "Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models." Econometrics 5, no. 1 (March 20, 2017): 14. http://dx.doi.org/10.3390/econometrics5010014.

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30

Olajide, Johnson T., and Olusanya E. Olubusoye. "Estimating Dynamic Panel Data Models with Random Individual Effect: Instrumental Variable and GMM Approach." Journal of Statistics Applications & Probability 5, no. 1 (March 1, 2016): 79–87. http://dx.doi.org/10.18576/jsap/050107.

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31

Bun, Maurice J. G., and Frank Windmeijer. "The weak instrument problem of the system GMM estimator in dynamic panel data models." Econometrics Journal 13, no. 1 (February 1, 2010): 95–126. http://dx.doi.org/10.1111/j.1368-423x.2009.00299.x.

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32

Li, Rui, Alan T. K. Wan, and Jinhong You. "Semiparametric GMM estimation and variable selection in dynamic panel data models with fixed effects." Computational Statistics & Data Analysis 100 (August 2016): 401–23. http://dx.doi.org/10.1016/j.csda.2016.02.001.

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33

Hayakawa, Kazuhiko. "Small sample bias properties of the system GMM estimator in dynamic panel data models." Economics Letters 95, no. 1 (April 2007): 32–38. http://dx.doi.org/10.1016/j.econlet.2006.09.011.

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34

Harris, Mark N., and László Mátyás. "A Comparative Analysis of Different IV and GMM Estimators of Dynamic Panel Data Models." International Statistical Review 72, no. 3 (January 15, 2007): 397–408. http://dx.doi.org/10.1111/j.1751-5823.2004.tb00244.x.

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35

Arnold, Matthias, and Dominik Wied. "Improved GMM estimation of random effects panel data models with spatially correlated error components." Papers in Regional Science 93, no. 1 (February 27, 2013): 77–99. http://dx.doi.org/10.1111/j.1435-5957.2012.00472.x.

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36

Hayakawa, Kazuhiko. "Identification problem of GMM estimators for short panel data models with interactive fixed effects." Economics Letters 139 (February 2016): 22–26. http://dx.doi.org/10.1016/j.econlet.2015.12.012.

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37

Do Kim, Du, Huyen Do Phuong, and Tam Le Xuan. "Diversifying Business Models and Bank Stability." European Journal of Business and Management Research 7, no. 3 (June 20, 2022): 299–303. http://dx.doi.org/10.24018/ejbmr.2022.7.3.1447.

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Research on banking sustainability plays an important role for banks in the growing financial market. As a result, banks must compete more, making the risk of weak banks increase. This study was conducted to evaluate the impact of business models on bank stability — analytical data on joint-stock commercial banks listed on the Vietnam stock exchange from 2012 to 2019. The panel data regression analysis model with a generalized method of moments (GMM) is used to analyze the results. The GMM results show that net interest income (NII) has a positive effect on bank stability. On the other hand, non-interest income (NNII and TRADE) has a negative impact on bank stability. This result indicates that business models are having a negative impact on bank stability. The study also points out several implications for improving the sustainability of banks.

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38

Dekiawan, Hermada. "KONVERGENSI PENERIMAAN DAN PENGELUARAN PEMERINTAH PROVINSI DI INDONESIA: PENDEKATAN DATA PANEL DINAMIS SPASIAL." Buletin Ekonomi Moneter dan Perbankan 17, no. 1 (December 22, 2014): 99–128. http://dx.doi.org/10.21098/bemp.v17i1.52.

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The study analyse sigma and beta convergence of provincial government revenues and expenditures in Indonesia (APBD) by using panel data 30 provinces over the period 2000-2012. The variables used in this study consists of real income per capita and the revenues and expenditures variables in the APBD. The study also included a spatial weights matrix to analyse dependency among provinces. Spatial weights matrix used consists of two types of weights, the real income per capita and the geographical distance. Testing for the presence of spatial dependency performed using Moran’s I. The model used in this study are spatial autoregressive model (SAM) and the spatial error model (SEM). The models are estimated using panel least squares, fixed effects, random effects, as well as both GMM first difference (GMM-DIFF) and system GMM (GMM-SYS). Based on sigma convergence approach, the results of the study showed that during the period of 2000-2012 occurred convergence on total revenue, income, tax, fund balance, total spending, employee spending, and goods spending, but not for the real income per capita. Estimation with beta convergence approach conducted on four variables as each sample (in per capita value), namely: total income, tax, total spending, and goods spending. Estimation with beta convergence is done by using additional explanatory variables which include: economic growth, the degree of openness, as well as economic growth. Based on the beta convergence approach, the study results indicate the occurrence of convergence on total income, tax, total spending, and goods spending. Estimates also lead to the conclusion that there are spatial dependencies between provinces either use distance and income per capita weight. Keywords: Sigma and Beta convergence, spatial weights, panel data, revenue and expenditure, the provincial budget (APBD)JEL Classification: H7,R1

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39

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.

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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.

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40

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.

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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.

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41

Wang, Xiaohui. "A Provincial Panel Data Analysis on Export Effect of China’s Inbound Tourism." International Journal of Business and Management 12, no. 6 (May 18, 2017): 196. http://dx.doi.org/10.5539/ijbm.v12n6p196.

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Based on China’s provincial panel data of the export, GDP, trade distance and inbound tourism income from 1997 to 2014, this study examines the export effect of inbound tourism in China, using dynamic panel data models and system GMM estimators. The empirical results for the estimations of the gravity equation demonstrate that inbound tourism promoted China's export through expanding the degree of opening to the outside world, shortening the cultural distance, "advertising effect" and "Marco Polo effect" of business tourism. Inbound tourism has a significant positive export effect, export will increase by 0.135 % when inbound tourism increases by 1%.

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42

Chatti, Walid, and Haitham Khoj. "Service Export Sensitivity to Internet Adoption: Evidence From Dynamic Panel Data Analysis." Research in World Economy 11, no. 6 (October 8, 2020): 259. http://dx.doi.org/10.5430/rwe.v11n6p259.

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This study aims to examine the causal linkages relating service exports to internet penetration for 116 countries over the period 2000-2017. Taking into account a wide panel of countries, we apply 2-Step GMM methodology for dynamic panel data models. The results show a bi-directional causality relating service exports to internet adoption for developed countries. For the global panel and developing countries, we find those same results attest a positive relationship between the internet adoption and service exports, but in the opposite way; the impact is very low and not significant. Regarding developing countries, despite the fact that internet positively affects service exports, it is considered less efficient than in developed countries.

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43

Račić, Željko, Dajana Ercegovac, and Dragana Milić. "The impact of macroeconomic indicators on the business performance of financial institutions in the Republic of Serbia: Panel data analysis." International Journal of Economic Practice and Policy 18, no. 1 (2021): 33–47. http://dx.doi.org/10.5937/skolbiz1-33232.

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This paper aims to estimate the impact of macroeconomic indicators (gross domestic product-GDP, inflation rate and industrial production index) on liquidity, profitability and solvency of financial institutions in the Republic of Serbia. The research is based on applying a dynamic GMM panel model, while the results of the application of static panel models were analyzed as the control results. The research results support the assumption that the growth of GDP and inflation rates affects the increase of financial institutions' profitability. Also, the estimation results implicate that the growth of GDP and the inflation rate is linked with the reduction of financial institutions' liquidity, while the growth of industrial production rate affects its increase. Finally, the results of the study indicate that GDP growth has an influence on the rise of financial sector solvency. This comparative analysis using panel data models is relevant to a broad range of researchers and policymakers interested in macroeconomic relations and the financial sector.

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44

BEKAR, Mustafa, Aslıhan TÜFEKCİ, Yeliz YALÇIN, and Furkan EMİRMAHMUTOGLU. "Panel Data Analysis of Telecommunications and Economic Growth." Ekonomi, Politika & Finans Araştırmaları Dergisi 7, no. 2 (June 30, 2022): 366–85. http://dx.doi.org/10.30784/epfad.1039016.

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Bu çalışmada, telekomünikasyon sektörüne ilişkin yatırımlar, dış ticaret hacmi ve tüketim verileri dikkate alınarak telekomünikasyon sektörünün ekonomik büyüme üzerindeki önemi incelenmiştir. “Telekomünikasyon yatırımları” olarak telekomünikasyon ağı ve servislerini sağlayan kuruluşların maddi ve maddi olmayan varlıkların edinimi veya mevcutların yenilenmesi için yapmış oldukları yatırımlar, “telekomünikasyon dış ticaretine konu emtia ve hizmetler” için Dünya Ticaret Örgütü’nün sınıflandırması, “telekomünikasyon tüketimi” olarak ise mobil hat abonelik sayısı, sabit hat abonelik sayısı ve internet kullanıcı sayısı verileri dikkate alınmıştır. Telekomünikasyon ve ekonomik büyüme ilişkisinin belirlenmesi için farklı bölgelerde ve farklı gelir düzeyindeki 44 ülke için 2000 – 2015 dönemine ait yıllık verileri ile dinamik panel veri modeli kullanılmıştır. Sistem GMM tahmin sonucunda elde edilen bulgular, ekonomik büyüme ile telekomünikasyon sektörüne yapılan yatırımlar, telekomünikasyon sektörüne ait dış ticaret hacmi ve telekomünikasyon tüketimi arasında literatür ile uyumlu olarak pozitif ve istatistiksel olarak anlamlı bir ilişkinin mevcut olduğunu göstermiştir. Ayrıca sonuçlar, ülkelerin ekonomik büyümesinin, telekomünikasyon ürünlerinin dış ticaret işlemleri tarafından doğrudan teşvik edildiğini de ortaya koymaktadır.

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45

Vojinović, Željko, Sunčica Milutinović, Dario Sertić, and Bojan Leković. "Determinants of Sustainable Profitability of the Serbian Insurance Industry: Panel Data Investigation." Sustainability 14, no. 9 (April 25, 2022): 5190. http://dx.doi.org/10.3390/su14095190.

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This paper aims to investigate the main drivers of sustainable profitability trends in the Serbian insurance industry over the years 2008–2019 (inclusive). Our study is motivated by the fact that insurance companies contribute to economic growth, and thus it is essential to understand the factors that contribute to their financial strength and stability. We use a set of standard panel regression models, including the mixed-effects model, followed by a more robust GMM estimation to uncover the linkage between selected micro-specific, macroeconomic, and institutional factors, and return of assets (ROA) and return on total premiums (ROTP). The present paper constitutes a significant contribution to the existing literature on the account of its comprehensiveness both in terms of the institutional datasets that we use, and in terms of the methodologies we apply (in particular, mixed effects and the generalized method of moments (GMM)). The estimated parameters are model-specific, and we find that firm size, GDP, the population growth rates, political stability, and the degree of specialization (in some empirical models) all lead to higher profitability. On the other hand, we observe that excessive risk-taking and inflation (in some specifications) are inversely related to profitability. Finally, we note that regulatory quality, average wage, and life expectancies are found to be not statistically significant. Accordingly, we argue that a profitability-centric managerial strategy should be based on expanded market share and stringent risk management protocols. At the macro level, we conclude that pro-growth and pro-population policies, combined with a well-oiled institutional setting that ensures political stability, constitute the best possible prescription for strong operational performance and profit sustainability in the Serbian insurance industry.

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46

Vojinović, Željko, Sunčica Milutinović, Dario Sertić, and Bojan Leković. "Determinants of Sustainable Profitability of the Serbian Insurance Industry: Panel Data Investigation." Sustainability 14, no. 9 (April 25, 2022): 5190. http://dx.doi.org/10.3390/su14095190.

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Abstract:

This paper aims to investigate the main drivers of sustainable profitability trends in the Serbian insurance industry over the years 2008–2019 (inclusive). Our study is motivated by the fact that insurance companies contribute to economic growth, and thus it is essential to understand the factors that contribute to their financial strength and stability. We use a set of standard panel regression models, including the mixed-effects model, followed by a more robust GMM estimation to uncover the linkage between selected micro-specific, macroeconomic, and institutional factors, and return of assets (ROA) and return on total premiums (ROTP). The present paper constitutes a significant contribution to the existing literature on the account of its comprehensiveness both in terms of the institutional datasets that we use, and in terms of the methodologies we apply (in particular, mixed effects and the generalized method of moments (GMM)). The estimated parameters are model-specific, and we find that firm size, GDP, the population growth rates, political stability, and the degree of specialization (in some empirical models) all lead to higher profitability. On the other hand, we observe that excessive risk-taking and inflation (in some specifications) are inversely related to profitability. Finally, we note that regulatory quality, average wage, and life expectancies are found to be not statistically significant. Accordingly, we argue that a profitability-centric managerial strategy should be based on expanded market share and stringent risk management protocols. At the macro level, we conclude that pro-growth and pro-population policies, combined with a well-oiled institutional setting that ensures political stability, constitute the best possible prescription for strong operational performance and profit sustainability in the Serbian insurance industry.

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47

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.

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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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48

Paul, Sujan Chandra, Nusrat Jahan, Md Mehedi Hassan, and Tandra Mondal. "Nexus Between FDI and Production Indices: Evidence from Asian Countries." Asian Journal of Empirical Research 11, no. 4 (October 15, 2021): 33–40. http://dx.doi.org/10.18488/journal.1007.2021.114.33.40.

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This study aims to explore the effect of foreign direct investment on different types of production indices and some other variables. For this, panel data of 46 countries from Asia was accumulated for the time frame of 1991 to 2018. This paper employed the OLS, POLS, 2SLS, and GMM models. The study reveals that there is a favorable association between foreign direct investment and food production index and fertilizer consumption in all the models used in the study. Livestock production index has significant positive association with foreign direct investment in POLS and GMM models. Crop yield has major positive association with regards to foreign direct investment in all mentioned models except GMM. Land under cereal production has significant positive association in respect of foreign direct investment in OLS and 2SLS models. Crop production index has significant mixed association with foreign direct investment in different models. In POLS model, crop production index and foreign direct investment has significant inverse relationship and in GMM model, crop production index and foreign direct investment has significant positive correlation. Finally, permanent cropland has significant negative relationship with regards to foreign direct investment derived from OLS and 2SLS models.

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Mollick, Andre Varella, Rene Ramos-Duran, and Esteban Silva-Ochoa. "Infrastructure and FDI Inflows into Mexico: A Panel Data Approach." Global Economy Journal 6, no. 1 (February 13, 2006): 1850078. http://dx.doi.org/10.2202/1524-5861.1094.

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In December 1993, restrictions to foreign ownership across major Mexican economic sectors were abolished. This paper studies output, industrialization intensity, "international infrastructure", and government expenditures on infrastructure as determinants of FDI inflows into Mexican states over 1994-2001. We conduct a "general to specific" estimation strategy across Mexican states. Telephone lines appear to be very important to FDI as their coefficients are around 2.0 in Random Effects Models. Industrialization is also important, with coefficients varying from 0.62 to 0.67. Allowing for endogeneity between FDI and real output, dynamic GMM panels confirm the robust effects of telephone lines on FDI. International infrastructure thus appears more conducive to FDI than domestic infrastructure, such as interstate and secondary roads. With international infrastructure being a major catalyst of FDI inflows into Mexico, we provide support to ongoing conventional wisdom promoting such type of investment.

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50

Vieira, Flávio Vilela, and Ronald MacDonald. "A panel data investigation of real exchange rate misalignment and growth." Estudos Econômicos (São Paulo) 42, no. 3 (September 2012): 433–56. http://dx.doi.org/10.1590/s0101-41612012000300001.

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The paper investigates the role of real exchange rate misalignment on long-run growth for a set of ninety countries using time series data from 1980 to 2004. We first estimate a panel data model (fixed and random effects) for the real exchange rate in order to produce estimates of the equilibrium real exchange rate and this is then used to construct measures of real exchange rate misalignment. We provide an alternative set of estimates of RER misalignment using panel cointegration methods. The results for the two-step System GMM panel growth models indicate that the coefficients for real exchange rate misalignment are positive for different model specification and samples, which means that a more depreciated (appreciated) real exchange rate helps (harms) long-run growth. The estimated coefficients are higher for developing and emerging countries.

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