Centre for Development, Environment and Policy P101 Applied Econometrics Prepared by Francesca Di Nuzzo This module is partially based on the earlier module ‘Applied Econometrics for the Agricultural and Food Sector’ prepared for the University of London’s External Programme by Alison Burrell. © SOAS | 3740 Applied Econometrics Module Introduction ABOUT THIS MODULE This module is about econometric methods and how they are applied to estimate and test the unknown parameters of economic relationships. Priority is given to both the statistical reasoning underlying the methodology and the practical considerations involved in using this methodology with a variety of models and real data. The focus of the module is on the classical linear regression model. This is the basis for much econometric methodology and it provides the framework for organising the module. The module covers the principles of regression analysis and its statistical foundations the simple linear regression model the multiple linear regression model violations of the assumptions of classical linear regression There is a limit to the distance that can be covered in the study time available. In an econometrics module, the trade-off between breadth and depth is low since, without good groundwork and sufficient information at each stage, ideas may be misunderstood and techniques misapplied. This module follows the standard itinerary of most econometrics textbooks. The practical exercises designed to be done with the help of the free computer software package R are an important element of the module. © SOAS CeDEP 2 Applied Econometrics Module Introduction STRUCTURE OF THE MODULE Each unit in the study guide follows the same format. Each unit will always start with a section on ideas or issues, whose purpose is to explain, in simple words and with a minimum of technical notation, the basic substance of the unit. The aim is to give you an intuitive feel for the subject matter before going into technical detail. If you feel that mathematics and statistics is not your strongest suit, this regular section will give you a few ‘analytical handles’ to hold on to when studying relevant techniques. But even if you are confident with mathematics and statistics, it is important not to skip this section. Technical expertise is not just a question of one’s ability to work out the steps in a technical procedure or to understand a mathematical derivation. It also involves understanding the type of questions a technique tries to address and the assumptions on which it is based as well as judging the appropriateness of particular technical procedures in specific conditions. Next, the module units contain a Key Concepts section, which guides your study in further detail. The purpose of this section is to highlight the main concepts as well as to structure your reading of the textbook. Following on from this, you will find a section containing an example (except Units 6 and 10). The purpose of this section is twofold. First, the example highlights a specific aspect of the topic under study in a particular unit of the module. Second, the example also gives you a glimpse of econometrics in action. The examples aim to highlight the links between economic theory and empirical investigation, and to illustrate the problems that can arise when we work with real data. Often, you will find a set of self-assessment questions at the end of each section. It is important that you work through all of these. Their purpose is threefold: to check your understanding of basic concepts and ideas to verify your ability to execute technical procedures in practice to develop your skills in interpreting the results of empirical analysis. Also, you will find additional exercises at the end of each unit, which aim to give you hands-on examples of how to carry out empirical analysis; in many cases, you will be asked to work with actual data and to interpret results. Answers to the self- assessment questions are provided. For each unit (except in Units 6 and 10) you will find a guide that explains how to use R – the software package you will use to carry out econometric exercises. This guide will help you to master this particular econometrics software package. You may find it more convenient to study each R guide individually at the end of each unit (before the section ‘Unit Self-Assessment Questions’). © SOAS CeDEP 3 Applied Econometrics Module Introduction When studying each unit, we suggest that you study the text at your own pace and then work through the questions which are always designed to test your understanding of the unit material. If these questions reveal some weak spots, refresh them first before going on to the applied, data-based questions which you will need to answer in conjunction with the R guide. The Summary at the end of each unit briefly describes the topics covered, and the table of Key Terms and Concepts lists all the important concepts you should have learned through your study. Applied statistics and econometrics are subjects with a great deal of specialised jargon. It can be disconcerting to be faced with a number of unfamiliar new terms, many of them quite long and often rather similar to each other. We recommend very strongly that, right from the beginning of the module, you keep a glossary in which you list each new term as you encounter it, together with an explanation of the term in your own words. You should read through this glossary every few weeks, updating your definitions if you find that, as the module progresses, your understanding of the term develops along with your familiarity with the concept. © SOAS CeDEP 4 Applied Econometrics Module Introduction WHAT YOU WILL LEARN Module Aims The specific aims of the module are: To explain the principles of econometric estimation and its statistical foundations. To present the theory of the classical linear regression model and explain why the conditions in such a model provide an ideal environment for ordinary least squares regression. To develop practical skills of data analysis, use of regression techniques and interpretation of regression results. To explain the procedures of interval estimation and hypothesis testing in the classical normal linear regression model. To show how econometric models can be made more realistic through the use of dummy variables. To explain how linear restrictions can be imposed on parameters during estimation and how these restrictions can be tested. To investigate the consequences of heteroscedasticity of the disturbances and endogeneity of the regressors. To encourage an appreciation of what constitutes a ‘good’ econometric model, and how to test that a model is well specified. Module Learning Outcomes By the end of this module students should be able to: understand and selectively and critically apply the basic principles of regression analysis and statistical inference in the context of a single-equation regression model formulate a single-equation regression model, estimate its parameters, carry out a variety of tests relating to model specification and critically interpret all results test hypotheses about economic behaviour and critically interpret the results of these tests specify and interpret models using dummy variables, different types of dynamic specification and incorporate and test linear restrictions test for heteroscedasticity and endogeneity, and take appropriate action when these conditions are found to be present. © SOAS CeDEP 5 Applied Econometrics Module Introduction ASSESSMENT This module is assessed by: • an examined assignment (EA) worth 40% • a written examination worth 60%. Since the EA is an element of the formal examination process, please note the following: (a) The EA questions and submission date will be available on the Virtual Learning Environment (VLE). (b) The EA is submitted by uploading it to the VLE. (c) The EA is marked by the module tutor and students will receive a percentage mark and feedback. (d) Answers submitted must be entirely the student’s own work and not a product of collaboration. (e) Plagiarism is a breach of regulations. To ensure compliance with the specific University of London regulations, all students are advised to read the guidelines on referencing the work of other people. For more detailed information, see the FAQ on the VLE. © SOAS CeDEP 6 Applied Econometrics Module Introduction STUDY MATERIALS Except for Unit 9, for which separate material will be provided, the single textbook for the module is: Gujarati, D. & Porter, D. (2010) Essentials of Econometrics. 4th edition. International Edition. McGraw-Hill. This book has been chosen for this self-study module because of its attention to full explanations of concepts and procedures, its long introductory section presenting the basic statistical concepts used in regression modelling, and its avoidance of unnecessary algebra and difficult notation. You are also encouraged to read those parts that are not specifically identified in the module texts, since all the material here should be within your grasp and will reinforce your understanding of the subject. By the end of the module, you will know this textbook well and will be ready for other more advanced readings. The following are not compulsory; however, they are more advanced in terms of notation and explanations, should you wish to go into more depth about specific concepts. Please note that these texts are recommendations and are not provided. Intermediate Greene, W. (2000) Econometric Analysis. 4th edition. New Jersey, Prentice Hall. Gujarati, D. (1979) Basic Econometrics. Singapore, McGraw-Hill. Gujarati, D. (2011) Econometrics by Example. Palgrave Macmillan. Advanced Maddala, G.S. & Lahiri, K. (2009) Introduction to Econometrics. 4th edition. Chichester, John Wiley & Sons. Other Hallam, D. (1990) Econometric Modelling of Agricultural Commodity Markets. London, Routledge. Discussion of econometric modelling in agricultural economics. © SOAS CeDEP 7 Applied Econometrics Module Introduction For each of the module units, the following are provided.