Overview.

Bayesian methods are increasingly used in econometrics, particularly in the field of macroeconomics. This is a course in Bayesian econometrics with a focus on the models used in empirical macroeconomics. It begins with a brief introduction to Bayesian econometrics, describing the main concepts underlying Bayesian theory and showing how Bayesian methods work in the familiar context of the regression model. Computational methods are of great importance in modern Bayesian econometrics and these are discussed in in detail. In light of the Big Data revolution, applied economists often face the situation where the number of variables under consideration is large relative to the number of observations and conventional econometric methods do not work well. We describe various methods that can be used with Big Data in the context of the regression model and emphasize the wider applicability of these methods in other modelling contexts. Subsequently, the course shows how Bayesian methods are used with models which are currently popular in macroeconomics such as Vector Autoregressions, state space models (including factor models and stochastic volatility models) and nonparametric methods such as regression trees.

Overview.

This is a course in advanced time series econometrics with a focus on the models used in macroeconomics and finance. It will cover four main classes of models: i) state space models, ii) nonlinear time series models, iii) factor models and iv) models for use with mixed frequency data. The focus of the course will be on using these models in practice. To this end, the lectures will discuss the models and their properties and show how to estimate and forecast with them using Stata. In addition to 14 hours of lectures, the course will include four hours of computer labs where students will gain experience in working with these models. The course will be assessed through a final exam.

Overview.

The abundance of time series data in economics and finance has sparked a growing interest in flexible data analytical techniques that efficiently handle data and identify meaningful patterns in time series. This module primarily focuses on statistical and machine learning tools that are increasingly used by practitioners, econometricians, data analysts and forecasters in central banks, government statistical agencies, policy institutions, or the private sector. It is designed to provide students with an in-depth understanding and application of advanced time series econometrics by covering topics such as linear and non-linear time series regressions, textual inference, as well as popular machine and statistical learning techniques in macroeconomics and finance. The module establishes a strong and closer link between students and the advanced and modern time series econometric tools used by data scientists and analysts for inference in macroeconomics and finance.

List of lectures and lab sessions.

1. Recap of linear time series data analytics
2. Text as data: Textual inference in macroeconomics and finance
3. Non-linear time series data analytics I: Markov switching
4. Non-linear time series data analytics II: Time-varying parameter regressions and volatility modeling
5. Non-linear time series data analytics III: Machine learning techniques using tree-based methods and deep learning techniques

Overview.

This module primarily focuses on analysing and modeling macroeconomic and financial time series. It is intended to provide students with a comprehensive introduction to time series and the application of basic time series econometrics in macroeconomics and finance. This is achieved through consideration of such topics as the introduction to univariate and multivariate time series modeling, local projections, vector autoregression, and factor models. The module will provide the necessary basic knowledge and fundamental tools for modeling time series, which can be used to conduct individual structural policy as well as scenario analysis in macroeconomics and finance.

List of lectures and lab sessions.

1. Basics in mathematics and statistics and Introduction to R
2. Introduction to time series data analytics: Autoregressive distributed lag model (ARDL) and local projections (LPs)
3. Multivariate time series data analytics: Estimation of vector autoregressions (VARs)
4. Structural identification in macroeconomics using LPs and VARs
5. Explorative pattern recognition methods in panel data: Principal components, static and dynamic factor models

Overview.

This class aims to provide students with the required tools and analytical frameworks (models) to understand contemporary macroeconomic issues, such as: decision marking of inflation targeting central banks, the interactions between the banking sector and monetary policy, policy responses to the global financial crisis, the Covid-19 pandemic, and the resurgence of inflation and developments in the labour market.

List of lectures.

1. The Demand Side
2. The Supply Side
3. The 3-Equation Model and Macroeconomic Policy
4. Expectations and Monetary Policy
5. Money, Banking and Sources of Financial Sector Instability
6. Fiscal Policy
7. Unconventional Monetary Policy
8. Models of the Labour Market & Unemployment Hysteresis
9. Toward Modern, Microfounded, Heterogeneous Agent Models

Overview.

The overarching aims of this class are: to extend students' knowledge and understanding of core macroeconomic models and of the econometric methods used to analyse macroeconomic data; and to develop their ability to apply these models and methods to address some key questions in macroeconomics. The class covers: economic growth; expectations; global macroeconomic issues; and policymaking and implementation. In addition, econometric methods covered include: univariate and multivariate regression with time series data; unit root testing; cointegration and error correction.

List of macroeconomics lectures.

1. Facts of growth
2. Solow model without technological progress
3. Solow model with technological progress
4. Expectations, consumption, and investment
5. The Euro
6. Covid-19
7. High debt
8. Policy and policy makers
9. Fiscal policy rules
10. Monetary policy rules

List of time series econometrics lectures.

1. Growth statistics and simple regression
2. Stationarity and unit roots
3. Cointegration
4. Volatility modeling

Overview.

This class teaches quantitative methods with a practical emphasis. Students will gain an understanding of aspects of econometric theory and of fundamental econometrics tools that are routinely used in the analysis of economic data. They will apply the methods they learn about to a variety of models and data sets used in economics and finance. On completion of the class students should be in a position to motivate and conduct their own empirical work, and they will have the opportunity to demonstrate this in their summer projects.

List of lectures.

1. Descriptive statistics
2. Simple regression models
3. Multiple regression models
4. Hypothesis testing and hetereoskedasticity
5. Time series econometrics I
6. Time series econometrics II
7. Instrumental variables
8. Limited dependent variable models
9. Regression discontinuity design
10. Panel data

Each topic is introduced in lectures and consolidated through practical applications in lab sessions.

Introduction to Bayesian econometrics with R, multivariate time series analysis for macroeconomics, Big Data issues.

Advanced Bayesian econometric methods, non-linear vector autoregressions, impulse response analysis, conditional forecasting, structural identification.

Introduction to econometrics with R.

Introduction to Bayesian econometrics with R, time series analysis.

Basic set theory and algebra, functions and relations, differential and integral calculus.

Introduction to the economics of the public sector.