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Roland Füss

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University of St.Gallen
University of St.Gallen

Course location

University of St.Gallen

Home university

University of St.Gallen
Roland-Füss
Roland Füss is Professor of Real Estate Finance at the University of St.Gallen and Member of the Board of the Swiss Institute for Banking and Finance s/bf-HSG and of the School of Finance. In addition, he is a Research Associate at the Centre for European Economic Research (ZEW), Mannheim, Germany. He studied economics at the University of Freiburg, Germany, from which he also obtained his doctoral degree and «habilitation». Roland Füss teaches courses in Statistics, Financial Econometrics, Real Estate Economics, and Real Estate Finance. His main research topics are in the field of empirical asset pricing, real estate finance and economics, risk management, and applied financial econometrics. His research has been published in economics and finance journals such as Journal of Financial and Quantitative Analysis, Review of Finance, Journal of Economic Dynamics and Control, and Journal of Money, Credit and Banking.

Courses taught by this instructor

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Instructor

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B = Basic
M = Intermediate
A = Advanced

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Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

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Regression Analysis for Spatial Data

This course focuses on the visualization and modeling of spatial data. Examples are taken from different research areas such as political science, empirical international trade, criminology, and real estate. It offers a detailed explanation of individual estimation methods and their implementation in R. In this course, students will learn ‑ How to generate a variety of different maps that visualize the location of spatial units ‑ How maximum likelihood estimation works and how to set up and optimize a likelihood function in R ‑ How to deal with computational problems that are frequently accounted when working with spatial data ‑ How to increase computation speed using concentrated maximum likelihood and the matrix exponential spatial specification model ‑ How to estimate a spatial regression model both, with cross‑sectional and with time‑series data ‑ How to properly interpret the output from a spatial regression model and how to investigate policy interventions. ‑ A basic background on spatial interaction models, heterogeneous coefficient SAR models, and spatio‑temporal models
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Regression Analysis for Spatial Data

This course focuses on the visualization and modeling of spatial data. Examples are taken from different research areas such as political science, empirical international trade, criminology, and real estate. It offers a detailed explanation of individual estimation methods and their implementation in R. In this course, students will learn ‑ How to generate a variety of different maps that visualize the location of spatial units ‑ How maximum likelihood estimation works and how to set up and optimize a likelihood function in R ‑ How to deal with computational problems that are frequently accounted when working with spatial data ‑ How to increase computation speed using concentrated maximum likelihood and the matrix exponential spatial specification model ‑ How to estimate a spatial regression model both, with cross‑sectional and with time‑series data ‑ How to properly interpret the output from a spatial regression model and how to investigate policy interventions. ‑ A basic background on spatial interaction models, heterogeneous coefficient SAR models, and spatio‑temporal models
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