Advanced Econometrics

ECON31031 Advanced Econometrics 20 Semester 1 Undergraduate Level 3 Simon Peters Unit coordinator Economics 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 In this unit students will progress their development of econometrics knowledge and skills continuing from Year 2 Econometrics. Students will learn about the important method of maximum likelihood estimation which will open up estimation for a large class of models. Students will also learn about the main building blocks of time-series modelling which will allow them to continue on to the studies of more advanced methods.  In this unit students will progress their development of econometrics knowledge and skills continuing from Year 2 Econometrics. Students will learn about the important method of maximum likelihood estimation which will open up estimation for a large class of models. Students will also learn about the main building blocks of time-series modelling which will allow them to continue on to the studies of more advanced methods.  To extend the material introduced in Econometrics to; a) cover models used for the analysis of microdata (particularly data with limited dependent responses), and to; b) introduce models for the analysis of macro-economic time series data.  Students should be able to:<ol> <li>understand the use of the method of Maximum Likelihood to estimate and test IID econometric models,</li> <li>extend the range of their microdata modelling capabilities to deal with non-linear, particularly binary, response variables,</li> <li>understand stationary time series processes</li> <li>formalize the notion of non-stationarity and test for its presence,</li> <li>use VARs to model multivariate time series.</li></ol>  Understand the theory and practice of Maximum Likelihood estimation and how it is applied in a variety of different modelling contexts Understand the importance of time-series properties (like stationarity) and testing for these properties in the context of time-series modelling  Develop a solid, intuitive and theoretical grasp of the dangers, pitfalls and problems encountered in applied modellingCritically analyse, appraise and interpret work in the area of applied economics  Have practical experience of the application of econometric methods based on practical exercises using economic datasetsFurther develop statistical software skills  Select and deploy relevant information and use these effectively in the written communication of ideas Other Problem solving, Synthesis and analysis of data and information, Numeracy, Time Management. Use of industry standard software. ProvisionalPart I: Theory and Microdata models1. Theory: Parametric Maximum Likelihood for IID data.2. Binary Response Data modelsSoftware: RPart II: Time series (Macrodata) models.3. stationary time series processes4. Unit roots.5. Introduction to VARs Software: R  Synchronous activities (such as Lectures or Review and Q&A sessions, and tutorials), and guided self-study 0 Other 100% <div>Formative assessment:Tutorial questionsSoftware practice tasksSummative assessment:25% Coursework 1 (1000 words)70% Exam (2h)</div>The assessment criteria used by Economics in the assessment of examinations and coursework can be found on the <a href="http://www.socialsciences.manchester.ac.uk/student-intranet/">UG Intranet</a> in your programme handbook (BSc Economics, BA(ECON)  In tutorial discussions and solutions providedOn-script and group level feedback ECON20110 Econometrics Pre-Requisite Compulsory ECON20110ECON20110 Y While there is a core text, other references (journals articles as well as texts) maybe be cited as appropriate.Core text: Verbeek (2017), A Guide to Modern Econometrics. (earlier editions may also be suitable).  0 0 0