Copenhagen Business School

This course provides an introduction to the application of various forecasting techniques. It introduces quantitative methods and techniques for time series modeling, analysis, and forecasting. Emphasis is also put on the applications in economic and business related areas. Computing is an integral part of this course, therefore all students are expected to do data analysis, modeling, and forecasting with computer programming software. The focus of the course is to use past data to predict the future. The key concept is that there is an underlying process that gives rise to the data. Using statistical properties of that process, we can develop forecasts. Forecasting methodology is presented in a lecture format in the first part of each class meeting. The application, however, is the centerpiece of the presentation. In the second part of the class meeting, students work on in-class applications. The course starts start with simple models that are widely used in business and progresses to modern methods that are used by professional forecasters and economists. It studies basic components of time-series data, such as trend, seasonal, and cyclical components, as well as basic model specification techniques, such as moving average and auto regressions, used in the forecasting of time-series. All forecasting methods are illustrated with detailed real world applications designed to mimic typical forecasting situations. The course uses applications not simply to illustrate the methods but also drive home an important lesson, the limitations of forecasting, by presenting truly realistic examples in which not everything works perfectly. Examples of the applications include, but are not limited to, forecasting retail sales, hotel occupancy, fishery output, consumer loan requests, predicting expansion of fast food chain stores, modeling and forecasting macroeconomic activity indices such as Gross Domestic Product, unemployment and inflation, modeling development of a small open economy, forecasting New York Stock Exchange index and currency exchange rates and many other applications.

This course introduces fundamental concepts of econometrics and data analysis that form the basis for data driven decision making, empirical analysis of causal relationships, and forecasting. It covers matrices and their use in linear regression analysis, probability distributions and their role in carrying out valid data approximations, and estimation methods and their importance in producing credible results of any data analysis. The course also introduces programming with R, which is the main programming language of statistical computing. It starts out with basic R operations and then, with time, students learn about ways to write their own functions in R.

This course introduces basic principles of the circular economy and the strategies, market arrangements, and regulatory landscapes that set barriers and enablers for circular transitions. The course is intensive and follows a problem-based learning approach, where students develop novel solutions to a real-world challenge posed by a case company that aspires to go circular. The class requires active participation from students, with classroom activities and discussions as well as working in project teams inside and outside the classroom. It facilitates learning by doing, learning by observing, and learning by explaining and critical reflecting. Students are supported in their learning process with coaching in collaboration with the Copenhagen School of Entrepreneurship.

This course explores how sustainability and social justice have evolved from isolated considerations to interrelated frameworks that jointly influence policy decisions. Discussions focus on understanding sustainable development through the diverse worldviews of local communities, businesses, and governments in emerging markets. The course not only examines sustainability from ecological and economic perspectives but also incorporates ethical and social considerations, particularly issues of intergenerational fairness and justice. The course specifically discusses and contrasts the concept of sustainability for social justice within the operations of both local and multinational corporations in emerging markets. It frames these discussions in the context of the unintended impacts that business investments can have on biodiversity, human rights, and global value chains. Special attention is given to the challenges businesses face in mitigating and adapting to climate change within their operations. The course provides the knowledge and tools needed to navigate the intricacies of sustainability, with a special emphasis on human rights, biodiversity, and global value chains in emerging markets. Supported by academic theory and real-world case studies, the course adopts a holistic, social-sciences perspective on current sustainability concepts. This comprehensive approach facilitates the design and evaluation of governmental and corporate policies and practices to contribute to a more equitable and sustainable future.

This course develops an understanding of the fundamentals of consumer behavior and the ways in which consumer behavior can be influenced. It introduces concepts and theories to provide insight into the drivers of consumer behavior, including the analysis of how consumers make decisions. Several company examples are discussed that show how insights about consumer behavior are applied and implemented in business contexts. Topics include consumer segmentation, tools to conduct consumer research, consumer decision making process (problem recognition, information search), internal influences on consumer behavior (exposure, attention, motivation, attitudes, memory, knowledge, learning), external influences on consumer behavior (social influence, reference groups, cultural influences), consumer biases and heuristics, and consumer behavior in the age of artificial intelligence.

This course introduces the basic tools to understand how to analyze economic phenomena and presents the main topics in macroeconomics and international economics. It focuses on applications and policy implications rather than formal demonstrations to provide a critical perspective on economic models and apply them to many different situations. Critical thinking and knowledge of the basic formal mechanisms of macroeconomic and international trade models is essential. The first module on modern macroeconomics analyzes the determinants of economic well-being in the short and long run and key macroeconomic terms (for example, defining and measuring economic growth, inflation, and unemployment). It examines how the economy grows in the short and long run, the role of productivity, and the impact of the business cycle, as well as the role of monetary and fiscal policies. The second module on policy in the open economy analyzes the financial linkages between different countries, especially regarding the Balance of Payments and its effects on national economies. It also surveys the role of money and finance in the world today and the role of government policies toward the foreign exchange market, including the choice between fixed and floating exchange rates, strategies to keep currencies under or overvalued, and the use of exchange controls to create impediments to currency flows. Finally, it incorporates climate change and income inequality in regular macroeconomic models and examines development problems and the role firms can play in fostering growth.

This course examines the dynamic nature of leadership and innovation as theorized in the past, its present application, and how it might evolve in the future within the backdrop of a fast-digitalizing emerging economy. Lectures focus on how leadership and innovation have evolved over time and how emerging economies are learning from the existing leadership debates and adding to it from their own experiences, culture, and social context. While this course starts from existing leadership discourse, it does not limit itself to the current understanding of leadership. It explores newer leadership experiences from emerging economies to develop a counter exploration to the existing leadership narrative and provides a holistic understanding of leadership by filling the gaps and insights from vibrant diverse cultures that identify themselves as emerging economies.

This course analyzes financial data in Microsoft Excel using Pivot Tables and various Excel financial functions and financial concepts. These concepts include capital budgeting and time value of money, corporate bonds and stock valuation models, discounted cash flows, portfolio analysis and the capital asset pricing model, forecasting sales and profits, measuring investment performance, financial sales analysis, and cost and profit analysis. The course combines the basic theory of finance with the intensive implementation of all concepts in Excel to provide a better understanding of financial theory and valuable skills in Excel.

This course provides theoretical and practical knowledge of individual behavior in the context of social marketing and behavioral science. It takes on a multidisciplinary approach drawing from psychology, consumer research, marketing science, and behavioral economics that allows a holistic understanding of how people consume and behave. Based on these theoretical frameworks, the course introduces a range of tools to foster behavioral change, such as social marketing or nudging, and examines how they are applied in practice. Applied consumer research is at the core of this course and its potential and limits in understanding and ultimately changing individual behavior are studied. The course provides an opportunity to practice formulating research questions and proposing social marketing and behavioral science solutions to address real-life social and environmental problems. It also engages critical discussion on the use of behavioral and marketing techniques by policy-makers and the private sector.