To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Understanding change over time is a critical component of social science. However, data measured over time – time series – requires their own set of statistical and inferential tools. In this book, Suzanna Linn, Matthew Lebo, and Clayton Webb explain the most commonly used time series models and demonstrate their applications using examples. The guide outlines the steps taken to identify a series, make determinations about exogeneity/endogeneity, and make appropriate modelling decisions and inferences. Detailing challenges and explanations of key techniques not covered in most time series textbooks, the authors show how navigating between data and models, deliberately and transparently, allows researchers to clearly explain their statistical analyses to a broad audience.
Empirical research papers are a mix of technical skill and unwritten insider information. Providing practical guidance on how to design, analyze, and write about research, this engaging second edition is fully updated with expanded coverage of finding and using data, a topical running example, and new appendices introducing quantitative analysis techniques. It covers everything from crafting a question, theory, and hypotheses to choosing a research design, acquiring and analyzing data, drafting, peer review, and presenting your work. Practical strategies are combined with a step-by-step breakdown of every stage of the research design and writing processes, conveyed with clarity and humor. The intuitive presentation illustrates the core insights and concepts in a lively and accessible manner for readers, including those with no mathematical background and from fields beyond political science. New 'Common Challenges' boxes join a wealth of inspiring pedagogical features. Online resources include a revised Instructor's Manual, exercises and essays.
This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024). Some matrix algebra knowledge is assumed but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions. The sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout.
This chapter focuses on games with unawareness, where the players may be unaware of some of the choices that others can make. The player’s view specifies the choices in the game that he is aware of. The chapter starts by explaining how a game with unawareness can be viewed as a collection of one-person decision problems. Subsequently, it is shown how belief hierarchies about choices and views can be visualized by means of a beliefs diagram, and mathematically encoded by means of an epistemic model with types. This is used to provide a formal definition of common belief in rationality. It is shown that the choices which are possible under common belief in rationality can be characterized by iterated strict dominance for unawareness. The chapter finally turns to the scenario of fixed beliefs on views, where the players hold some pre-specified beliefs about the opponents’ views.
This chapter investigates one-person decision problems under uncertainty. The main building block is that of a conditional preference relation: a mapping that assigns to every belief about the states a preference relation over the decision maker’s choices. Under certain conditions, such a conditional preference relation admits an expected utility representation, which allows us to summarize the conditional preference relation by a finite utility matrix. Throughout the book it is assumed that the conditional preference relation indeed has an expected utility representation.
This chapter focuses on standard games. It starts by explaining how a standard game can be viewed as a collection of one-person decision problems. Subsequently, it is shown how belief hierarchies about choices can be visualized by means of a beliefs diagram, and mathematically encoded by means of an epistemic model with types. This is used to provide a formal definition of common belief in rationality – the central line of reasoning which states that a player believes that others choose rationally, believes that others believe that others choose rationally, and so on. It is shown that the choices which are possible under common belief in rationality can be characterized by the iterated elimination of strictly dominated choices.
This chapter focuses on psychological games, where the players’ preferences may directly depend on higher-order beliefs. The chapter starts by explaining how a psychological game can be viewed as a collection of one-person decision problems. By using the same epistemic model as for standard games, it provides a formal definition of common belief in rationality. It is shown that the choices which are possible under common belief in rationality can be characterized by the iterated elimination of choices and second-order expectations. The chapter finally demonstrates when the easier procedure of iterated elimination of choices and states is sufficient for common belief in rationality.
This chapter starts by introducing the notion of a simple belief hierarchy, and shows that a simple belief hierarchy in combination with common belief in rationality leads to Nash equilibrium. It then turns to the weaker notion of symmetric belief hierarchies and shows, in a similar fashion, that a symmetric belief hierarchy in combination with common belief in rationality leads to correlated equilibrium. It finally investigates the one theory per choice condition, and demonstrates how it leads to canonical correlated equilibrium when combined with common belief in rationality and a symmetric belief hierarchy.
This chapter starts by introducing the notion of a simple belief hierarchy, and shows that a simple belief hierarchy in combination with common belief in rationality leads to generalized Nash equilibrium. It then turns to the weaker notion of symmetric belief hierarchies and shows, in a similar fashion, that a symmetric belief hierarchy in combination with common belief in rationality leads to Bayesian equilibrium. It subsequently investigates the one theory per choice-utility pair condition, and demonstrates how it leads to canonical Bayesian equilibrium when combined with common belief in rationality and a symmetric belief hierarchy. The chapter finally turns to the scenario of fixed beliefs on utilities, where the players hold some pre-specified beliefs about the opponents’ utility functions.
This chapter focuses on games with incomplete information, where the players may be uncertain about the utility functions of the other players. It starts by explaining how a game with incomplete information can be viewed as a collection of one-person decision problems. Subsequently, it is shown how belief hierarchies about choices and utility functions can be visualized by means of a beliefs diagram, and mathematically encoded by means of an epistemic model with types. This is used to provide a formal definition of common belief in rationality. It is shown that the choices which are possible under common belief in rationality can be characterized by the generalized iterated strict dominance procedure. The chapter finally turns to the scenario of fixed beliefs on utilities, where the players hold some pre-specified beliefs about the opponents’ utility functions.