DOCSLIB.ORG

Using Panel Data Analysis to Define Most Global Warming Characteristic

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Using Panel Data Analysis to Define Most Global Warming Characteristic Al-Azhar University-Gaza Deanship of Postgraduate Studies Faculty of Economics and Administrative Sciences Department of Applied Statistic Using Panel Data Analysis To Define Most Global Warming Characteristic استخدام تحليل السﻻسل الزمنية المقطعية في التعريف بعوامل ارتفاع حرارة اﻻرض by Mohammed Ismail Ramadan Khader Supervisor Prof. Dr. Abdalla El-Habil Professor of Statistics A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master in Applied Statistic 2018 II DECLARATION I, the undersigned hereby, declare that the thesis titled: Using Panel Data Analysis To Define Most Global Warming Characteristic استخدام تحليل السﻻسل الزمنية المقطعية في التعريف بعوامل ارتفاع حرارة اﻻرض Is my own research work and the work provided in this thesis, unless otherwise referenced, is the researcher's own work, and has never been submitted elsewhere for any other degree qualifications nor for any academic titles, nor for any other academic or publishing institutions. I, hereto, affirm that I will be completely responsible in academic and legal terms if this work proves the opposite. Student's name: Mohammed Ismail Ramadan Khader Signature: Mohammed Ismail Ramadan Khader Date: 6 / 11 / 2018 III بسم اهلل الرمحن الرحيم ( أَحَسِبَ النَّاسُ أَنْ يُتْرَكُوا أَنْ يَقُولُوا آمَنَّا وَهُمْ ﻻ يُفْتَنُونَ ) )سورة العنكبوث: اﻷيت 2( IV TO MY PARENTS and My family V ACKNOWLEDGEMENTS First, i would like to express my sincere gratitude to “Allah” for the blessing and mercy given to me during the completion of this thesis. To my parents for their support in all circumstances. I would like to express my sincere gratitude and heartfelt thanks to my supervisor Prof. Dr. Abdalla El-Habil for the continuous support of my M.Sc. study and related research, for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my M.Sc. study. My sincere thanks also goes to Prof. Dr. Mahmoud Okasha, Dr. Shady Al- Telbany, and Dr. Mo'omen El-Hanjouri, who provided me an opportunity to join their team as intern, and who gave access to the laboratory and research facilities. Without their precious support it would not be possible to conduct this research. Besides my advisor, I would like to thank the rest of my thesis committee, Dr. Mo'omen El-Hanjouri and Dr. Raed Sallha for their insightful comments and encouragement, but also for the hard question which incented me to widen my research from various perspectives. Last but not least, I would like to thank my family, friends and those who supported me throughout my years of study. Thank you All. VI ABSTRACT This thesis, discusses the linear panel data analysis procedures which is basically models in panel data literature. It also discusses some of the related estimation methods and the so called individual specific effect . We discussed the unobservable individual effects as one of the key advantages of panel data models which lead to control for omitted variables. Linear models with constant slopes and individual intercepts (with no time effect) had particular importance in this thesis. Appropriate estimation methods for linear panel data illustrated, ordinary least squares and generalized least squares method and its technics were displayed as the adopted estimation method in this thesis. Also, this thesis explain suitable estimation for fixed effect and random effect models. Problems like endogenity and its sources, heteroskedasticity and autocorrelation had been presented. A lot of climate data had been treated with statistical analysis, especially with time series. In this thesis, we look forward to dealing climate data with panel data analysis. Global warming is our case study, we present in a simplified way the phenomenon of climate change, some related concepts. We illustrated factors believed to be the cause of climate change like carbon dioxide emissions and sun activity. We applied almost all methods that we theoretically discussed in the preceding chapters on a real dataset from thirty countries included individual temperature anomalies as dependent variable, global carbon dioxide, individual carbon dioxide, sun spot number (sun activity) and global temperature anomalies, all as dependent variables. The study concluded that the linear fixed effect model represent the best model among all other linear model and it can explain more than 66% of the variance in the independent temperature anomalies variable. Global carbon dioxide emissions is significant variable increasing effect on temperature anomalies per million metric ton. Also, the Individual carbon dioxide emission is significant variable with p- value (0.000) and decreasing effect on temperature anomalies per million metric ton. The overall constant in the model is significant p- value (0.0063) with increasing effect on temperature anomalies . The global temperature anomalies is significant variable p-value (0.000) with increasing effect . Which means, the individual temperature anomalies are effected by the overall global temperature anomalies . Sun activity variable(sun spot number) has no significant effect in the model p-value (0.9905), that means the change in its periodic activity doesn't influence the climate change phenomena.. The overall constant in the model is significant p- value (0.0063) with increasing effect on temperature anomalies . And finally, each country has its own intercept ( ̂ ), some of them have increasing effect on the temperature anomalies and the others have decreasing effect. VII ملخص ذٌالش ُزٍ اﻷطشّحح إجشاءاخ ذحل٘ل ت٘اًاخ السﻻسل الضهٌ٘ح الومطؼ٘ح الخط٘ح ّالوؼشّفح توسؤ الثٌل, ّالرٖ ُٖ ًوارج أساس٘ح فٖ أدت٘اخ ت٘اًاخ الثٌل. لذهٌا هو٘ضاخ الثٌل ّأّضحٌا ذفْلِا هماسًح هغ السﻻسل الضهٌ٘ح ّ ًوارج اﻻًحذاس الخطٖ, ّذث٘ي أى ُزٍ الوو٘ضاخ ٗرثؼِا صؼْتح فٖ الرحل٘ل ّاﻻسرخذام. ّﻷى ذحل٘ل الثٌل ؼٗرثش كأسلْب ذحل٘ل هرؼذد اﻻذجاُاخ )Multivariate analysis( ذْجة ػلٌ٘ا إٗضاح الٌوْرج الخطٖ تاسرخذام الوصفْفاخ الشٗاض٘ح ّكزلك ت٘اى الشكل الؼام لوصفْفح الرغاٗش. كوا ًالشٌا تؼض طشق الرمذٗش راخ الصلح ّالرأث٘ش الفشدٕ الوحذد. كزلك أّضحٌا أُن هو٘ضاخ الثٌل الوروثلح فٖ اكرشاف الرأث٘شاخ الفشدٗح غ٘ش الماتلح لﻻكرشاف تاػرثاسُا إحذٓ الوضاٗا الشئ٘س٘ح لٌوارج ت٘اًاخ الثٌل, ّ الرٖ ٗوكي هي خﻻلِا الرغلة ػلٔ هشكلح الورغ٘شاخ الوخف٘ح أّ الوحزّفح. ّلمذ أّلد ُزٍ اﻷطشّحح أُو٘ح خاصح للٌوارج الخط٘ح راخ الوؼالن الثاترح ّالمْاطغ الفشدٗح )تذّى ذأث٘ش صهٌٖ(. ّتوا أى طشق الرمذٗش الرمل٘ذٗح ﻻ ذجذٕ ًفؼا هغ ُزا اﻻسلْب الورطْس, ذن إٗضاح تؼض طشق الرمذٗش الوٌاسثح لث٘اًاخ الثٌل الخط٘ح , كطشٗمح ذمذٗش الوشتؼاخ الصغشٓ ّطشٗمح الوشتؼاخ الصغشٓ الوؼووح ّتؼض اﻷسال٘ة الخاصح تِا ّأسرخذاهِا كطشق هؼروذج للرمذٗش. كوا ذششح ُزٍ الشسالح ذمذٗ ًشا هٌاسثًا لٌوارج الرأث٘شاخ الثاترح ًّوارج الرأث٘شاخ الؼشْائ٘ح , ّاﻷُن ك٘ف ٗوكي الرؼاهل هغ أُن هشكلر٘ي فٖ ذحل٘ل الثٌل ُّٖ ػذم الرجاًس ت٘ي ذثاٗي اﻻخطاء الؼشْائ٘ح ّكزلك الرشاتظ الزاذٖ ت٘ي ُزٍ اﻷخطاء. أضف الٔ رلك الرشاتظ ت٘ي اﻷخطاء الؼشْائ٘ح ّالورغ٘شاخ الوسرملح ال ُو َسؤ (Endogenity) ّهصادسٍ. اﻹحرشاس الوٌاخٖ ُْ هْضْع الذساسح الرطث٘م٘ح فٖ ُزٍ اﻻطشّحح. لذهٌا تشكل هثسظ ظاُشج الرغ٘ش الوٌاخٖ ّتؼض الوفاُ٘ن الوشذثطح تِا. أّضحٌا تؼض الؼْاهل الوفرشضح كوسثثاخ للرغ٘ش الوٌاخٖ هثل اًثؼاثاخ غاص ثاًٖ أكس٘ذ الكشتْى ّالٌشاط الشوسٖ. ثن لوٌا ترطث٘ك هؼظن اﻻسال٘ة الٌظشٗح ساتمح الزكش ػلٔ هجوػْح ت٘اًاخ حم٘م٘ح ذشول ثﻻثْى دّلح ّفِ٘ا هرغ٘ش اًحشاف دسجاخ الحشاسج ػي الورْسظ كورغ٘ش ذاتغ, ّهرغ٘شاخ اًثؼاثاخ ثاًٖ أكس٘ذ الكشتْى الوحل٘ح ّاًثؼاثاذَ الؼالو٘ح ّكزلك اًحشاف دسجاخ الحشاسج الؼالو٘ح ػي الورْسظ ػّذد الثمغ الشوس٘ح )الٌشاط الشوسٖ(, جوؼِ٘ا كورغ٘شاخ هسرملح. اسرٌرجد الذساسح أى ًوْرج الرأث٘شاخ الثاترح ُْ اﻷفضل هي ت٘ي الٌوارج اﻻخشٓ, ّأى ُزا الٌوْرج ٗوكٌَ ذفس٘ش 66% هي الرثاٗي الخاص تالورغ٘ش الراتغ. كوا أى هرغ٘ش اًثؼاثاخ الكشتْى الؼالو٘ح رّ دﻻلح إحصائ٘ح ػٌذ p-value 0.05 ّلَ ذأث٘ش تالضٗادج ػلٔ اًحشاف دسجاخ الحشاسج ػي الورْسظ تومذاس لكل هلْ٘ى طي هرشٕ, كوا أى هرغ٘ش اًثؼاثاخ ثاًٖ أكس٘ذ الكشتْى الوحل٘ح رّ دﻻلح احصائ٘ح ػٌذ p-value 0.05 ّلَ ذأث٘ش تالسالة ػلٔ اًحشاف دسجاخ الحشاسج ػي الورْسظ تومذاس لكل هلْ٘ى طي هرشٕ. كوا أى همذاس الثاتد رّ دﻻلَ احصائ٘ح ػٌذ (p- value (0.05 ّلَ ذأث٘ش تالضٗادج تومذاس ,كوا أى هرغ٘ش هرْسظ اًحشاف دسجاخ الحشاسج الؼالو٘ح رّ دﻻلح احصائ٘ح ػٌذ p-value 0.05 تومذاس . أها هرغ٘ش ػذد الثمغ الشوس٘ح ّالخاص تالٌشاط الشوسٖ لن ٗكي رّ دﻻلح احصائ٘ح (p-value (0.9905 , إٔ أى الشوس ل٘س لِا دّس ٗزكش فٖ اﻻحرشاس الوٌاخٖ. كوا أى لكل دّلح همذاس ثاتد خاص تِا ̂ تؼضِا رّ ذأث٘ش تالضٗادج ّتؼضِا تالسالة. VIII TABLE OF CONTENTS ABSTRACT ................................................................................................................................ VII LIST OF TABLES ...................................................................................................................... XII LIST OF FIGURES .................................................................................................................... XIII CHAPTER ONE - 1 -INTRODUCTION ................................................................................... - 1 - 1.1 Background .................................................................................................................... - 1 - 1.2 Research Problem ............................................................................................................ - 2 - 1.3 Research Methodology ..................................................................................................... - 3 - 1.4 Research Objectives ......................................................................................................... - 3 - 1.5 Literature Review ............................................................................................................
Load more

Recommended publications
  • Module Three, Part One: Panel Data Analysis in Economic Education Research
    MODULE THREE, PART ONE: PANEL DATA ANALYSIS IN ECONOMIC EDUCATION RESEARCH William E. Becker, William H. Greene and John J. Siegfried* As discussed in Modules One and Two, most of the empirical economic education research is based on a “value-added,” “change-score” or a “difference-in-differences” model specifications in which the expected improvement in student performance from a pre-program measure (pretest) to its post-program measurement (posttest) is estimated and studied for a cross section of subjects. Other than the fact that testing occurs at two different points in time, there is no time dimension, as seen in the data sets employed in Modules One and Two. Panel data analysis provides an alternative structure in which measurements on the cross section of subjects are taken at regular intervals over multiple periods of time.i Collecting data on the cross section of subjects over time enables a study of change. It opens the door for economic education researchers to address unobservable attributes that lead to biased estimators in cross-section analysis.ii As demonstrated in this module, it also opens the door for economic education researchers to look at things other than test scores that vary with time. This module provides an introduction to panel data analysis with specific applications to economic education. The data structure for a panel along with constant coefficient, fixed effects and random effects representations of the data generating processes are presented. Consideration is given to different methods of estimation and testing. Finally, as in Modules One and Two, contemporary estimation and testing procedures are demonstrated in Parts Two, Three and Four using LIMDEP (NLOGIT), STATA and SAS.
    [Show full text]
  • Lectures On: Panel Data Analysis for Social Scientists, Given at the University of Bergen, October 2006
    Lectures on: Panel data analysis for social scientists, given at the University of Bergen, October 2006 You may find these lecture notes a useful complement to those I will use for EC968. They cover a wider range of topics and go at a slower pace, with less emphasis on technical issues. Steve Pudney Panel Data Analysis for Social Scientists University of Bergen Department of Sociology Department of Comparative Politics Steve Pudney Gabriella Conti ISER Aims of course • Introduce the distinctive features of panel data. • Review some panel data sets commonly used in social sciences. • Present the advantages (and limitations) of panel data, and consider what sort of questions panel data can(not) address. • Show how to handle and describe panel data. • Introduce the basic estimation techniques for panel data • Discuss how to choose (and test for) the right technique for the question being addressed. • Discuss interpretation of results 01/02/2007 (2) Structure of course (1) • 5 days × (3 hours lectures + 2 hour lab sessions ) • Lab sessions will illustrate concepts using Stata software (“industry standard” in survey-based applied work) • Main data will be from British Household Panel Survey (BHPS) • Focus is on understanding the concepts and applying them. • Full lecture slides on the web • Technical detail kept to a minimum but available in “appendices” 01/02/2007 (3) Structure of course (2) Day 1: Basics • What are panel data (examples)? • Why use panel data? • Handling panel data in Stata – some basic commands. • Patterns of observations in panel data (non-response and attrition) • Within and between variation • Transitions. • Cohort analysis Day 2: Statistical analysis • Inference using panel data: some identification issues unobservables.
    [Show full text]
  • Panel Data Econometrics: Theory
    Panel Data Econometrics This page intentionally left blank Panel Data Econometrics Theory Edited By Mike Tsionas Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom © 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.
    [Show full text]
  • A Three-Paper Dissertation on Longitudinal Data Analysis in Education and Psychology
    A Three-Paper Dissertation on Longitudinal Data Analysis in Education and Psychology Hedyeh Ahmadi Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 c 2019 Hedyeh Ahmadi All rights reserved ABSTRACT A Three-Paper Dissertation on Longitudinal Data Analysis in Education and Psychology Hedyeh Ahmadi In longitudinal settings, modeling the covariance structure of repeated measure data is essential for proper analysis. The first paper in this three-paper dissertation presents a survey of four journals in the fields of Education and Psychology to identify the most commonly used methods for analyzing longitudinal data. It provides literature reviews and statistical details for each identified method. This paper also offers a summary table giving the benefits and drawbacks of all the surveyed methods in order to help researchers choose the optimal model according to the structure of their data. Finally, this paper highlights that even when scholars do use more advanced methods for analyzing repeated measure data, they very rarely report (or explore in their discussions) the covariance structure implemented in their choice of modeling. This suggests that, at least in some cases, researchers may not be taking advantage of the optimal covariance patterns. This paper identifies a gap in the standard statistical practices of the fields of Education and Psychology, namely that researchers are not modeling the covariance structure as an extension of fixed/random effects modeling. The second paper introduces the General Serial Covariance (GSC) approach, an extension of the Linear Mixed Modeling (LMM) or Hierarchical Linear Model (HLM) techniques that models the covariance structure using spatial correlation functions such as Gaussian, Exponential, and other patterns.
    [Show full text]
  • Fixed Effects Models Versus Mixed Effects Models for Clustered Data
    Psychological Methods © 2018 American Psychological Association 2019, Vol. 24, No. 1, 20–35 1082-989X/19/$12.00 http://dx.doi.org/10.1037/met0000182 Fixed Effects Models Versus Mixed Effects Models for Clustered Data: Reviewing the Approaches, Disentangling the Differences, and Making Recommendations Daniel McNeish Ken Kelley Arizona State University University of Notre Dame Abstract Clustered data are common in many fields. Some prominent examples of clustering are employees clustered within supervisors, students within classrooms, and clients within therapists. Many methods exist that explicitly consider the dependency introduced by a clustered data structure, but the multitude of available options has resulted in rigid disciplinary preferences. For example, those working in the psychological, organizational behavior, medical, and educational fields generally prefer mixed effects models, whereas those working in economics, behavioral finance, and strategic management generally prefer fixed effects models. However, increasingly interdisciplinary research has caused lines that separate the fields grounded in psychology and those grounded in economics to blur, leading to researchers encountering unfamiliar statistical methods commonly found in other disciplines. Persistent discipline-specific preferences can be particularly problematic because (a) each approach has certain limitations that can restrict the types of research questions that can be appropriately addressed, and (b) analyses based on the statistical modeling decisions common in one discipline can be difficult to understand for researchers trained in alternative disciplines. This can impede cross-disciplinary collaboration and limit the ability of scientists to make appropriate use of research from adjacent fields. This article discusses the differences between mixed effects and fixed effects models for clustered data, reviews each approach, and helps to identify when each approach is optimal.
    [Show full text]
  • What Are Panel Data? Panel Data Are a Type of Longitudinal Data, Or Data Collected at Different Points in Time
    Introduction to Regression Models for Panel Data Analysis Indiana University Workshop in Methods October 7, 2011 Professor Patricia A. McManus Panel Data Analysis October 2011 What are Panel Data? Panel data are a type of longitudinal data, or data collected at different points in time. Three main types of longitudinal data: Time series data. Many observations (large t) on as few as one unit (small N). Examples: stock price trends, aggregate national statistics. Pooled cross sections. Two or more independent samples of many units (large N) drawn from the same population at different time periods: o General Social Surveys o US Decennial Census extracts o Current Population Surveys* Panel data. Two or more observations (small t) on many units (large N). o Panel surveys of households and individuals (PSID, NLSY, ANES) o Data on organizations and firms at different time points o Aggregated regional data over time This workshop is a basic introduction to the analysis of panel data. In particular, I will cover the linear error components model. WIM Panel Data Analysis October 2011| Page 1 Why Analyze Panel Data? We are interested in describing change over time o social change, e.g. changing attitudes, behaviors, social relationships o individual growth or development, e.g. life-course studies, child development, career trajectories, school achievement o occurrence (or non-occurrence) of events We want superior estimates trends in social phenomena o Panel models can be used to inform policy – e.g. health, obesity o Multiple observations on each unit can provide superior estimates as compared to cross-sectional models of association We want to estimate causal models o Policy evaluation o Estimation of treatment effects WIM Panel Data Analysis October 2011| Page 2 What kind of data are required for panel analysis? Basic panel methods require at least two “waves” of measurement.
    [Show full text]
  • Longitudinal and Panel Data: Analysis and Applications for the Social Sciences
    Longitudinal and Panel Data: Analysis and Applications for the Social Sciences by Edward W. Frees Longitudinal and Panel Data: Analysis and Applications for the Social Sciences Brief Table of Contents Chapter 1. Introduction PART I - LINEAR MODELS Chapter 2. Fixed Effects Models Chapter 3. Models with Random Effects Chapter 4. Prediction and Bayesian Inference Chapter 5. Multilevel Models Chapter 6. Random Regressors Chapter 7. Modeling Issues Chapter 8. Dynamic Models PART II - NONLINEAR MODELS Chapter 9. Binary Dependent Variables Chapter 10. Generalized Linear Models Chapter 11. Categorical Dependent Variables and Survival Models Appendix A. Elements of Matrix Algebra Appendix B. Normal Distribution Appendix C. Likelihood-Based Inference Appendix D. Kalman Filter Appendix E. Symbols and Notation Appendix F. Selected Longitudinal and Panel Data Sets Appendix G. References This draft is partially funded by the Fortis Health Insurance Professorship of Actuarial Science. © 2003 by Edward W. Frees. All rights reserved To appear: Cambridge University Press (2004) October, 2003 Longitudinal and Panel Data: Analysis and Applications for the Social Sciences Table of Contents Table of Contents i Preface vi 1. Introduction 1.1 What are longitudinal and panel data? 1-1 1.2 Benefits and drawbacks of longitudinal data 1-4 1.3 Longitudinal data models 1-9 1.4 Historical notes 1-13 PART I - LINEAR MODELS 2. Fixed Effects Models 2.1 Basic fixed effects model 2-1 2.2 Exploring longitudinal data 2-5 2.3 Estimation and inference 2-10 2.4 Model specification
    [Show full text]
  • A Primer for Panel Data Analysis by Robert A. Yaffee September 2003
    A Primer for Panel Data Analysis by Robert A. Yaffee September 2003 Updated April 2005 Acknowledgement: I would like to thank Kate Monahan and Jill Hochberg of NYU ITS, and Chunling Lu of Harvard University for their invaluable editorial assistance. Introduction Panel data analysis is an increasingly popular form of longitudinal data analysis among social and behavioral science researchers. A panel is a cross-section or group of people who are surveyed periodically over a given time span. First, we will consider a small sample of panel data analytic applications in the social sciences. Then we will address the data structure for panel analysis. Principal models of panel analysis will be summarized, along with some of their relative advantages and disadvantages. We will discuss a test to determine whether to use fixed or random effects models. After a synopsis of methods of estimations tailored to different situations, we will conclude with a brief discussion of popular software capable of performing panel analysis. Some Applications of Panel Analysis Panel data analysis is a means of studying a particular subject within multiple sites, periodically observed over a defined time frame. Within the social sciences, panel analysis has enabled researchers to undertake longitudinal analyses in a wide variety of fields. In economics, panel data analysis is used to study the behavior of firms and wages of people over time. In political science, it is used to study political behavior of parties and organizations over time. It is used in psychology, sociology and health research to study characteristics of groups of people followed over time.
    [Show full text]