Search result: Catalogue data in Autumn Semester 2024
Agricultural Sciences Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Major in Agriculture Economics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Methods in Agricultural Economics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-0305-00L | Empirical Methods in Management | W+ | 3 credits | 2G | S. Tillmanns | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this class, students learn how to understand and conduct empirical research. It will enable them to manage a business based on evident-based decision-making. The class includes assignments related to the lecture content. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The general objective of the course is to enable students to understand the basic principles of empirical studies. After successfully passing the class, they will be able to formulate research questions, design empirical studies, and analyze data by using basic statistical approaches. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Data has become an important resource in today’s business environment, which can be used to make better management decisions. However, evidence-based decision-making comes along with challenges and requires a basic understand of statistical approaches. Therefore, this class introduces problems and key concepts of empirical research, which might be qualitative or quantitative in its nature. Concerning qualitative research, students learn how to conduct and evaluate interviews. In the area of quantitative research, they learn how to apply measurement and scaling methods and conduct experiments. In addition, basic statistical analyses like a variance analysis and how to conduct it in a standard statistical software package like SPSS or R are also part of the lecture. The lessons learned from the lecture will empower students to critically assess the quality and outcomes of studies published in the media and scientific journals, which might form a basis of their managerial decision-making. We recommend the lecture also to students without basic statistical skills, who plan to attend more advanced lectures in the field of artificial intelligence such as Marketing Analytics. The lecture will be taught in presence. There will be individual assignments that students have to solve throughout the lecture. In addition to that, there will be some non-mandatory online exercises as an additional opportunity to prepare for the exam. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature and readings will be announced. For a basic understanding we recommend the Handbook of Good Research by Jürgen Brock and Florian von Wangenheim. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course includes out-of-class assignments to give students some hands-on experience in conducting empirical research in management. Projects will focus on one particular aspect of empirical research, like the formulation of a research question or the design of a study. Assignments will be graded and need to be turned-in on time as they will be shown and discussed in class. Class participation is encouraged and can greatly improve students' learning. In this spirit, students are expected to attend class regularly and come to class prepared. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-0585-00L | Intermediate Econometrics | W+ | 3 credits | 2V | G. Masllorens Fuentes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The aim of the course is to discuss different econometric models and their empirical applications. We will cover cross-sectional linear and non-linear regression models, models for estimating treatment effects, and linear panel data models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | By the end of the course, students should understand the different existing approaches, their applicability, and their advantages and disadvantages. They should be able to read and understand regression output tables. Additionally, students will be able to apply the estimation approaches in practice using STATA. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The lectures will consist of both theoretical and practical components. In the theoretical part, we will discuss each estimation approach in detail. The lecture will present the assumptions, derivations, as well as the advantages and disadvantages of the estimation approach. In the empirical part, we will look at simulation results using artificial data. Furthermore, we will investigate a particular research question using STATA. The course will tentatively cover the following subjects: - review of ordinary least squares (OLS) estimation - instrumental variable estimation and two-stage least squares estimation - seemingly unrelated regression models - simultaneous equation models - maximum likelihood estimation - binary response models - count data models - censored and truncated regression models - sample selection models - treatment effect models - static linear panel data models (random effects and fixed effects estimation) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | For the theoretical portions of the lectures, we will prepare slides for in-class discussion. The format of the course is in-person. Slides will be distributed electronically before each lecture. For the applied portion of the lectures, we will provide STATA do files, log files, and data sets. Problem sets will also be made available after every lecture. These problem sets will not be collected or graded, but students can use them in order to prepare for the final exam. Solutions will be made available in the following lecture. While there is no required textbook for the course, we draw from the following texts, which are also recommend for the preparation of the exam: - Wooldridge, J.M. (2015). Introductory Econometrics. - Wooldridge, J.M. (2010). Econometrics of Cross Section and Panel Data. - Cameron, A.C. and P. Trivedi (2005). Microeconometrics. Methods and Applications. - Cameron, A.C. and P. Trivedi (2009). Microeconometrics Using Stata. - Angrist, J.D. and Pischke, J.-S. (2009). Mostly Harmless Econometrics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Jeffrey M. Wooldridge: Introductory Econometrics; Jeffrey M. Wooldridge: Econometric Analysis of Cross Section and Panel Data; A. Colin Cameron and Pravin K. Trivedi. Microeconometrics: Methods and Applications. Joshua A. Angrist and Jörn-Steffen Pischke: Mostly Harmless Econometrics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prior basic knowledge of matrix algebra and probability theory is strongly recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 751-0423-00L | Risk Analysis and Risk Management in Agriculture | W+ | 3 credits | 2G | R. Finger, J. Schmitt | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Agricultural production is exposed to various risks and risk management is indispensable. This course introduces modern concepts on farmers' decision making under risk and risk management. We present innovative insights, emprical example from European agriculture. You gain hands-on experience using R. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | -to develop a better understanding of decision making under uncertainty and risk; - gain hands-on experience in risk analysis and management using R -to gain experience in different approaches to analyze risky decisions; -to develop an understanding for different sources of risk in agricultural production; -to understand the crucial role of subjective perceptions and preferences for risk management decisions; -to get an overview on risk management in the agricultural sector, with a particular focus on insurance solutions -to get insights in the role of big data and machine learning for agricultural risk management | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - Quantification and measurement of risk - Risk preferences, Expected Utility Theory, Cumulative Prospect Theory - Production and input use decisions under risk - Portfolio Theory and Farm Diversification - Forwards, Futures, Crop Insurance - Weather Index Insurance and Satellite Imagery - Big data and machine learning for agricultural risk management - Empirical Applications using R | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handouts will be distributed in the lecture and available on the moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | knowledge of basic concepts of probability theory and microeconomics | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 751-1573-00L | Dynamic Simulation in Agricultural and Regional Economics | W+ | 3 credits | 2V | B. Kopainsky | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this class, students learn the basics of system dynamics and its application to agricultural and regional economic questions. In the second half of the class, students develop their own simulation model, with which they evaluate potential interventions for improving the economic as well as the ecological sustainability of food systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | - Students learn the basic theory and practice of dynamic simulation - Students can develop, analyze and extend a dynamic simulation model and interpret its results. - By applying the developed simulation model, students gain insights into food system issues. They also learn to recognize the benefits and pitfalls of dynamic simulation, both from a theoretical and an applied perspective. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | slides (will be provided during the class) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | articles and papers (will be provided during the class) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 363-0541-00L | Economic Dynamics and Complexity | W | 3 credits | 3G | F. Schweitzer, L. Verginer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | What causes economic business cycles? How are limited resources, competition, and cooperation reflected in growth dynamics? To answer such questions, we combine macroeconomic models and methods of nonlinear dynamics. We study the role of bifurcations and control parameters for dynamic stability. Feedback cycles and coupled dynamics are reasons for limited predictability, instability and chaos. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | successful participant of the course is able to: - understand the importance of different modeling approaches - formalize and solve one- and two-dimensional nonlinear models - identify critical conditions for stability and dynamic transitions - analyze macroeconomic models of business cycles, supply and demand - apply formal concepts to model economic growth and competition | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | System theory sees the economy as a complex adaptive system. What does this mean for economic modeling? We focus on two sources of complexity: (a) nonlinear dynamics, which is captured in this course, "Economic Dynamics and Complexity" and (b) collective interactions, which is captured in the course "Agent-Based Modeling of Economic Systems" (in Spring). Our approach to economic dynamics combines insights from different disciplines: macroeconomics studying business cycles and growth, system dynamics rooted general system theory and cybernetics, and nonlinear dynamics using applied mathematics. We start with a comparison of different modeling approaches, to highlight the problems and challenges of system modeling. The subsequent lectures then introduce different one- and two-dimensional nonlinear models with applications in economics, such as models of supply and demand, business cycles, growth and competition. Emphasis is on the formal analysis of these models using methods from applied mathematics and tools for solving coupled differential equations. Weekly self-study tasks are used to apply the concepts introduced in the lectures. We practice how to solve nonlinear models formally and numerically and how to interpret the results. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The lecture slides are provided as handouts - including notes and literature sources - to registered students only. All material is to be found on the Moodle platform. More details during the first lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students should be familar with nonlinear differential equations and should have basic programming skills. All necessary details to solve nonlinear models will be provided in the course. The course will not build on mathematical proofs, optimization, statistics, efficient numerical computation and other specialized skills. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 401-0647-00L | Introduction to Mathematical Optimization | W | 5 credits | 2V + 1U | D. Adjiashvili | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Topics covered in this course include: - Linear programming (simplex method, duality theory, shadow prices, ...). - Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...). - Modelling with mathematical optimization: applications of mathematical programming in engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Information about relevant literature will be given in the lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This course is meant for students who did not already attend the course "Linear & Combinatorial Optimization", which is a more advance lecture covering similar topics. Compared to "Linear & Combinatorial Optimization", this course has a stronger focus on modeling and applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-0565-00L | Principles of Macroeconomics | W | 3 credits | 2V | J.‑E. Sturm, E. Baselgia | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course examines the behaviour of macroeconomic variables, such as gross domestic product, unemployment and inflation rates. It tries to answer questions like: How can we explain fluctuations of national economic activity? What can economic policy do against unemployment and inflation? | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This lecture will introduce the fundamentals of macroeconomic theory and explain their relevance to every-day economic problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course helps you understand the world in which you live. There are many questions about the macroeconomy that might spark your curiosity. Why are living standards so meagre in many African countries? Why do some countries have high rates of inflation while others have stable prices? Why have some European countries adopted a common currency? These are just a few of the questions that this course will help you answer. Furthermore, this course will give you a better understanding of the potential and limits of economic policy. As a voter, you help choose the policies that guide the allocation of society's resources. When deciding which policies to support, you may find yourself asking various questions about economics. What are the burdens associated with alternative forms of taxation? What are the effects of free trade with other countries? How does the government budget deficit affect the economy? These and similar questions are always on the minds of policy makers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The course Moodle page contains announcements, course information and lecture slides. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The set-up of the course will closely follow the book of N. Gregory Mankiw and Mark P. Taylor (2023), Economics, Cengage Learning, 6th Edition. This book can also be used for the course '363-0503-00L Principles of Microeconomics' (Filippini). Besides this textbook, the slides, lecture notes and problem sets will cover the content of the lecture and the exam questions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-1017-00L | Risk and Insurance Economics | W | 3 credits | 2G | H. Schernberg | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course covers the economics of risk and insurance, in particular the following topics will be discussed: 2) individual decision making under risk 3) models of insurance demand, risk sharing, insurance supply 4) information issues in insurance markets 5) advanced topics in microeconomics and behavioral economics 5) the macroeconomic role of insurers and insurance regulation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course introduces students to basic microeconomic models of risk attitudes and highlight the role insurance can – or cannot – play for individuals facing risks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Everyday, we take decisions involving risks. These decisions are driven by our perception of and our appetite for risk. Insurance plays a significant role in people's risk-management strategies. In the first part of this lecture, we discuss a normative decision concept, Expected Utility theory, and compare it with empirically observed behaviour. Students then learn about the rationale for individuals to purchase insurance, and for companies to offer it. We derive the optimal level of insurance demand and discuss how it depends on our model's underlying assumptions. We then discuss the consequences of information asymmetries in insurance markets and the consequences for insurance supply. Finally, we discuss refinements in decision theory that help account for observed behaviours that don't fit with the basic models of microeconomic theory. For example, we'll explore how behavioural economics can be leveraged by the insurance industry. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Main literature: - Zweifel, P., & Eisen, R. (2012). Insurance Economics. Springer. - Handbook of the Economics of Risk and Uncertainty, Volume1; Further readings: - Dionne, G. (Ed.). (2013). Handbook of Insurance (2nd ed.). Springer. References will be given on a topic-by-topic basis during the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-1137-00L | Applied Econometrics in Environmental and Energy Economics  Does not take place this semester. It is highly recommended to take 363-0570-00L Principles of Econometrics first. | W | 3 credits | 2V | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course introduces to the most common empirical methods for the analysis of issues in environmental, energy, and resource economics. The course includes computer laboratory sessions, and covers the following broad topics: demand models, discrete choice models, empirical methods in policy evaluation, field- and quasi-experiments. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | At the end of the course, the students will be able to: understand the most common empirical methodologies used in environmental, energy, and resource economics; understand the problems the methodologies learnt in class aim to address; appreciate the importance of causal inference in empirical economics; read and understand the research papers in the literature; apply the empirical methods learnt in class using the software R. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course introduces students to empirical statistical methods that have wide application in environmental, energy, and resource economics and it is divided in four blocks. The first block is a quick review of the basic econometric methodology and concepts (OLS, standard errors, logit/probit models); the second block introduces demand models like the Almost Ideal Demand System, discrete choice models, and their evolutions; the third block explores causal inference in empirical economics and the main reduced-form econometric techniques used in policy evaluation, such as difference-in-differences, regression discontinuity and synthetic control; the fourth block introduces field experiments and instrumental variables, and their characteristics. At the end of each block there will be a computer laboratory class in which the student will learn to apply the methodologies learnt in class using the statistical open-source software R. Throughout the course, students will have the chance to work on actual data used for analysis in economics papers. The lectures will make use of current research papers in the literature to illustrate practical examples in which the methodologies learnt in class have been used. Students will be expected to read in advance the paper that will be explained during the lecture. The evaluation policy has the aim to allow students to get practical experience on the econometric methodologies learnt in class. Thus, beyond a final open-book computer exercise exam {60% of the grade), the course includes short takehome computer exercises {40% of the grade). As the course will be centered on econometric methods, it is recommended that students have taken 363-0570-00L Principles of Econometrics first, or have otherwise a solid knowledge of basic econometric methodologies as detailed in Part 1 of Wooldridge, Jeffrey M. (2018) lntroductory Econometrics : A Modern Approach. Seventh ed. ISBN: 978-1-337-55886-0. Knowledge of statistical software R is helpful, but not required and will be taught in the computer laboratory sessions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | It is highly recommended to take 363-0570-00L Principles of Econometrics first. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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