79296 - Advanced Analysis of Data

Course Unit Page

Academic Year 2018/2019

Learning outcomes

By the end of the course the student will develop advanced expertise in formulating and implementing statistical approaches to practical problems in a wide variety of subject areas. To integrate material covered in various lecture courses with skills developed through practical work in order to solve real-world problems. By the end of this course students will be able to: - formulate questions of interest and identify relevant informal and formal statistical methodology for a wide variety of practical contexts; - implement the various stages of advanced statistical analysis appropriately in R; - interpret the output of R procedures; - critically collate results and conclusions; - present the main results and conclusions in the form of concise summaries; - present results of analyses in the form of written reports; - critically assess published applications of statistical analysis; - work independently on practical data analysis problems.

Course contents

First Module Contents (To Be Advised)

Second Module (proposed by Prof. Luke B Connelly)

Contents of the proposed statistical module

A statistical module that introduces students to econometric formulations, their estimation and interpretation to provide students with the ability to undertake analyses of non-experimental (or “observational”) data using modern econometric methods. Specific policy-relevant applications of these techniques to a wide range of topics in economics (e.g. education, labour markets, determinants of crime, health and health care).

In particular, at the end of this module, the student should be able to:

  • identify and apply modern econometric techniques to analyse complex datasets including panel (i.e., longitudinal) datasets;
  • analyse continuous and limited dependent variables, including unordered and ordered categorical variables;
  • characterise and identify specification and “identification” problems in applied econometric models, and to compute and interpret the coefficients and marginal effects (MEs) produced by those models
  • understand and employ commonly-used econometric model selection and specification criteria; and
  • to explain, in a way that is intelligible to a general audience, the results of policy-relevant applications of econometrics.
  • Applied Econometrics: Review
    • Review economic approaches to the analysis of non-experimental (observational) data: linear regression models in economics; typical econometric specifications; derivation and interpretation of marginal effects (MEs); common sources of bias; goodness of fit; the “identification” problem.

      Laboratory applications of linear regression to supplied datasets using Stata.

  • Econometric models with instrumental variables (IVs) limited dependent variables
    • Econometric formulations of logit and probit models and models for ordered and unordered dependent variables (ordered probit and logit; multinomial logit and probit); heteroscedasticity; specifications and data issues (including misspecification and detection of misspecification problems); model selection criteria and diagnostic testing.

    Laboratory implementation of IV and other estimation methods; estimation of limited dependent variable models and testing, using Stata.

  • Econometric panel (longitudinal) data models
    • Analysis of data with cross-section (x) and time-series (t) elements; unobserved heterogeneity; fixed- and random-effects panel data regression; extensions (e.g., count-data econometric models with panel datasets); diagnostic testing and model comparison.

Laboratory modelling of panel FE and RE models and implementation of diagnostic tests, using Stata.


Readings/Bibliography

Main References (Required Reading)

Wooldridge JM (2016) Introductory Econometrics, South-Western Cengage Learning: Mason (OH):

  • Chapter 13: Pooling Cross Sections Across Time: Simple Panel Data Models
  • Chapter 14: Advanced Panel Data Methods
  • Chapter 15: Instrumental Variables Estimation and Two Stage Least Squares
  • Chapter 16: Simultaneous Equations Models
  • Chapter 18: Limited Dependent Variable Models and Sample Selection Corrections
  • Chapter 19: Carrying Out an Empirical Project

Selected Sections (Supplementary Reading)

Cameron ACC and Trivedi P (2009) Microeconometrics Using Stata, Stata Press: New York.

Further reading (papers and reports)

Citations to additional papers will be provided in lecture and laboratory sessions. These additional sources are not required readings, but are provided as further reading for the interested student.

Teaching methods

Lectures with open discussion + Computer labs + Selected readings

Assessment methods

The exam will evaluate the achievements of the student through a direct assessment of the following learning outcomes:

  • ability to identify the most suitable methods required to carry out the analysis and interpretation of health-related data
  • ability to conduct a critical appraisal of the methodology adopted in a research project or scientific publication

The exam will consist of an in depth oral assessment concerning the ability to understand a problem and conduct an advanced statistical analysis in any of the fields of interest presented in the course. The student should be able to respond to questions regarding the choice of methods in different situations, with specific application in health research, including aspects related to data acquisition.

Teaching tools

Textbook, further readings, lectures, datasets, laboratory exercises, class discussion.

Office hours

See the website of Luke Brian Connelly

See the website of Cinzia Viroli