# Search result: Catalogue data in Autumn Semester 2020

Agricultural Sciences Master | ||||||

Major in Agriculture Economics | ||||||

Methodology Competences | ||||||

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 group assignments, where students will cover small parts of the lecture content in self-created videos. | |||||

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 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 auf their decision-making. We recommend the lecture also to students without basic statistical skill, who plan to attend more advanced lectures in the field of artificial intelligence such as Marketing Analytics. The lecture will be taught online this fall semester. Therefore, it involves group work, where students form groups in order to create small learning videos, which cover small parts of the lecture. These videos will be shown and discussed in the online lecture and will make up 30% of the final grade. Part of this assignment will be the evaluation of videos from other students. The preparation of the videos will also prepare students for the final exam. 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 undertanding we recommend the Handbook of Good Research by Jürgen Brock and Florian von Wangenheim. | |||||

Prerequisites / Notice | The course includes out-of-class assignments and projects 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. Students will form groups and create a learning video regarding one specific topic. Assignments will be graded and need to be turned-in on time as they will be shown and discussed in class. Students will also have to evaluate the videos of other student groups. Online 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. | |||||

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. | |||||

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) For the theoretical portions of the lectures, we will prepare slides for in-class discussion. 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. | |||||

751-0423-00L | Risk Analysis and Risk Management in Agriculture | W+ | 3 credits | 2G | R. Finger | |

Abstract | Agricultural production is exposed to various risks which are important for decisions taken by farmers and other actors in the agri-food sector. Moreover, risk management is indispensable for all actors. This course introduces modern concepts on decision making under risk and recent developments in risk management. The focus of this course in on agriculture applications. | |||||

Objective | -to develop a better understanding of decision making under uncertainty and risk; -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 | |||||

Content | - Quantification and measurement of risk - Risk preferences, expected utility theory and alternative models of risk behavior - Concepts on the decision making under risk - Production, investment and diversification decisions under risk - Risk management in agriculture | |||||

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 | |||||

751-1573-00L | Dynamic Simulation in Agricultural and Regional Economics | W+ | 2 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. | |||||

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 | Systems Dynamics and Complexity | W | 3 credits | 3G | F. Schweitzer | |

Abstract | Finding solutions: what is complexity, problem solving cycle. Implementing solutions: project management, critical path method, quality control feedback loop. Controlling solutions: Vensim software, feedback cycles, control parameters, instabilities, chaos, oscillations and cycles, supply and demand, production functions, investment and consumption | |||||

Objective | A successful participant of the course is able to: - understand why most real problems are not simple, but require solution methods that go beyond algorithmic and mathematical approaches - apply the problem solving cycle as a systematic approach to identify problems and their solutions - calculate project schedules according to the critical path method - setup and run systems dynamics models by means of the Vensim software - identify feedback cycles and reasons for unintended systems behavior - analyse the stability of nonlinear dynamical systems and apply this to macroeconomic dynamics | |||||

Content | Why are problems not simple? Why do some systems behave in an unintended way? How can we model and control their dynamics? The course provides answers to these questions by using a broad range of methods encompassing systems oriented management, classical systems dynamics, nonlinear dynamics and macroeconomic modeling. The course is structured along three main tasks: 1. Finding solutions 2. Implementing solutions 3. Controlling solutions PART 1 introduces complexity as a system immanent property that cannot be simplified. It introduces the problem solving cycle, used in systems oriented management, as an approach to structure problems and to find solutions. PART 2 discusses selected problems of project management when implementing solutions. Methods for identifying the critical path of subtasks in a project and for calculating the allocation of resources are provided. The role of quality control as an additional feedback loop and the consequences of small changes are discussed. PART 3, by far the largest part of the course, provides more insight into the dynamics of existing systems. Examples come from biology (population dynamics), management (inventory modeling, technology adoption, production systems) and economics (supply and demand, investment and consumption). For systems dynamics models, the software program VENSIM is used to evaluate the dynamics. For economic models analytical approaches, also used in nonlinear dynamics and control theory, are applied. These together provide a systematic understanding of the role of feedback loops and instabilities in the dynamics of systems. Emphasis is on oscillating phenomena, such as business cycles and other life cycles. Weekly self-study tasks are used to apply the concepts introduced in the lectures and to come to grips with the software program VENSIM. Another objective of the self-study tasks is to practice efficient communication of such concepts. These are provided as home work and two of these will be graded (see "Prerequisites"). | |||||

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 | |||||

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. | |||||

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 "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications. | |||||

363-0565-00L | Principles of Macroeconomics | W | 3 credits | 2V | J.‑E. Sturm | |

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? | |||||

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 webpage (to be found at Link) 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 (2020), Economics, Cengage Learning, Fifth Edition. Besides this textbook, the slides, lecture notes and problem sets will cover the content of the lecture and the exam questions. | |||||

363-1031-00L | Quantitative Methods in Energy and Environmental EconomicsDoes not take place this semester. | W+ | 4 credits | 3G | to be announced | |

Abstract | The course provides an introduction to quantitative methods used to analyze problems in energy and environmental economics. Emphasis will be put on partial and general equilibrium models, regression models to estimate demand functions, econometric techniques for policy evaluations, and panel data methods. | |||||

Objective | The objectives of the course are twofold. First, the course is intended to provide an introduction to the economic assessment of energy and environmental policy. To this end, the course provides students with an overview of state-of-the-art tools to economic modeling and econometric approaches. Second, the course is intended to familiarize master (and doctoral students) with the computer software necessary to implement these quantitative methods to initiate their own research in energy and environmental economics. Ancillary objectives of the course include an introduction to environmental implications of energy use and the role of economic analysis in designing policies which address issues of energy security, climate change and related environmental externalities. | |||||

Literature | Lecture notes, exercises and reference material will be made available to students during the semester. | |||||

Prerequisites / Notice | Basic knowledge of microeconomics and calculus. Knowledge from the courses "Energy Economics and Policy (363-0514-00L)" and "Principles of Microeconomics" are required. Block course during two weeks before the start of the semester. Students work on a group project during the semester. Presentation of group projects by students in week 8 and 9 of the semester. Performance assessment is based on group projects during the semester. |

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