This module is a core module for the MSc in Business Analytics. It covers a range of techniques widely used in modern applied econometric work, and demonstrates how these techniques are applied to specific areas of economic enquiry using real-world datasets.

This module covers a range of techniques widely used in modern applied econometric work, and demonstrates how these techniques are applied to specific areas of economic enquiry. 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. By the end of the module, students should be able to read and understand the main lines of argument in a number of contemporary published empirical econometric studies on the topics covered. They will also be in a position to use appropriate computer software to conduct econometric analysis on these and related topics. Syllabus: 1. Introduction to a two-variable regression analysis, economic data types and R-software; 2. The two-variable regression analysis: OLS derivation and assumptions, Goodness-of-fit and hypothesis testing; 3. Multiple regression analysis: the problem of estimation; 4. Regression modelling issues and further model specification criteria, non-linear relationships, and dummy variable regression models; 5. Cross-sectional data analysis: detecting and correcting for heteroscedasticity; 6. Time-series analysis I: definition and characteristics of time-series data and applications, detecting and correcting for autocorrelation; 7. Simultaneous-equation methods: endogeneity sources and 2SLS regression; 8. Time-series analysis II and forecasting: smoothing techniques, ARIMA models, Vector Autoregressive (VAR) Model, model time-series volatility for financial and business data, ARCH/GARCH modelling; 9. Panel data models: fixed-effects and random-effects models.

On successful completion of this module, students will be able to: Formulate suitable econometric models for the empirical study of economic phenomena. Organise and measure real-world data for the purpose of econometric analysis. Estimate econometric models using suitable software. Critically evaluate the results of econometric analysis and derive conclusions.

On successful completion of this module, students will be able to: Justify the context in which a problem is to be addressed. Appreciate the role and relevance of econometrics in applied research. Question the limitations of the applicability of econometrics in business and economic science.

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This module will be taught through a combination of lectures and practical computer laboratory classes, as well as private study hours. Students are expected to take responsibility for their own independent learning, and should be pro-active in their engagement with the learning materials. The University of Limerick Graduate Attributes will be developed by: Broadening students' knowledge and proactivity through encouraging independent and directed research; emphasising student's responsibility to organise their time in an efficient manner and to work to specific deadlines. The module will draw from published research in the relevant topics to help extend student's understanding.