NDAB20006U Econometrics B (ØkB)

After completing the course the student is expected to be able to:

Knowledge:

• Understand the central assumptions in linear panel data model with unobserved effects; including how these assumptions are tested and how potential violation affect identification and estimation of parameters.
• Understand the specification, identification and estimation issues that arise in static and dynamic binary response model for panel data models with and without unobserved effects (e.g. scale and level normalization, state dependence, initial conditions, endogeneity, neglected heterogeneity). 
• Understand the specification, identification and estimation issues that arise in multinomial discrete choice models and their implied predictions (substitution patterns,  
• Understand the principle of M-estimation in terms of estimation and inference, as well as key examples of M-estimators such as maximum likelihood and non-linear least squares.  
• Understand the properties of simulation based estimators (consistency, asymptotic normality, rates of convergence and smoothness) and how they can be applied in the context of discrete choice models.
• Understand how the most common numerical optimizers and solution algorithm work in an estimation context.
• Understand the differences in goals, methods and settings between the machine learning methods and the traditional econometrics and statistics methods. 

Skills:

• Identify the characteristic properties of a given economic cross-sectional or panel data set, suggest and construct an appropriate econometric model and select a suitable estimation approach.
• Derive estimators of the statistical model’s parameters using the principles of M-estimation and estimate and interpret the parameters. This may involve deriving the sample objective function (such as the likelihood function), the gradient and the information matrix and implement the estimation method numerically using optimization methods.
• Construct misspecification tests, analyze to what extent an econometric model is congruent with the data, formulate economic questions as hypotheses on the parameters of the statistical model and test these hypotheses.
• Use an imperative programing language such as Python or MATLAB to implement the mathematical representation of the econometric models and estimation methods covered in the course. This involves computing parameter estimates that are typically not available in closed form as well as computation of partial effects, elasticities, standard errors, test statistics, and simulation of counterfactual predictions based on the estimated model.
• Present a statistical model and empirical results in a clear and concise way in a focused paper. This includes using statistic and econometric terms in a correct way, giving statistically sound and economically relevant interpretations of statistical results, and presenting results in a way so that they can be reproduced by others.

Competencies:

When faced with a new dataset and a given economic problem, students should be able to:

• Assess which estimator is best suited to address the problem.
• Develop arguments supporting an identification strategy.
• Program the estimator and estimate the parameters of the model.
• Test specification and economic hypotheses in their model.
• Report and interpret the results (marginal effects, elasticities, counterfactual simulations) 
• Assess the identification strategies in existing research papers as well as in their own analyses.
• Read and critically evaluate research papers containing applied econometric cross-section and panel data analyses.

The acquired skills in microeconometric theory and practice provide a strong background that enable students to do empirical analyses at a level suitable for the bachelor thesis, but also relevant for answering empirical economic questions that could be encountered in a government agency or in the private sector.