Essays in Applied Panel Data Econometrics and Machine Learning

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (19 download)

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Book Synopsis Essays in Applied Panel Data Econometrics and Machine Learning by : Ghalib Absar Ahmed Minhas

Download or read book Essays in Applied Panel Data Econometrics and Machine Learning written by Ghalib Absar Ahmed Minhas and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Essays in Applied Panel Data Econometrics

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ISBN 13 :
Total Pages : 226 pages
Book Rating : 4.:/5 (778 download)

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Book Synopsis Essays in Applied Panel Data Econometrics by : Stephanie Kremer

Download or read book Essays in Applied Panel Data Econometrics written by Stephanie Kremer and published by . This book was released on 2011 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Essays in Honor of Cheng Hsiao

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Publisher : Emerald Group Publishing
ISBN 13 : 1789739594
Total Pages : 418 pages
Book Rating : 4.7/5 (897 download)

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Book Synopsis Essays in Honor of Cheng Hsiao by : Dek Terrell

Download or read book Essays in Honor of Cheng Hsiao written by Dek Terrell and published by Emerald Group Publishing. This book was released on 2020-04-15 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.

Essays on Applied Economics with Machine Learning Approach

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ISBN 13 :
Total Pages : 110 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Essays on Applied Economics with Machine Learning Approach by : Tzai-Shuen Chen

Download or read book Essays on Applied Economics with Machine Learning Approach written by Tzai-Shuen Chen and published by . This book was released on 2018 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation concentrates on applying machine learning methods to economic policy analysis. When talking about using machine learning or other non-behavioral model to conduct policy analysis, the first question raised by economists is the Lucas critique. A policy intervention would affect the incentive that people face and thus changes the underlying decision-making problem. A predictive model without the component of optimizing behavior might not capture people's reactions to the policy intervention to give a reliable prediction. Even if the quantitative effect of the Lucas critique is not significant, the machine learning method might have no advantage over a well-performed standard econometric model in terms of prediction or time efficiency. The first chapter presents an out-of-sample prediction comparison between major machine learning models and the structural econometric model. To evaluate the benefits of this approach, I use the most common machine learning algorithms, CART, C4.5, LASSO, random forest, and adaboost, to construct prediction models for a cash transfer experiment conducted by the Progresa program in Mexico, and I compare the prediction results with those of a previous structural econometric study. Two prediction tasks are performed in this paper: the out-of-sample forecast and the long-term within-sample simulation. For the out-of-sample forecast, both the mean absolute error and the root mean square error of the school attendance rates found by all machine learning models are smaller than those found by the structural model. Random forest and adaboost have the highest accuracy for the individual outcomes of all subgroups. For the long-term within-sample simulation, the structural model has better performance than do all of the machine learning models. The poor within-sample fitness of the machine learning model results from the inaccuracy of the income and pregnancy prediction models. The result shows that the machine learning model performs better than does the structural model when there are many data to learn; however, when the data are limited, the structural model offers a more sensible prediction. In addition to prediction outcome, machine learning models are more time-efficient than the structural model. The most complicated model, random forest, takes less than half an hour to build and less than one minute to predict. The findings show promise for adopting machine learning in economic policy analyses in the era of big data. The second chapter exploits the predictive power of machine learning algorithms to conduct covariate adjustment for estimating average treatment effects and the log-odds ratio. Previous semi-parametric approaches have proven that baseline covariate adjustment can increase the estimator efficiency and statistical power, compared to an unadjusted estimator. I use random forest model to select predictive covariates and conduct a Monte Carlo simulation to compare the efficiency and statistical power of unadjusted, OLS-based, and random-forest-based approaches in different parameter settings. The simulation result indicates that the random-forest-based estimator is more efficient and has higher statistical power than the other two methods. In addition, I apply this approach to the Zomba Cash Transfer Experiment in Malawi to study the difference in policy effect between conditional and unconditional cash transfers. The third chapter investigates the possibility of using machine learning models to conduct the counterfactual analysis for conditional policies. Conditional Cash Transfer has become a popular tool to alleviate intergenerational poverty in many developing countries due to the success of the Progresa program in Mexico. There are some experiments focused on the implementation details to explore the efficient practice of the policy implementation. The policy analysis, however, still heavily relies on the counterfactual prediction because of the budget and time constraints. Recently, machine learning has been proved successful in many prediction applications. Adopting machine learning model into economic policy analysis might help to increase the prediction performance and hence offer another approach of counterfactual analysis. While it is straightforward to apply machine learning algorithms to conduct counterfactual prediction for the unconditional policy, there is no direct prediction for the conditional policy due to the lack of behavioral description. This chapter uses the Zomba Cash Transfer Experiment in Malawi to examine the error of using an unconditional machine learning approach to prediction the outcome of the conditional policy. The result shows that the error from the conditional-unconditional difference is a minor source of prediction errors, which provides support of exploiting the predictive power of machine learning algorithms to offer policy suggestions for the conditional policy.

Advances in Panel Data Analysis in Applied Economic Research

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Publisher : Springer
ISBN 13 : 3319700553
Total Pages : 701 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Advances in Panel Data Analysis in Applied Economic Research by : Nicholas Tsounis

Download or read book Advances in Panel Data Analysis in Applied Economic Research written by Nicholas Tsounis and published by Springer. This book was released on 2018-04-17 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings volume presents new methods and applications in applied economic research with an emphasis on advances in panel data analysis. Featuring papers presented at the 2017 International Conference on Applied Economics (ICOAE) held at Coventry University, this volume provides current research on econometric panel data methodologies as they are applied in microeconomics, macroeconomics, financial economics and agricultural economics. International Conference on Applied Economics (ICOAE) is an annual conference that started in 2008 designed to bring together economists from different fields of applied economic research in order to share methods and ideas. Applied economics is a rapidly growing field of economics that combines economic theory with econometrics to analyse economic problems of the real world usually with economic policy interest. In addition, there is growing interest in the field for panel data estimation methods, tests and techniques. This volume makes a contribution in the field of applied economic research in this area. Featuring country specific studies, this book will be of interest to academics, students, researchers, practitioners, and policy makers in applied economics and economic policy.

Essays on Panel Data Econometrics

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (128 download)

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Book Synopsis Essays on Panel Data Econometrics by : Ayden Higgins

Download or read book Essays on Panel Data Econometrics written by Ayden Higgins and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Essays on the Econometric Analysis of Panel Data

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ISBN 13 :
Total Pages : 440 pages
Book Rating : 4.:/5 (448 download)

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Book Synopsis Essays on the Econometric Analysis of Panel Data by : Keisuke Hirano

Download or read book Essays on the Econometric Analysis of Panel Data written by Keisuke Hirano and published by . This book was released on 1998 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Essays in Panel Data Econometrics Examining Selection Bias and Average Treatment Effects

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ISBN 13 :
Total Pages : 230 pages
Book Rating : 4.3/5 (129 download)

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Book Synopsis Essays in Panel Data Econometrics Examining Selection Bias and Average Treatment Effects by : Kamyar Nasseh

Download or read book Essays in Panel Data Econometrics Examining Selection Bias and Average Treatment Effects written by Kamyar Nasseh and published by . This book was released on 2007 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Essays in Panel Data Econometrics with Cross-sectional Dependence

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (16 download)

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Book Synopsis Essays in Panel Data Econometrics with Cross-sectional Dependence by : Lena Körber

Download or read book Essays in Panel Data Econometrics with Cross-sectional Dependence written by Lena Körber and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Essays in Applied Machine Learning and Economics

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ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (136 download)

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Book Synopsis Essays in Applied Machine Learning and Economics by : Garima Singal

Download or read book Essays in Applied Machine Learning and Economics written by Garima Singal and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we contribute to two strands of economic literature- applied economics and game theory. The contribution to applied economics is developing a prediction model for PM2.5, a key indicator of air pollution. The PM2.5 predictions from this model enable an analysis of economic and environmental policies that were previously infeasible due to a lack of PM2.5 measurements, especially in developing regions. We also demonstrate the unsuitability, for Delhi specifically, of the predominant benchmark estimates for PM2.5, in the applied economics literature supplied by van Donkelaar et al. Additionally, we were able to introduce and demonstrably improve upon a frontier technique from the deep learning literature to the applied economics literature. The contribution to the game theory literature takes the form of assessing the optimality of contests as a mechanism in the context of a standard Myersonian mechanism design environment, a previously unexplored setting. We find that despite extensive usage of contests as a mechanism in the real world, it is not without loss in revenue to use optimal contests. This dissertation's primary contribution is developing a modeling pipeline with lower data requirements and better predictive performance than the existing state-of-the-art estimates in the applied economics literature.

Panel Data Analysis using EViews

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Publisher : John Wiley & Sons
ISBN 13 : 111871556X
Total Pages : 544 pages
Book Rating : 4.1/5 (187 download)

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Book Synopsis Panel Data Analysis using EViews by : I. Gusti Ngurah Agung

Download or read book Panel Data Analysis using EViews written by I. Gusti Ngurah Agung and published by John Wiley & Sons. This book was released on 2013-12-31 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and accessible guide to panel data analysis using EViews software This book explores the use of EViews software in creating panel data analysis using appropriate empirical models and real datasets. Guidance is given on developing alternative descriptive statistical summaries for evaluation and providing policy analysis based on pool panel data. Various alternative models based on panel data are explored, including univariate general linear models, fixed effect models and causal models, and guidance on the advantages and disadvantages of each one is given. Panel Data Analysis using EViews: Provides step-by-step guidance on how to apply EViews software to panel data analysis using appropriate empirical models and real datasets. Examines a variety of panel data models along with the author’s own empirical findings, demonstrating the advantages and limitations of each model. Presents growth models, time-related effects models, and polynomial models, in addition to the models which are commonly applied for panel data. Includes more than 250 examples divided into three groups of models (stacked, unstacked, and structured panel data), together with notes and comments. Provides guidance on which models not to use in a given scenario, along with advice on viable alternatives. Explores recent new developments in panel data analysis An essential tool for advanced undergraduate or graduate students and applied researchers in finance, econometrics and population studies. Statisticians and data analysts involved with data collected over long time periods will also find this book a useful resource.

Essays in Honor of Joon Y. Park

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Publisher : Emerald Group Publishing
ISBN 13 : 1837532125
Total Pages : 449 pages
Book Rating : 4.8/5 (375 download)

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Book Synopsis Essays in Honor of Joon Y. Park by : Yoosoon Chang

Download or read book Essays in Honor of Joon Y. Park written by Yoosoon Chang and published by Emerald Group Publishing. This book was released on 2023-04-24 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volumes 45a and 45b of Advances in Econometrics honor Professor Joon Y. Park, who has made numerous and substantive contributions to the field of econometrics over a career spanning four decades since the 1980s and counting.

Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes

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Publisher : World Scientific
ISBN 13 : 9811220794
Total Pages : 167 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes by : Feng Qu

Download or read book Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes written by Feng Qu and published by World Scientific. This book was released on 2020-08-24 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to fill the gap between panel data econometrics textbooks, and the latest development on 'big data', especially large-dimensional panel data econometrics. It introduces important research questions in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, Estimation and Structural Changes also introduces new research questions and results in recent literature in this field.

Analysis of Panel Data

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Publisher : Cambridge University Press
ISBN 13 : 131651210X
Total Pages : 539 pages
Book Rating : 4.3/5 (165 download)

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Book Synopsis Analysis of Panel Data by : Cheng Hsiao

Download or read book Analysis of Panel Data written by Cheng Hsiao and published by Cambridge University Press. This book was released on 2022-07-07 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction of fundamental panel data methodologies.

The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data

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Publisher : International Monetary Fund
ISBN 13 : 1513529978
Total Pages : 21 pages
Book Rating : 4.5/5 (135 download)

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Book Synopsis The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data by : Marijn A. Bolhuis

Download or read book The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data written by Marijn A. Bolhuis and published by International Monetary Fund. This book was released on 2020-02-28 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: We leverage insights from machine learning to optimize the tradeoff between bias and variance when estimating economic models using pooled datasets. Specifically, we develop a simple algorithm that estimates the similarity of economic structures across countries and selects the optimal pool of countries to maximize out-of-sample prediction accuracy of a model. We apply the new alogrithm by nowcasting output growth with a panel of 102 countries and are able to significantly improve forecast accuracy relative to alternative pools. The algortihm improves nowcast performance for advanced economies, as well as emerging market and developing economies, suggesting that machine learning techniques using pooled data could be an important macro tool for many countries.

Panel Data Econometrics

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Publisher : OUP Oxford
ISBN 13 : 0191529672
Total Pages : 244 pages
Book Rating : 4.1/5 (915 download)

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Book Synopsis Panel Data Econometrics by : Manuel Arellano

Download or read book Panel Data Econometrics written by Manuel Arellano and published by OUP Oxford. This book was released on 2003-06-26 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, by one of the world's leading experts on dynamic panel data, presents a modern review of some of the main topics in panel data econometrics. The author concentrates on linear models, and emphasizes the roles of heterogeneity and dynamics in panel data modelling. The book combines methods and applications, so will appeal to both the academic and practitioner markets. The book is divided in four parts. Part I concerns static models, and deals with the problem of unobserved heterogeneity and how the availability of panel data helps to solve it, error component models, and error in variables in panel data. Part II looks at time series models with error components. Its chapters deal with the problem of distinguishing between unobserved heterogeneity and individual dynamics in short panels, modelling strategies of time effects, moving average models, inference from covariance structures, the specification and estimation of autoregressive models with heterogeneous intercepts, and the impact of assumptions about initial conditions and heteroskedacity on estimation. Part III examines dynamics and predeterminedness. Its two chapters consider alternative approaches to estimation from small and large T perspectives, looking at models with both strictly exogenous and lagged dependent variables allowing for autocorrelation of unknown form, models in which the errors are mean independent of current and lagged values of certain conditioning variables but not with their future values. Together Parts II and III provide a synthesis, and unified perspective, of a vast literature that has had a significant impact on recent econometric practice. Part IV reviews the main results in the theory of generalized method of moments estimation and optimal instrumental variables.

Mostly Panel Econometrics

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Publisher :
ISBN 13 : 9789180392266
Total Pages : pages
Book Rating : 4.3/5 (922 download)

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Book Synopsis Mostly Panel Econometrics by : Ovidijus Stauskas

Download or read book Mostly Panel Econometrics written by Ovidijus Stauskas and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis consists of five chapters which focus on panel data theory. Four of them analyze explicit panel data models and one chapter deals with time series forecasting model, where external panel data help us estimate unobserved explanatory variables. The broad topics discussed in the thesis include i) simplification of distribution of a statistical test under double asymptotics, ii) elimination of fixed effects and bias correction in dynamic panels, iii) accounting for cross-section dependence and estimation of latent factors when they can be non-stationary and iv) usage of latent factors to improve out-of-sample forecasts and testing competing forecast models. In Chapter I, we re-visit a problem posed by Phillips and Lee (2015, Econometric Reviews). They considered a simple bivariate vector autoregression (VAR), where both series exhibited different degrees of non-stationarity: near unit root and mild explosiveness. While one is interested in testing whether both series are in the lower vicinity of unit root and share the same persistence features, unfortunately, Wald test statistic degenerates under the null. We re-consider this setup in the context of panel data, where we use extra observations from the cross-section to simplify asymptotic distributions in order to obtain chi-square-based inference.??Chapter II looks into very popular factor augmented linear forecast models and tests to evaluate out-of-sample forecasting accuracy. In large macroeconomic datasets, various series tend to co-move together and it is modelled by employing a small number of latent factors (see e.g. Stock and Watson, 1999 and 2002). Instead of using a large number of available variables, researchers reduce the dataset dimension by estimating the driving factors and use those estimates directly. We further explore two tests of equal forecasting accuracy for nested models to investigate if factor augmented model outperforms parsimonious model with known set of variables. Unlike Gonçalves el. al (2017, Journal of Econometrics), where the factors are estimated using Principal Components (PC) under presumably known number of factors, we employ Common Correlated Effects (CCE) estimator which is very user friendly and employs a common thematic block structure of large macro datasets. Factors are estimated as block averages to proxy the common underlying information given by factors.??We continue discussing latent factors in Chapter III and Chapter IV. Here we focus on panel data, where unobserved factors model strong cross-section dependence among the panel units and possible endogeneity within the individual time series. Pesaran (2006, Econometrica) suggested solving these issues by augmenting the regression with cross-section averages of the dependent and independent variables. This is CCE estimator. While very simple, pooled version of CCE (CCEP) is asymptotically biased under homogeneous slopes, unless the number of individuals dominates the length of time series in the panel. Moreover, typically the bias is inestimable and analytic correction is not possible. In Chapter III, we analyze the properties of a simple 'pairs' bootstrap algorithm discussed in Kapetanios (2008, Econometrics Journal) in the context of CCE and develop bootstrap-based bias correction procedure. In Chapter IV, we continue the study of Westerlund (2018, Econometrics Journal), where CCE was extended to non-stationary factors of a very general type. In the latter study, however, only CCEP under homogeneous slopes was examined, but we extend the analysis to heterogeneous slopes and explore the properties of the mean group (CCEMG) estimator in order to further model unobserved heterogeneity.??The thesis concludes with Chapter V, where we re-visit at a classical problem in dynamic panels with fixed effects known as Nickel Bias. De-meaning the data to purge individual-specific effects in dynamic panels makes the model errors correlated, and the bias accumulates if the time dimension is large. On the other hand, if we estimate the fixed effects, we run into incidental parameter problem. Bai (2013, Econometrica) considered the so-called Factor Analytical (FA) estimator, which circumvents these issues by estimating the sample variance of individual effects rather than the effects themselves. In the latter study, panel AR(1) model with autoregressive parameter in the stationary region was explored. We extend this to autoregressive coefficient tending to unity and incidental trends, similarly to Moon and Phillips (2004, Econometrica) in order to account for trending and drifting variables.