Economics (all)

Prerequisites: solid understanding of Basic Statistics and Basic Econometrics

Goals and contents of the module: In large part, economic data is based on time series, which is data collected on the same observational unit at multiple time periods (e. g. yearly, quarterly or monthly). Analyzing time series data requires specific statistical models and methods, which are usually not taught in basic statistics and basic econometrics courses. Subject of this course is to provide an overview about the most important standard methods for describing and analyzing time series data. Thereby the main focus is on the practical application of forecasting methods. The Statistical Software R will intensively be used upon many real data examples. Contents: Introduction to TSA, Review of Basic Essentials, Basic Elements of TSA, Basic Properties of Time Series, Forecasting Theory, AR(I)MA Processes, ADL- and VAR-Models, Nonstationarity, Estimation of Dynamic Causal Effects, Additional Topics in TSA

Expected competences acquired after completion of the module: At the end of the semester students

• know and understand most common TSA methods and their theoretical background
• know how to construct forecasting models, how to conduct model based forecasts and how to check model performance
• can proficiently use R for all important parts of TSA: constructing graphics, estimating and testing, forecasting, model diagnosis and assessment
• have experienced the possibilities and limitations of time series methods on the basis of real data examples

Prerequisites: solid understanding of Basic Statistics and Basic Econometrics

Goals and contents of the module: In large part, economic data is based on time series, which is data collected on the same observational unit at multiple time periods (e. g. yearly, quarterly or monthly). Analyzing time series data requires specific statistical models and methods, which are usually not taught in basic statistics and basic econometrics courses. Subject of this course is to provide an overview about the most important standard methods for describing and analyzing time series data. Thereby the main focus is on the practical application of forecasting methods. The Statistical Software R will intensively be used upon many real data examples. Contents: Introduction to TSA, Review of Basic Essentials, Basic Elements of TSA, Basic Properties of Time Series, Forecasting Theory, AR(I)MA Processes, ADL- and VAR-Models, Nonstationarity, Estimation of Dynamic Causal Effects, Additional Topics in TSA

Expected competences acquired after completion of the module: At the end of the semester students

• know and understand most common TSA methods and their theoretical background
• know how to construct forecasting models, how to conduct model based forecasts and how to check model performance
• can proficiently use R for all important parts of TSA: constructing graphics, estimating and testing, forecasting, model diagnosis and assessment
• have experienced the possibilities and limitations of time series methods on the basis of real data examples

Veranstaltungsort und -zeit:
Einführungsveranstaltung: 14.02.2022, 17:15 Uhr bis 18:00 Uhr, via Zoom.

Blockseminar: 19.05.2022 und 20.05.2022, je 08.30 Uhr bis 17.30 Uhr, Ort wird noch bekanntgegeben.

• Our first goal is to introduce the canonical models in international trade. A tentative list of topics includes the gravity equation, neoclassical trade theory, trade and labor markets, economic geography, and the role of firms in international trade. 
• The second goal is to present the empirical tools used in international trade. A tentative list of topics includes the US-China trade war, trade and labor market, and the gravity equation. Students will also get familiar with several widely used trade-related datasets and learn how to conduct empirical analysis.
• Our third goal to introduce frontier researches to students. We will draw on some recent academic papers from international trade, which will allow students to have a good understanding of cutting-edge researches and help students outline future research questions.