Dissertations / Theses: '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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Loudermilk, Margaret Susan. "Estimation and testing in dynamic, nonlinear panel data models." Diss., Connect to online resource - MSU authorized users, 2006.

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Evaldsson, Matilda. "Has EMU Led to Higher Debt Levels? : -A Dynamic Panel Data Estimation." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-120396.

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Europe is in the midst of its deepest crisis since the 1930s where unsustainable debt-to-GDP levels are among the most alarming issues. It is so critical that it is unsure if the Euro can be saved. The risk of moral hazard increases within EMU when governments are taking too much risk in their public debt policies due to the anticipation that ECB or other Member States would eventually bail them out. Moreover, the SGP imposes restrictions on government deficits and debts but have previously failed to enforce them. The weakness seen in the past is that no sanctions have been put in place once the limits have been breached and the SGP is therefore incredible. Previous research on common pool and debt spillovers in a monetary union point to an upward drift of public debt as countries join the EMU. Does this argument hold true? In order to find out, 25 OECD countries between the years of 1995 and 2010 are analyzed using System GMM Arellano-Bover/Blundell-Bond one-step estimator. The primary balance, the interest payments, and GDP growth are regressed respectively in order to see through what channel EMU displays its effect. One regression will cover the entire time period and another will only cover the years from 1995 to 2007 in order to isolate the effects of the current crisis. The results, based on the years over the entire time period (including the crisis) suggest that the effect of an EMU Membership goes via the Interest payments which it is connected to positively. By using the equation of debt dynamics, the fact that net debt interest payments are higher for a country within EMU indicates, all else equal, that they have on average higher levels of debt. Nevertheless, this realization might be a crisis phenomenon and the implication of this is not clear. However more importantly, the regressions based on the years of 1995 and 2007 (prior to the crisis) did not display any significant results. These results indicate that there is no significant relationship between a country’s membership in EMU and its level of debt prior to the crisis.

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Ciccarelli, Matteo. "Bayesian interference in heterogeneous dynamic panel data models: three essays." Doctoral thesis, Universitat Pompeu Fabra, 2001. http://hdl.handle.net/10803/31792.

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The task of this work is to discuss issues conceming the specification, estimation, inference and forecasting in multivariate dynamic heterogeneous panel data models from a Bayesian perspective. Three essays linked by a few conraion ideas compose the work. Multivariate dynamic models (mainly VARs) based on micro or macro panel data sets have become increasingly popular in macroeconomics, especially to study the transmission of real and monetary shocks across economies. This great use of the panel VAR approach is largely justified by the fact that it allows the docimientation of the dynamic impact of shocks on key macroeconomic variables in a framework that simultaneously considers shocks emanating from the global enviromnent (world interest rate, terms of trade, common monetary shock) and those of domestic origin (supply shocks, fiscal and monetary policy, etc.). Despite this empirical interest, the theory for panel VAR is somewhat underdeveloped. The aim of the thesis is to shed more light on the possible applications of the Bayesian framework in discussing estimation, inference, and forecasting using multivariate dynamic models where, beside the time series dimensión we can also use the information contained in the cross sectional dimensión. The Bayesian point of view provides a natural environment for the models dlscussed in this work, due to its flexibility in combining diíferent sources of information. Moreover, it has been recently shown that Bayes estimates of hierachical dynamic panel data models have a reduced small sample bias, and help in improving the forecasting performance of these models.

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Winther, Blindum Steen. "Strict exogeneity and nonlinear panel data models with unobserved heterogeneity /." Copenhagen, 2005. http://www.gbv.de/dms/zbw/50660389X.pdf.

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Arellano, Gomez Manuel. "Estimation and testing of dynamic econometric models from panel data." Thesis, London School of Economics and Political Science (University of London), 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261293.

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Lee, Joonhwan, and Iván Fernández-Val. "Panel data models with nonadditive unobserved heterogeneity : estimation and inference." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87526.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Economics, 2014.
"February 2014." Abstract page contains the following information: "This paper is based in part on the second chapter of Fernández-Val (2005)'s MIT PhD dissertation." -- Authors: "Iván Fernández-Val and Joonhwan Lee." Cataloged from PDF version of thesis.
Includes bibliographical references (pages 25-27 (first group)).
This paper considers fixed effects estimation and inference in linear and nonlinear panel data models with random coefficients and endogenous regressors. The quantities of interest - means, variances, and other moments of the random coefficients - are estimated by cross sectional sample moments of GMM estimators applied separately to the time series of each individual. To deal with the incidental parameter problem introduced by the noise of the within-individual estimators in short panels, we develop bias corrections. These corrections are based on higher-order asymptotic expansions of the GMM estimators and produce improved point and interval estimates in moderately long panels. Under asymptotic sequences where the cross sectional and time series dimensions of the panel pass to infinity at the same rate, the uncorrected estimator has an asymptotic bias of the same order as the asymptotic variance. The bias corrections remove the bias without increasing variance. An empirical example on cigarette demand based on Becker, Grossman and Murphy (1994) shows significant heterogeneity in the price effect across U.S. states.
by Joonhwan Lee.
S.M.

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Zhang, Miao. "The comparison of stochastic frontier analysis with panel data models." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/9643.

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From the idea of efficiency raised by Koopmans in 1951, and the panel data first introduced into the efficiency analysis by Pitt and Lee (1981) and Schmidt and Sickles (1984), the techniques of stochastic frontier analysis are fast developed and the applications of stochastic frontier are widely used in different areas, such as education, industry and hospital. But most researchers focus on only one aspect, either the development of new models or empirical applications. This thesis attempts to fill the gap to get a general idea of the properties of different panel data stochastic frontier models, on both statistical aspects and economic aspects, by the comparison of different models applied to different production applications. The thesis is also attempt to shed light on whether particular panel data stochastic frontier models are better suited to different data sets. The models selected capture the simplest situation, with no heterogeneity or heteroscedasticity, and complicated ones, with exogenous variables included in the models. Not only the classical models, such as the Pitt and Lee (1981) and Battese and Coelli (1992.1995), but also the new developed models, such as the latent class model and fixed management model are detected in the thesis. On the economic aspect, the data selected captures both microeconomic and macroeconomic, with the application to the World GDP and the Italian manufacturing industry. The results show that: the panel data stochastic frontier models perform better on the microeconomic level than on the macroeconomic level; the classical models perform better than the new developed ones; some panel data stochastic frontier models make ideal assumptions but the requirements to the dataset are hard to achieve; that the influence from the exogenous variables is quite strong.

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Muller, Christoffel Joseph Brand. "Bayesian approaches of Markov models embedded in unbalanced panel data." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71910.

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Thesis (PhD)--Stellenbosch University, 2012.
ENGLISH ABSTRACT: Multi-state models are used in this dissertation to model panel data, also known as longitudinal or cross-sectional time-series data. These are data sets which include units that are observed across two or more points in time. These models have been used extensively in medical studies where the disease states of patients are recorded over time. A theoretical overview of the current multi-state Markov models when applied to panel data is presented and based on this theory, a simulation procedure is developed to generate panel data sets for given Markov models. Through the use of this procedure a simulation study is undertaken to investigate the properties of the standard likelihood approach when fitting Markov models and then to assess its shortcomings. One of the main shortcomings highlighted by the simulation study, is the unstable estimates obtained by the standard likelihood models, especially when fitted to small data sets. A Bayesian approach is introduced to develop multi-state models that can overcome these unstable estimates by incorporating prior knowledge into the modelling process. Two Bayesian techniques are developed and presented, and their properties are assessed through the use of extensive simulation studies. Firstly, Bayesian multi-state models are developed by specifying prior distributions for the transition rates, constructing a likelihood using standard Markov theory and then obtaining the posterior distributions of the transition rates. A selected few priors are used in these models. Secondly, Bayesian multi-state imputation techniques are presented that make use of suitable prior information to impute missing observations in the panel data sets. Once imputed, standard likelihood-based Markov models are fitted to the imputed data sets to estimate the transition rates. Two different Bayesian imputation techniques are presented. The first approach makes use of the Dirichlet distribution and imputes the unknown states at all time points with missing observations. The second approach uses a Dirichlet process to estimate the time at which a transition occurred between two known observations and then a state is imputed at that estimated transition time. The simulation studies show that these Bayesian methods resulted in more stable results, even when small samples are available.
AFRIKAANSE OPSOMMING: Meerstadium-modelle word in hierdie verhandeling gebruik om paneeldata, ook bekend as longitudinale of deursnee tydreeksdata, te modelleer. Hierdie is datastelle wat eenhede insluit wat oor twee of meer punte in tyd waargeneem word. Hierdie tipe modelle word dikwels in mediese studies gebruik indien verskillende stadiums van ’n siekte oor tyd waargeneem word. ’n Teoretiese oorsig van die huidige meerstadium Markov-modelle toegepas op paneeldata word gegee. Gebaseer op hierdie teorie word ’n simulasieprosedure ontwikkel om paneeldatastelle te simuleer vir gegewe Markov-modelle. Hierdie prosedure word dan gebruik in ’n simulasiestudie om die eienskappe van die standaard aanneemlikheidsbenadering tot die pas vanMarkov modelle te ondersoek en dan enige tekortkominge hieruit te beoordeel. Een van die hoof tekortkominge wat uitgewys word deur die simulasiestudie, is die onstabiele beramings wat verkry word indien dit gepas word op veral klein datastelle. ’n Bayes-benadering tot die modellering van meerstadiumpaneeldata word ontwikkel omhierdie onstabiliteit te oorkom deur a priori-inligting in die modelleringsproses te inkorporeer. Twee Bayes-tegnieke word ontwikkel en aangebied, en hulle eienskappe word ondersoek deur ’n omvattende simulasiestudie. Eerstens word Bayes-meerstadium-modelle ontwikkel deur a priori-verdelings vir die oorgangskoerse te spesifiseer en dan die aanneemlikheidsfunksie te konstrueer deur van standaard Markov-teorie gebruik te maak en die a posteriori-verdelings van die oorgangskoerse te bepaal. ’n Gekose aantal a priori-verdelings word gebruik in hierdie modelle. Tweedens word Bayesmeerstadium invul tegnieke voorgestel wat gebruik maak van a priori-inligting om ontbrekende waardes in die paneeldatastelle in te vul of te imputeer. Nadat die waardes ge-imputeer is, word standaard Markov-modelle gepas op die ge-imputeerde datastel om die oorgangskoerse te beraam. Twee verskillende Bayes-meerstadium imputasie tegnieke word bespreek. Die eerste tegniek maak gebruik van ’n Dirichletverdeling om die ontbrekende stadium te imputeer by alle tydspunte met ’n ontbrekende waarneming. Die tweede benadering gebruik ’n Dirichlet-proses om die oorgangstyd tussen twee waarnemings te beraam en dan die ontbrekende stadium te imputeer op daardie beraamde oorgangstyd. Die simulasiestudies toon dat die Bayes-metodes resultate oplewer wat meer stabiel is, selfs wanneer klein datastelle beskikbaar is.

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Zhang, Yonghui. "Three essays on large panel data models with cross-sectional dependence." Thesis, Singapore Management University (Singapore), 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3601351.

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My dissertation consists of three essays which contribute new theoretical results to large panel data models with cross-sectional dependence. These essays try to answer or partially answer some prominent questions such as how to detect the presence of cross-sectional dependence and how to capture the latent structure of cross-sectional dependence and estimate parameters efficiently by removing its effects.

Chapter 2 introduces a nonparametric test for cross-sectional contemporaneous dependence in large dimensional panel data models based on the squared distance between the pair-wise joint density and the product of the marginals. The test can be applied to either raw observable data or residuals from local polynomial time series regressions for each individual to estimate the joint and marginal probability density functions of the error terms. In either case, we establish the asymptotic normality of our test statistic under the null hypothesis by permitting both the cross section dimension n and the time series dimension T to pass to infinity simultaneously and relying upon the Hoeffding decomposition of a two-fold U-statistic. We also establish the consistency of our test. A small set of Monte Carlo simulations is conducted to evaluate the finite sample performance of our test and compare it with that of Pesaran (2004) and Chen, Gao, and Li (2009).

Chapter 3 analyzes nonparametric dynamic panel data models with interactive fixed effects, where the predetermined regressors enter the models nonparametrically and the common factors enter the models linearly but with individual specific factor loadings. We consider the issues of estimation and specification testing when both the cross-sectional dimension N and the time dimension T are large. We propose sieve estimation for the nonparametric function by extending Bai's (2009) principal component analysis (PCA) to our nonparametric framework. Following Moon and Weidner's (2010, 2012) asymptotic expansion of the Gaussian quasilog-likelihood function, we derive the convergence rate for the sieve estimator and establish its asymptotic normality. The sources of asymptotic biases are discussed and a consistent bias-corrected estimator is provided. We also propose a consistent specification test for the linearity of the nonparametric functional form by comparing the linear and sieve estimators. We establish the asymptotic distributions of the test statistic under both the null hypothesis and a sequence of Pitman local alternatives.

To improve the finite sample performance of the test, we also propose a bootstrap procedure to obtain the bootstrap p-values and justify its validity. Monte Carlo simulations are conducted to investigate the finite sample performance of our estimator and test. We apply our model to an economic growth data set to study the relationship between capital accumulation and real GDP growth rate.

Chapter 4 proposes a nonparametric test for common trends in semiparametric panel data models with fixed effects based on a measure of nonparametric goodness-of-fit (R²). We first estimate the model under the null hypothesis of common trends by the method of profile least squares, and obtain the augmented residual which consistently estimates the sum of the fixed effect and the disturbance under the null.

Then we run a local linear regression of the augmented residuals on a time trend and calculate the nonparametric R² for each cross section unit. The proposed test statistic is obtained by averaging all cross sectional nonparametric R²'s, which is close to 0 under the null and deviates from 0 under the alternative. We show that after appropriate standardization the test statistic is asymptotically normally distributed under both the null hypothesis and a sequence of Pitman local alternatives. We prove test consistency and propose a bootstrap procedure to obtain p-values. Monte Carlo simulations indicate that the test performs well infinite samples. Empirical applications are conducted exploring the commonality of spatial trends in UK climate change data and idiosyncratic trends in OECD real GDP growth data. Both applications reveal the fragility of the widely adopted common trends assumption.

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Fernández-Val, Iván. "Three essays on nonlinear panel data models and quantile regression analysis." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32408.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2005.
Includes bibliographical references.
This dissertation is a collection of three independent essays in theoretical and applied econometrics, organized in the form of three chapters. In the first two chapters, I investigate the properties of parametric and semiparametric fixed effects estimators for nonlinear panel data models. The first chapter focuses on fixed effects maximum likelihood estimators for binary choice models, such as probit, logit, and linear probability model. These models are widely used in economics to analyze decisions such as labor force participation, union membership, migration, purchase of durable goods, marital status, or fertility. The second chapter looks at generalized method of moments estimation in panel data models with individual-specific parameters. An important example of these models is a random coefficients linear model with endogenous regressors. The third chapter (co-authored with Joshua Angrist and Victor Chernozhukov) studies the interpretation of quantile regression estimators when the linear model for the underlying conditional quantile function is possibly misspecified.
by Iván Fernández-Val.
Ph.D.

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Iwakura, Haruo. "Asymptotic Efficiency of Estimates for Panel Data Models with Fixed Effect." Kyoto University, 2014. http://hdl.handle.net/2433/188444.

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Humphreys, Keith. "Latent variable models for discrete longitudinal data with measurement error." Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295045.

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Kleiber, Christian, and Achim Zeileis. "The Grunfeld Data at 50." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/464/1/document.pdf.

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This paper revisits Grunfeld's well-known investment data, one of the most widely used data sets in all of econometrics, on the occasion of their 50th anniversary. It presents, apparently for the first time after the publication of the original Chicago Ph.D. thesis, the full data set and points out errors and inconsistencies in several currently available versions. It also revisits a number of empirical studies from the literature of the last five decades.
Series: Research Report Series / Department of Statistics and Mathematics

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Lo, Chi-ho, and 盧子豪. "Estimation of structural parameters for panel data in credibility context." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31557442.

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Kazemi, Iraj. "The initial conditions problem in dynamic panel data models with random effects." Thesis, Lancaster University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431463.

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Abrevaya, Jason. "Semiparametric estimation methods for nonlinear panel data models and mismeasured dependent variables." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10822.

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Hoonhout, P. J. M. "Identification and estimation of panel data models with attrition using refreshment samples." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1318085/.

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This thesis deals with attrition in panel data. The problem associated with attrition is that it can lead to estimation results that suffer from selection bias. This can be avoided by using attrition models that are sufficiently unrestrictive to allow for a wide range of potential selection. In chapter 2, I propose the Sequential Additively Nonignorable (SAN) attrition model. This model combines an Additive Nonignorability assumption with the Sequential Attrition assumption, to just-identify the joint population distribution in Panel data with any number of waves. The identification requires the availability of refreshment samples. Just-identification means that the SAN model has no testable implications. In other words, less restrictive identified models do not exist. To estimate SAN models, I propose a weighted Generalized Method of Moments estimator, and derive its repeated sampling behaviour in large samples. This estimator is applied to the Dutch Transportation Panel and the English Longitudinal Study of Ageing. In chapter 4, a likelihood-based alternative estimation approach is proposed, by means of an EM algorithm. Maximum Likelihood estimates can be useful if it is hard to obtain an explicit expression for the score function implied by the likelihood. In that case, the weighted GMM approach is not applicable.

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Afonso, Lutcy Menezes. "Correcting for attrition in panel data using inverse probability weighting : an application to the european bank system." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/8155.

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Mestrado em Econometria Aplicada e Previsão
Esta dissertação analiza técnicas de correção do efeito do enviesamento que pode ocorrer no caso dos dados utilizados apresentarem valores em falta. Tais técnicas serão aplicadas a um modelo económico para caracterização da margem líquida de juros (MLJ) bancária, utilizando dados provinientes 15 países que pertencem ao sistema bancário da União Europeia (UE15). As variáveis que caracterizam os bancos são observados entre de 2004 e 2010. E são escolhidas seguindo Valverde et al. (2007). Adicionalmente aos regressores são acrescentadas algumas variáveis macroeconómicas. A seleção proviniente da falta de alguns valores para os regressores é tratada através da ponderação probabilistica inversa. Os ponderadores são aplicados a estimadores GMM para um modelo de dados de painel dinámico.
This thesis discusses techniques to correct for the potentially biasing effects of missing data. We apply the techniques on an economic model that explains the Net Interest margin (NIM) of banks, using data from 15 countries that are part of the European Union (EU15) banking system. The variables that describe banks cover the period 2004 and 2010. We use the variables that were also used in Carbó-Valverde and Fernndez (2007). In addition, also macroeconomic variables are used as regressors. The selection that occurs as a consequence of missing values in these regressor variables is dealt with by means of Inverse Probability Weighting (IPW) techniques. The weights are applied to a GMM estimator for a dynamic panel data model that would have been consistent in the absence of missing data.

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Badinger, Harald, and Peter Egger. "Estimation and Testing of Higher-Order Spatial Autoregressive Panel Data Error Component Models." Springer, 2013. http://epub.wu.ac.at/5468/1/JoGS_2012.pdf.

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This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define a generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their joint asymptotic distribution, and provide Monte Carlo evidence on their small sample performance.

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Akinc, Deniz. "Statistical Modelling Of Financial Statements Of Turkey: A Panel Data Analysis." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609824/index.pdf.

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Financial failure is an important subject for both the economical development of the country and for the self - evaluation of individual companies. Increase in the number of financially failed companies points out the misuse of the country resources. Recently, financial failure threatens both small and large companies in Turkey. It is important to determine factors that affect the financial failure by analyzing models and to use these models for auditing the financial situation. In today&rsquo
s Turkey, the statistical methods that are used for this purpose involve single level models applied to cross-sectional data. However, multilevel models applied to panel data are more preferable as they gather more information, and also, enable the calculated financial success probabilities to be more trustworthy. In this thesis, publicly available panel data that are collected from The Istanbul Stock Exchange are investigated. Mainly, financial success of companies from two sectors, namely industry and services, are investigated. For the analysis of this panel data, data exploration methods, missing data imputation, possible solutions to multicollinearity problem, single level logistic regression models and multilevel models are used. By these models, financial success probabilities for each company are calculated
the factors related to the financial failure are determined, and changes in time are observed. Models and early warning systems resulted in correct classification rates of up to 100%. In the services sector, a small number of companies having publicly available data result in a decline in the success of models. It is concluded that sharing data with more subjects observed in a longer time period collected in the same format with academicians, will result in better justified outputs, which are useful for both academicians and managers.

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Ranganathan, Shyam. "Non-linear dynamic modelling for panel data in the social sciences." Doctoral thesis, Uppsala universitet, Tillämpad matematik och statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-261289.

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Non-linearities and dynamic interactions between state variables are characteristic of complex social systems and processes. In this thesis, we present a new methodology to model these non-linearities and interactions from the large panel datasets available for some of these systems. We build macro-level statistical models that can verify theoretical predictions, and use polynomial basis functions so that each term in the model represents a specific mechanism. This bridges the existing gap between macro-level theories supported by statistical models and micro-level mechanistic models supported by behavioural evidence. We apply this methodology to two important problems in the social sciences, the demographic transition and the transition to democracy. The demographic transition is an important problem for economists and development scientists. Research has shown that economic growth reduces mortality and fertility rates, which reduction in turn results in faster economic growth. We build a non-linear dynamic model and show how this data-driven model extends existing mechanistic models. We also show policy applications for our models, especially in setting development targets for the Millennium Development Goals or the Sustainable Development Goals. The transition to democracy is an important problem for political scientists and sociologists. Research has shown that economic growth and overall human development transforms socio-cultural values and drives political institutions towards democracy. We model the interactions between the state variables and find that changes in institutional freedoms precedes changes in socio-cultural values. We show applications of our models in studying development traps. This thesis comprises the comprehensive summary and seven papers. Papers I and II describe two similar but complementary methodologies to build non-linear dynamic models from panel datasets. Papers III and IV deal with the demographic transition and policy applications. Papers V and VI describe the transition to democracy and applications. Paper VII describes an application to sustainable development.

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Lundmark, Albin, and Emma Roxström. "Urbanization and economic freedom - are they threats to air quality? : Evidence from a panel study of low and lower-middle-income countries." Thesis, Uppsala universitet, Nationalekonomiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-435088.

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Air pollution (in terms of PM2.5) is severe in developing countries, and the rapid population growth accompanied by urbanization may limit their potential economic development. This paper aims to investigate if urbanization and economic freedom cause higher levels of PM2.5 in developing countries. By measuring the potential effect of economic freedom on PM2.5 with the Ease of Doing Business-score by the World Bank, a new measure is introduced to the research on socioeconomic factors’ influence on air pollution. It is done by running both fixed effects- and system GMM regressions on a panel consisting of 63 low- and lower-middle-income economies between 2010-2017. The results indicate that PM2.5 is insensitive to changes in both variables and that urbanization’s effect on PM2.5 depends on the level of economic freedom and vice versa. However, both estimators may suffer from bias, and thus, the real relationship of urbanization and economic freedom on PM2.5 remains uncertain.

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Rodrigues, Paulo Jorge Maurício. "Panel data models with spatially correlated and heteroscedastic innovations : large and small sample results /." [S.l. : s.n.], 2007. http://swbplus.bsz-bw.de/bsz277731119inh.pdf.

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Salabasis, Mickael. "Bayesian time series and panel models : unit roots, dynamics and random effects." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics (Ekonomiska forskningsinstitutet vid Handelshögsk.) (EFI), 2004. http://www.hhs.se/efi/summary/632.htm.

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35

Darpeix, Pierre-Emmanuel. "Three essays in applied economics with panel data." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEH099/document.

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Cette thèse se compose de trois articles empiriques appliquant à divers sujets des techniques d'économétrie sur données de panel. L'article principal étudie l'évolution de la transmission des prix des trois principales céréales (blé, maïs, riz) des marchés internationaux vers les producteurs domestiques pour 52 pays sur la période 1970-2013, et cherche à identifier les principaux facteurs expliquant l'hétérogénéité des pass-through. Le second article mesure l'élasticité du trafic aérien au produit intérieur brut dans le monde et met en évidence la grande stabilité de la relation tant dans le temps que d'une région à l'autre. Enfin, le troisième article modélise le mécanisme de fixation du taux de rendement servi par les assureurs français à leurs clients sur les produits d'assurance-vie
This dissertation is composed of three empirical articles resorting to econometric methods in panel data analysis to address various research questions. The main article investigates the evolution of the level of price transmission for the three major cereals (wheat, maize and rice) from the international commodity markets down to the local producers for 52 countries between 1970 and 2013 while attempting to identify the main drivers of the heterogeneity in pass-through. The second article measures the elasticity of air-traffic to GDP around the world and demonstrates that the relationship is very stable across régions and through time. Eventually, the third article models the mechanisms through which French life-insurers set the rate of return they pay annually to their policyholders

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LeSage, James P., and Manfred M. Fischer. "Conventional versus network dependence panel data gravity model specifications." WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/6828/1/2019%2D2%2D11_v12_panel_gravity_model.pdf.

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Past focus in the panel gravity literature has been on multidimensional fixed effects specifications in an effort to accommodate heterogeneity. After introducing conventional multidimensional fixed effects, we find evidence of cross-sectional dependence in flows. We propose a simultaneous dependence gravity model that allows for network dependence in flows, along with computationally efficient Markov Chain Monte Carlo estimation methods that produce a Monte Carlo integration estimate of log-marginal likelihood useful for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications. The most important sources of network dependence were found to be membership in trade organizations, historical colonial ties, common currency and spatial proximity of countries.
Series: Working Papers in Regional Science

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37

Collado-Vindel, Maria Dolores. "Dynamic econometric models for cohort and panel data : methods and applications to life-cycle consumption." Thesis, London School of Economics and Political Science (University of London), 1994. http://etheses.lse.ac.uk/2829/.

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The purpose of this research is to analyze dynamic models for cohort and panel data, with special emphasis in the applications to life-cycle consumption. In the second chapter of the thesis we analyze the estimation of dynamic models from time-series of independent cross-sections. The population is divided in groups with fixed membership (cohorts) and the cohort sample means are used as a panel subject to measurement errors. We propose measurement error corrected estimators and we analyze their asymptotic properties. We also calculate the asymptotic biases of the non-corrected estimators to check up to what extent the measurement error correction is needed. Finally, we carry out Monte Carlo simulations to get an idea of the performance of our estimators in finite samples. The purpose of the second part is to test the life-cycle permanent income hypothesis using an unbalanced panel from the Spanish family expenditure survey. The model accounts for aggregate shocks and within period non-separability in the Euler equation among consumption goods, contrary to most of the literature in this area. The results do not indicate excess sensitivity of consumption growth to income. In the last chapter, we specify a system of nonlinear intertemporal (or Frisch) demands. Our choice of specification is based on seven criteria for such systems. These criteria are in terms of consistency with the theory, flexibility and econometric tractability. Our specification allows us to estimate a system of exact Euler equations in contrast to the usual practice in the literature. We then estimate the system on Spanish panel data. This is the first time that a Frisch demand system has been estimated on panel data. We do not reject any of the restrictions derived from theory. Our results suggest strongly that the intertemporal substitution elasticity is well determined.

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Salish, Nazarii [Verfasser]. "Essays on Heterogeneity and Non-Linearity in Panel Data and Time Series Models / Nazarii Salish." Bonn : Universitäts- und Landesbibliothek Bonn, 2016. http://d-nb.info/1188703617/34.

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39

Sandberg, Rickard. "Testing the unit root hypothesis in nonlinear time series and panel models." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-536.

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The thesis contains the four chapters: Testing parameter constancy in unit root autoregressive models against continuous change; Dickey-Fuller type of tests against nonlinear dynamic models; Inference for unit roots in a panel smooth transition autoregressive model where the time dimension is fixed; Testing unit roots in nonlinear dynamic heterogeneous panels. In Chapter 1 we derive tests for parameter constancy when the data generating process is non-stationary against the hypothesis that the parameters of the model change smoothly over time. To obtain the asymptotic distributions of the tests we generalize many theoretical results, as well as new are introduced, in the area of unit roots . The results are derived under the assumption that the error term is a strong mixing. Small sample properties of the tests are investigated, and in particular, the power performances are satisfactory. In Chapter 2 we introduce several test statistics of testing the null hypotheses of a random walk (with or without drift) against models that accommodate a smooth nonlinear shift in the level, the dynamic structure, and the trend. We derive analytical limiting distributions for all tests. Finite sample properties are examined. The performance of the tests is compared to that of the classical unit root tests by Dickey-Fuller and Phillips and Perron, and is found to be superior in terms of power. In Chapter 3 we derive a unit root test against a Panel Logistic Smooth Transition Autoregressive (PLSTAR). The analysis is concentrated on the case where the time dimension is fixed and the cross section dimension tends to infinity. Under the null hypothesis of a unit root, we show that the LSDV estimator of the autoregressive parameter in the linear component of the model is inconsistent due to the inclusion of fixed effects. The test statistic, adjusted for the inconsistency, has an asymptotic normal distribution whose first two moments are calculated analytically. To complete the analysis, finite sample properties of the test are examined. We highlight scenarios under which the traditional panel unit root tests by Harris and Tzavalis have inferior or reasonable power compared to our test. In Chapter 4 we present a unit root test against a non-linear dynamic heterogeneous panel with each country modelled as an LSTAR model. All parameters are viewed as country specific. We allow for serially correlated residuals over time and heterogeneous variance among countries. The test is derived under three special cases: (i) the number of countries and observations over time are fixed, (ii) observations over time are fixed and the number of countries tend to infinity, and (iii) first letting the number of observations over time tend to infinity and thereafter the number of countries. Small sample properties of the test show modest size distortions and satisfactory power being superior to the Im, Pesaran and Shin t-type of test. We also show clear improvements in power compared to a univariate unit root test allowing for non-linearities under the alternative hypothesis.
Diss. Stockholm : Handelshögskolan, 2004

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40

Kessler, Lawrence. "Bayesian Estimation of Panel Data Fractional Response Models with Endogeneity: An Application to Standardized Test Rates." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4518.

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In this paper I propose Bayesian estimation of a nonlinear panel data model with a fractional dependent variable (bounded between 0 and 1). Specifically, I estimate a panel data fractional probit model which takes into account the bounded nature of the fractional response variable. I outline estimation under the assumption of strict exogeneity as well as when allowing for potential endogeneity. Furthermore, I illustrate how transitioning from the strictly exogenous case to the case of endogeneity only requires slight adjustments. For comparative purposes I also estimate linear specifications of these models and show how quantities of interest such as marginal effects can be calculated and compared across models. Using data from the state of Florida, I examine the relationship between school spending and student achievement, and find that increased spending has a positive and statistically significant effect on student achievement. Furthermore, this effect is roughly 50% larger in the model which allows for endogenous spending. Specifically, a $1,000 increase in per-pupil spending is associated with an increase in standardized test pass rates ranging from 6.2-10.1%.

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41

Stammann, Amrei [Verfasser], Florian [Gutachter] Heiß, and Joel [Gutachter] Stiebale. "Nonlinear Panel Data Models with High-Dimensional Fixed Effects / Amrei Stammann ; Gutachter: Florian Heiß, Joel Stiebale." Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2020. http://d-nb.info/1203369735/34.

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42

Feng, Qu. "Essays on testing for cross-sectional dependence and estimation of change points in panel data models." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2009. http://wwwlib.umi.com/cr/syr/main.

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LeSage, James P., and Manfred M. Fischer. "MCMC estimation of panel gravity models in the presence of network dependence." WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6550/1/2018%2D10%2D2_WU%2DPub__panel_gravity_model.pdf.

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Past focus in the panel gravity literature has been on multidimensional fixed effects specifications in an effort to accommodate heterogeneity. After introducing fixed effects for each origin- destination dyad and time-period speciffic effects, we find evidence of cross-sectional dependence in flows. We propose a simultaneous dependence gravity model that allows for network dependence in flows, along with computationally efficient MCMC estimation methods that produce a Monte Carlo integration estimate of log-marginal likelihood useful for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications.
Series: Working Papers in Regional Science

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LeSage, James, and Manfred M. Fischer. "Cross-sectional dependence model specifications in a static trade panel data setting." WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/6886/1/2019%2D03%2D31_WP_Cross%2Dsectional.pdf.

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The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time invariant factors such as socio-cultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific and time-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.
Series: Working Papers in Regional Science

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45

LeSage, James P., and Manfred M. Fischer. "Cross-sectional dependence model specifications in a static trade panel data setting." WU Vienna University of Economics and Business, 2017. http://epub.wu.ac.at/5904/1/intl_trade_flows_dec_07_2017v3.pdf.

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The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time invariant factors such as socio-cultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.
Series: Working Papers in Regional Science

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46

Ruzibuka, John S. "The impact of fiscal deficits on economic growth in developing countries : Empirical evidence and policy implications." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/16282.

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This study examines the impact of fiscal deficits on economic growth in developing countries. Based on deduction from the relevant theoretical and empirical literature, the study tests the following hypotheses regarding the impact of fiscal deficits on economic growth. First, fiscal deficits have significant positive or negative impact on economic growth in developing countries. Second, the impact of fiscal deficits on economic growth depends on the size of deficits as a percentage of GDP – that is, there is a non-linear relationship between fiscal deficits and economic growth. Third, the impact of fiscal deficits on economic growth depends on the ways in which deficits are financed. Fourth, the impact of fiscal deficits on economic growth depends on what deficit financing is used for. The study also examines whether there are any significant regional differences in terms of the relationship between fiscal deficits and economic growth in developing countries. The study uses panel data for thirty-one developing countries covering the period 1972- 2001, which is analysed based on the econometric estimation of a dynamic growth model using the Arellano and Bond (1991) generalised method of moments (GMM) technique. Overall, the results suggest the following. First, fiscal deficits per se have no any significant positive or negative impact on economic growth. Second, by contrast, when the deficit is substituted by domestic and foreign financing, we find that both domestic and foreign financing of fiscal deficits exerts a negative and statistically significant impact on economic growth with a lag. Third, we find that both categories of economic classification of government expenditure, namely, capital and current expenditure, have no significant impact on economic growth. When government expenditure is disaggregated on the basis of a functional classification, the results suggest that spending on education, defence and economic services have positive but insignificant impact on growth, while spending on health and general public services have positive and significant impact. Fourth, in terms of regional differences with regard to the estimated relationships, the study finds that, while there are some regional differences between the four different regions represented in our sample of thirty-one developing countries - namely, Asia and the Pacific, Latin America and the Caribbean, Middle East and North Africa, and Sub-Saharan Africa – these differences are not statistically significant. On the basis of these findings, the study concludes that fiscal deficits per se are not necessarily good or bad for economic growth in developing countries; how the deficits are financed and what they are used for matters. In addition, the study concludes that there are no statistically significant regional differences in terms of the relationship between fiscal deficits and economic growth in developing countries.

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Kapoor, Mudit. "Panel data models with spatial correlation estimation theory and empirical investigation of the US wholesale gasoline industry /." College Park, Md. : University of Maryland, 2003. http://hdl.handle.net/1903/143.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2003.
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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48

Pihl, Svante, and Leonardo Olivetti. "An Empirical Comparison of Static Count Panel Data Models: the Case of Vehicle Fires in Stockholm County." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412014.

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In this paper we study the occurrences of outdoor vehicle fires recorded by the Swedish Civil Contingencies Agency (MSB) for the period 1998-2019, and build static panel data models to predict future occurrences of fire in Stockholm County. Through comparing the performance of different models, we look at the effect of different distributional assumptions for the dependent variable on predictive performance. Our study concludes that treating the dependent variable as continuous does not hamper performance, with the exception of models meant to predict more uncommon occurrences of fire. Furthermore, we find that assuming that the dependent variable follows a Negative Binomial Distribution, rather than a Poisson Distribution, does not lead to substantial gains in performance, even in cases of overdispersion. Finally, we notice a slight increase in the number of vehicle fires shown in the data, and reflect on whether this could be related to the increased population size.

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Tekieh, Mohammad Hossein. "Analysis of Healthcare Coverage Using Data Mining Techniques." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20547.

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This study explores healthcare coverage disparity using a quantitative analysis on a large dataset from the United States. One of the objectives is to build supervised models including decision tree and neural network to study the efficient factors in healthcare coverage. We also discover groups of people with health coverage problems and inconsistencies by employing unsupervised modeling including K-Means clustering algorithm. Our modeling is based on the dataset retrieved from Medical Expenditure Panel Survey with 98,175 records in the original dataset. After pre-processing the data, including binning, cleaning, dealing with missing values, and balancing, it contains 26,932 records and 23 variables. We build 50 classification models in IBM SPSS Modeler employing decision tree and neural networks. The accuracy of the models varies between 76% and 81%. The models can predict the healthcare coverage for a new sample based on its significant attributes. We demonstrate that the decision tree models provide higher accuracy that the models based on neural networks. Also, having extensively analyzed the results, we discover the most efficient factors in healthcare coverage to be: access to care, age, poverty level of family, and race/ethnicity.

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50

Ruzibuka, John Shofel. "The impact of fiscal deficits on economic growth in developing countries : empirical evidence and policy implications." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/16282.

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Abstract:

This study examines the impact of fiscal deficits on economic growth in developing countries. Based on deduction from the relevant theoretical and empirical literature, the study tests the following hypotheses regarding the impact of fiscal deficits on economic growth. First, fiscal deficits have significant positive or negative impact on economic growth in developing countries. Second, the impact of fiscal deficits on economic growth depends on the size of deficits as a percentage of GDP - that is, there is a non-linear relationship between fiscal deficits and economic growth. Third, the impact of fiscal deficits on economic growth depends on the ways in which deficits are financed. Fourth, the impact of fiscal deficits on economic growth depends on what deficit financing is used for. The study also examines whether there are any significant regional differences in terms of the relationship between fiscal deficits and economic growth in developing countries. The study uses panel data for thirty-one developing countries covering the period 1972- 2001, which is analysed based on the econometric estimation of a dynamic growth model using the Arellano and Bond (1991) generalised method of moments (GMM) technique. Overall, the results suggest the following. First, fiscal deficits per se have no any significant positive or negative impact on economic growth. Second, by contrast, when the deficit is substituted by domestic and foreign financing, we find that both domestic and foreign financing of fiscal deficits exerts a negative and statistically significant impact on economic growth with a lag. Third, we find that both categories of economic classification of government expenditure, namely, capital and current expenditure, have no significant impact on economic growth. When government expenditure is disaggregated on the basis of a functional classification, the results suggest that spending on education, defence and economic services have positive but insignificant impact on growth, while spending on health and general public services have positive and significant impact. Fourth, in terms of regional differences with regard to the estimated relationships, the study finds that, while there are some regional differences between the four different regions represented in our sample of thirty-one developing countries - namely, Asia and the Pacific, Latin America and the Caribbean, Middle East and North Africa, and Sub-Saharan Africa - these differences are not statistically significant. On the basis of these findings, the study concludes that fiscal deficits per se are not necessarily good or bad for economic growth in developing countries; how the deficits are financed and what they are used for matters. In addition, the study concludes that there are no statistically significant regional differences in terms of the relationship between fiscal deficits and economic growth in developing countries.

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

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