Relevant bibliographies by topics / GMM, Panel Data Models

Academic literature on the topic 'GMM, Panel Data Models'

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Published: 10 December 2022
Last updated: 28 January 2023

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

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "GMM, Panel Data Models"

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Cantarinha, Ana Isabel Guerra. "Comparação de estimadores alternativos para modelos dinâmicos com dados de painel." Master's thesis, Universidade de Évora, 2006. http://hdl.handle.net/10174/16338.

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Esta dissertação tem por objeto de estudo métodos de estimação para modelos dinâmicos com dados de painel. Estes modelos são usualmente estimados pelo método dos momentos generalizados (GMM), sendo o principal objetivo desta dissertação a análise do desempenho de algumas variantes desse método em pequenas amostras, de modo a verificar se as suas propriedades assimptóticas conhecidas são de alguma forma indicadoras das suas propriedades em amostras finitas. Assim, através dum estudo de simulação de Monte Carlo, examinou-se o comportamento desses estimadores em amostras finitas em vários cenários alternativos, que passam: por considerar o caso homoscedástico e heteroscedástico para a componente do termo do erro variante no tempo; por gerar esta componente do erro de acordo com as distribuições Normal, t-Student e Qui-Quadrado; por considerar diferentes valores para a dimensão da amostra tanto em termos seccionais como temporais; por considerar diferentes pesos de cada componente do erro na variância da variável dependente; e por considerar diferentes valores para o parâmetro auto-regressivo. De entre os estimadores GMM, os estimadores SYS revelam um comportamento muito melhor, mostrando-se claramente preferíveis aos DIF para valores de δ` Próximos de um, e evidenciando uma certa robustez face aos vários cenários analisados. Em particular, a Versão proposta por Windmeijer (2000) parece ser a mais indicada para trabalho empírico. /ABSTRACT - In this dissertation we studied estimation methods for dynamic models for panel data. These models are usually estimated by the generalized method of moments (GMM), being the main goal of this dissertation the analysis of the small sample properties of the main variants of that method. Thus, through a Monte Carlo simulation study, the behaviour of those estimators was examined in finite samples in several alternative sceneries, including: homoscedastic and heteroscedastic time-variant error terms; error terms generated according to the Normal, t-Student and chi-square distributions; different cross-sectional and time-series sample sizes; different weights of each error component in the variance of the dependent variable; different values for the autoregressive parameter. The best behaviour was displayed by the variant SYS, which is clearly preferable to the variant DIF for values of the auto-regressive parameter close to the unity and seems to be robust to the several sceneries analyzed. Among the alternative SYS estimators, the version proposed by Windmeijer (2000) appears to be the most suitable for empiric Work.
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Hu, Wanhong. "Estimation of dynamic heterogeneous panel data models." Connect to resource, 1996. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1266934002.

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Bada, Oualid [Verfasser]. "Essays on Large Panel Data Models / Oualid Bada." Bonn : Universitäts- und Landesbibliothek Bonn, 2015. http://d-nb.info/1077266820/34.

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Mutl, Jan. "Dynamic panel data models with spatially correlated disturbances." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3729.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Economics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Bun, Maurice Josephus Gerardus. "Accurate statistical analysis in dynamic panel data models." [Amsterdam : Amsterdam : Thela Thesis] ; Universiteit van Amsterdam [Host], 2001. http://dare.uva.nl/document/57690.

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Sarafidis, Vasilis. "Estimating panel data models with cross-sectional dependence." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613908.

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Khatoon, Rabeya. "Estimation and inference of microeconometric models based on moment condition models." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/estimation-and-inference-of-microeconometric-models-based-on-moment-condition-models(fb572e1e-7238-4410-8e27-052b4a438962).html.

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The existing estimation techniques for grouped data models can be analyzed as a class of estimators of instrumental variable-Generalized Method of Moments (GMM) type with the matrix of group indicators being the set of instruments. Econometric literature (e.g. Smith, 1997; Newey and Smith, 2004) show that, in some cases of empirical relevance, GMM can have shortcomings in terms of the large sample behaviour of the estimator being different from the finite sample properties. Generalized Empirical Likelihood (GEL) estimators are developed that are not sensitive to the nature and number of instruments and possess improved finite sample properties compared to GMM estimators. In this thesis, with the assumption that the data vector is iid within a group, but inid across groups, we developed GEL estimators for grouped data model having population moment conditions of zero mean of errors in each group. First order asymptotic analysis of the estimators show that they are √N consistent (N being the sample size) and normally distributed. The thesis explores second order bias properties that demonstrate sources of bias and differences between choices of GEL estimators. Specifically, the second order bias depends on the third moments of the group errors and correlation among the group errors and explanatory variables. With symmetric errors and no endogeneity all three estimators Empirical Likelihood (EL), Exponential Tilting (ET) and Continuous Updating Estimator (CUE) yield unbiased estimators. A detailed simulation exercise is performed to test comparative performance of the EL, ET and their bias corrected estimators to the standard 2SLS/GMM estimators. Simulation results reveal that while, with a few strong instruments, we can simply use 2SLS/GMM estimators, in case of many and/or weak instruments, increased degree of endogeneity, or varied signal to noise ratio, bias corrected EL, ET estimators dominate in terms of both least bias and accurate coverage proportions of asymptotic confidence intervals even for a considerably large sample. The thesis includes a case where there are within group dependent data, to assess the consequences of a key assumption being violated, namely the within-group iid assumption. Theoretical analysis and simulation results show that ignoring this feature can result in misleading inference. The proposed estimators are used to estimate the returns to an additional year of schooling in the UK using Labour Force Survey data over 1997-2009. Pooling the 13 years data yields roughly the same estimate of 11.27% return for British-born men aged 25-50 using any of the estimation techniques. In contrast using 2009 LFS data only, for a relatively small sample and many weak instruments, the return to first degree holder men is 13.88% using EL bias corrected estimator, where 2SLS estimator yields an estimate of 6.8%.
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Müller, Werner, and Michaela Nettekoven. "A Panel Data Analysis: Research & Development Spillover." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/620/1/document.pdf.

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Panel data analysis has become an important tool in applied econometrics and the respective statistical techniques are well described in several recent textbooks. However, for an analyst using these methods there remains the task of choosing a reasonable model for the behavior of the panel data. Of special importance is the choice between so-called fixed and random coefficient models. This choice can have a crucial effect on the interpretation of the analyzed phenomenon, which is demonstrated by an application on research and development spillover. (author's abstract)
Series: Forschungsberichte / Institut für Statistik
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Shi, Wei. "Essays on Spatial Panel Data Models with Common Factors." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461300292.

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Papa, Gianluca. "Essays on econometrics of panel data and treatment models." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209408.

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In this thesis, I apply the sophisticated tools made available by the econometrics of panel data and treatment models to a range of different issues. In the first Chapter, an ECM model is used to test on the existence of financing constraints in firms’ investment and R&D, taken a proxy for the efficiency of market institutions and governance rules in different countries. In the second chapter we test an agency model linking pay-performance contracts of CEOS to the financial situation of a firm by using a UK panel data. In the third chapter I use a sophisticated treatment model to evaluate the effectiveness of Italian public subsidies to R&D. Finally, in the fourth chapter I try to evaluate the efficiency of Italian regional systems of public healthcare by controlling for socio-economic factors and quality of healthcare in a composite model using panel data estimation and efficient frontier techniques.

The first Chapter analyzes the investment behavior of a sample of R&D intensive firms which are quoted on the stock market from USA, UK and Japan for the period 1990-1998. By using an error correction model we test the elasticity of investment and R&D to cash flow in these countries to see by which measure different market institutions and corporate governance rules affects the cost of external financing. Contrary to previous studies, we find significant differences in the sensitivity to cash flow of the two types of investment, with R&D expenditure being much less sensitive than ordinary investment. This is not surprising given the more long-term nature of R&D expenditures. For what concerns the comparison between the different systems/countries, the USA stock markets confirms as the most efficient market providing outside financing at a much lower cost compared to other markets, especially for young, smaller firms.

The second Chapter is a joint work with Biagio Speciale. It uses the data on a panel of quoted UK firms over the period 1995–2002 to study the effects of financial leverage on managerial compensation. The change in the investors’ expectations that caused the recent collapse of the stock market tech bubble is a perfect example of natural experiment that has been used as a source of plausibly exogenous variation in the firm’s debt. The estimates show that pay-for-performance sensitivity is increasing in financial leverage, with the exception of the 10% most levered firms, giving rise at the end to a non-linear (inverted U-shape) relationship between the two variables. The chapter includes also a theoretical model accounting for this relationship where an higher leverage increases both the expected returns and the expected variance of investment returns: the first effect (determining increased pay-performance sensitivity) prevails for low leverage values and the second effect (determining decreased pay-performance sensitivity) prevails for high leverage values.

The third Chapter undertakes an empirical estimation of the additionality of public funding on both the propensity to initiate R&D activity and the intensity of R&D spending of Italian enterprises for the period 1998-2000, using data from the Third Community Innovation Survey and from firms' financial accounts. The chosen methodology (Endogenous Switching Type II-Tobit) takes into account the possibility that decisions about both starting an R&D activity (sample selection effect) and applying for/obtaining public funding (essential heterogeneity) are influenced by private knowledge of enterprises' idiosyncratic propensities in R&D spending. The present analysis shows that both these effects are indeed important and that they contribute to explain most of the additionality found with less sophisticated models.

The fourth Chapter investigates the underlying causes of variability of public health expenditure per capita (SSPC henceforth) between Italian regions. A fixed-effect panel data estimate on the SSPC (for the period 1997-2006) is used in the first part of the paper to account for regional differences in terms of physical, demographic, socio-economic characteristics and in terms of other variables that affect demand and supply of health services. In the second part, we take the ‘adjusted’ SSPC and proceed to estimate an "efficient production function" of the quality of health services through Data Envelopment Analysis. This procedure allows us to separate the share of expenditure used for the improvement of the quality from the one that can be traced only to an inefficient use of financial resources. A comparison of regional SSPC after factoring out the socio-economic factors and the quality of healthcare shows that big differences still remain and are even exacerbated, signalling big pockets of inefficiency and correspondingly a huge potential for cost savings. Finally, a preliminary analysis shows a positive correlation between the efficiency of regional public spending in healthcare and the level of social capital.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

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Books on the topic "GMM, Panel Data Models"

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Xiao, Zheng. Random coefficient panel data models. Bonn, Germany: IZA, 2004.

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Massetti, Emanuele. Estimating Ricardian models with panel data. Cambridge, MA: National Bureau of Economic Research, 2011.

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Ncube, Mthuli. Modelling implied volatility with OLS and panel data models. London: London School of Economics, Financial Markets Group, 1994.

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Wooldridge, Jeffrey M. Distribution-free estimation of some nonlinear panel data models. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1990.

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Anderson, Gordon. Alternative error covariance assumptions in dynamic panel data models. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1988.

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Wooldridge, Jeffrey M. Multiplicative panel data models without the strict exogeneity assumption. Cambridge, Mass: Massachusetts Institute of Technology, 1991.

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Panel data econometrics: Methods-of-moments and limited dependent variables. San Diego: Academic Press, 2002.

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Hensher, David A. Issues in the pre-analysis of panel data. 's-Gravenhage: Ministerie van Verkeer en Waterstaat, Projectbureau Integrale Verkeers- en Vervoerstudies, 1985.

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Kahn, Barbara E. Measuring variety-seeking and reinforcement behaviors using panel data. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1986.

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Kahn, Barbara E. Measuring variety-seeking and reinforcement behaviors using panel data. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1985.

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Book chapters on the topic "GMM, Panel Data Models"

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Windmeijer, Frank. "GMM for Panel Data Count Models." In Advanced Studies in Theoretical and Applied Econometrics, 603–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75892-1_18.

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Calzolari, Giorgio, and Laura Magazzini. "Improving GMM Efficiency in Dynamic Models for Panel Data with Mean Stationarity." In Studies in Classification, Data Analysis, and Knowledge Organization, 201–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25147-5_13.

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Windmeijer, Frank. "Efficiency Comparisons for a System GMM Estimator in Dynamic Panel Data Models." In Innovations in Multivariate Statistical Analysis, 175–84. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4603-0_11.

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Dhrymes, Phoebus J. "Panel Data Models." In Mathematics for Econometrics, 335–49. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8145-4_11.

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Elhorst, J. Paul. "Spatial Panel Data Models." In SpringerBriefs in Regional Science, 37–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40340-8_3.

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Elhorst, J. Paul. "Spatial Panel Data Models." In Handbook of Applied Spatial Analysis, 377–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03647-7_19.

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Kyriazidou, Ekaterini. "Nonlinear Panel Data Models." In The New Palgrave Dictionary of Economics, 1–11. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2094-1.

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Gujarati, Damodar. "Panel Data Regression Models." In Econometrics, 326–43. London: Macmillan Education UK, 2015. http://dx.doi.org/10.1007/978-1-137-37502-5_17.

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Kyriazidou, Ekaterini. "Nonlinear Panel Data Models." In Microeconometrics, 154–68. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280816_19.

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Asteriou, Dimitrios, and Stephen G. Hall. "Traditional Panel Data Models." In Applied Econometrics, 441–56. London: Macmillan Education UK, 2016. http://dx.doi.org/10.1057/978-1-137-41547-9_21.

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Conference papers on the topic "GMM, Panel Data Models"

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Wang, Jiye, and Minnan Wang. "An Improved PVAR-GMM Model Based on Inter-Provincial Panel Data." In 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2021. http://dx.doi.org/10.1109/tocs53301.2021.9688733.

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Ming, Deng. "GMM estimation of fixed efficient spatial panel data model with error components." In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN). IEEE, 2011. http://dx.doi.org/10.1109/iccsn.2011.6014608.

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Solibakke, Per Bjarte, Torbjorn Arethun, and Ove Oklevik. "Determinants for European energy markets intra-day volatility using dynamic panel data models and GMM-type estimators." In 2010 7th International Conference on the European Energy Market (EEM 2010). IEEE, 2010. http://dx.doi.org/10.1109/eem.2010.5558748.

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Zhang, Zhengyu, Shuming Bao, and Pingfang Zhu. "GMM and 2SLS estimation of panel data models with spatially lagged dependent variables and spatially correlated error components." In Geoinformatics 2007, edited by Jingming Chen and Yingxia Pu. SPIE, 2007. http://dx.doi.org/10.1117/12.761375.

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Szarowská, Irena. "Impact of public R&D expenditure on economic growth in selected EU countries." In Business and Management 2016. VGTU Technika, 2016. http://dx.doi.org/10.3846/bm.2016.16.

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The aim of the paper is to investigate influence of research and development (R&D) expenditure on economic growth in 20 selected EU member states in the period 1995-2013, time span is also divided into a pre-crisis and a post-crisis period. Basic source of data is Eurostat database.The research is based on a dynamic panel regression model (GMM) and estimations are based on Arellan-Bond estimator (1991). Results confirm positive and statistically significant impact of government R&D expenditure, which is the main driver for economic growth during the analysed period. Importance and positive impact of higher education R&D expenditure increases in the post-crisis period. Contrary, business expenditure is found to be insignificant. Traditional growth variables (a higher share of qualified human resources and a higher intensity of investment) report positive effect, although investment only partly.
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Alanzi, Eman, Nada Kulen, and Thu Huong Nguyen. "MODELLING FACTORS AFFECTING RELIGIOUS TOURISM FLOWS TO SAUDI ARABIA." In GLOBAL TOURISM CONFERENCE 2021. PENERBIT UMT, 2021. http://dx.doi.org/10.46754/gtc.2021.11.024.

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Religious tourism demand is one of the major contributors to Saudi Arabia economy and considered to play an important role in the “Vision 2030”, which seeks to diversify Saudi Arabia’s economy reliance on oil revenues. As the country has undergone structural changes in international tourism and removed travel restrictions in the past few years, there is a need to identify the determinant factors that influence international tourists to plan and manage their trips. Therefore, this current study aims to investigate the effects of economic and noneconomic factors on international tourist flows by using A panel data gravity model for the period 2000-2019. The empirical evidence is based on the Generalized Method of Moments (GMM) and the Panel Regression technique. The findings of the regression show that the traditional gravity variables are important to explain Saudi Arabia’s religious tourism demand. The study also has found that habit persistence, the Pandemic Index, GDP per capita of Saudi and the original countries, human rights and investments in the tourist sector have a significant and positive impact on religious tourism demand. While political risks, transport costs, and tourism price have a statistically significant and negative effect on religious tourists’ arrivals. This study will contribute largely to the tourism demand literature by introducing country characteristics factors which include human rights issues as security proxies, pandemics, and quality of life and by measuring the impact of these variables in tourism demand in the context of an oil-based economy that under the transition to a diversified economy with a new vision. The findings of this study may assist in the development of Saudi Arabia’s tourism sector and economic development by providing knowledge to policymakers, investors, and other tourism stakeholders.
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"Panel Discussion DFM 2011." In 2011 First Workshop on Data-Flow Execution Models for Extreme Scale Computing (DFM). IEEE, 2011. http://dx.doi.org/10.1109/dfm.2011.7.

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Vasilescu, Denisa. "INCOME INEQUALITIES IN THE EU COUNTRIES BASED ON PANEL DATA MODELS." In SGEM 2014 Scientific SubConference on POLITICAL SCIENCES, LAW, FINANCE, ECONOMICS AND TOURISM. Stef92 Technology, 2014. http://dx.doi.org/10.5593/sgemsocial2014/b24/s7.044.

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Bingham, Derek, C. Shane Reese, and Brian Williams. "Panel discussion: Integrating data from multiple simulation models of different fidelity." In 2011 Winter Simulation Conference - (WSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/wsc.2011.6147867.

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Long, Zhihe, and BianLing Ou. "Bootstrap LM-lag test for spatial dependence in panel data models." In 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/iemss-17.2017.138.

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Reports on the topic "GMM, Panel Data Models"

1

Massetti, Emanuele, and Robert Mendelsohn. Estimating Ricardian Models With Panel Data. Cambridge, MA: National Bureau of Economic Research, June 2011. http://dx.doi.org/10.3386/w17101.

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Liu, Laura, Hyungsik Roger Moon, and Frank Schorfheide. Forecasting with Dynamic Panel Data Models. Cambridge, MA: National Bureau of Economic Research, September 2018. http://dx.doi.org/10.3386/w25102.

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Athey, Susan, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and Khashayar Khosravi. Matrix Completion Methods for Causal Panel Data Models. Cambridge, MA: National Bureau of Economic Research, October 2018. http://dx.doi.org/10.3386/w25132.

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Shiu, Ji-Liang, and Oliver Linton. Semiparametric nonlinear panel data models with measurement error. The IFS, January 2018. http://dx.doi.org/10.1920/wp.cem.2018.0918.

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Weidner, Martin, Ivan Fernandez-Val, and Mingli Chen. Nonlinear factor models for network and panel data. The IFS, April 2019. http://dx.doi.org/10.1920/wp.cem.2019.1819.

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Arkhangelsky, Dmitry, and Guido Imbens. Double-Robust Identification for Causal Panel Data Models. Cambridge, MA: National Bureau of Economic Research, January 2021. http://dx.doi.org/10.3386/w28364.

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Weidner, Martin, Ivan Fernandez-Val, and Mingli Chen. Nonlinear factor models for network and panel data. The IFS, July 2018. http://dx.doi.org/10.1920/wp.cem.2018.3818.

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Altonji, Joseph, and Rosa Matzkin. Panel Data Estimators for Nonseparable Models with Endogenous Regressors. Cambridge, MA: National Bureau of Economic Research, March 2001. http://dx.doi.org/10.3386/t0267.

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Arellano, Manuel, and Stéphane Bonhomme. Identifying distributional characteristics in random coefficients panel data models. Institute for Fiscal Studies, August 2009. http://dx.doi.org/10.1920/wp.cem.2009.2209.

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Bonhomme, Stéphane, and Manuel Arellano. Nonlinear panel data methods for dynamic heterogeneous agent models. The IFS, November 2016. http://dx.doi.org/10.1920/wp.cem.2016.5116.

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