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Focusing on methods for data that are ordered in time, this textbook provides a comprehensive guide to analyzing time series data using modern techniques from data science. It is specifically tailored to economics and finance applications, aiming to provide students with rigorous training. Chapters cover Bayesian approaches, nonparametric smoothing methods, machine learning, and continuous time econometrics. Theoretical and empirical exercises, concise summaries, bolded key terms, and illustrative examples are included throughout to reinforce key concepts and bolster understanding. Ancillary materials include an instructor's manual with solutions and additional exercises, PowerPoint lecture slides, and datasets. With its clear and accessible style, this textbook is an essential tool for advanced undergraduate and graduate students in economics, finance, and statistics.
Brownian motion is an important topic in various applied fields where the analysis of random events is necessary. Introducing Brownian motion from a statistical viewpoint, this detailed text examines the distribution of quadratic plus linear or bilinear functionals of Brownian motion and demonstrates the utility of this approach for time series analysis. It also offers the first comprehensive guide on deriving the Fredholm determinant and the resolvent associated with such statistics. Presuming only a familiarity with standard statistical theory and the basics of stochastic processes, this book brings together a set of important statistical tools in one accessible resource for researchers and graduate students. Readers also benefit from online appendices, which provide probability density graphs and solutions to the chapter problems.
This chapter introduces some nonlinear time series models of widespread use in economics and finance. Specifically, we consider structural breaks, GARCH models, and copula models.
This chapter gives a more comprehensive treatment of nonparametric methods for estimating density functions and dynamic regression models. We also consider the emerging material on the case where there are many explanatory variables and how selection methods can be used to apply estimation and inference techniques to this case.
A multi-year process of debate around draft articles for a Crimes Against Humanity Treaty is underway and calls to categorize gender-based persecution as a stand-alone crime and to codify gender apartheid form fundamental aspects of discussion. These developments in international criminal law are significant to anticipate forced migration as recent changes in asylum regulations across the EU suggest. Between December 2022 and February 2023, Sweden, Finland, and Denmark moved to grant asylum to women and girls from Afghanistan on general risks of gender-based persecution. This falls in line with the EU Agency for Asylum establishing that the accumulation of repressive measures against women and girls in the country, which have been described as gender apartheid, amounts to persecution. In efforts to offer new perspectives on foresight in forced migration, I use case study method and legal-institutional analysis to delineate migration scenarios for gender apartheid and asylum. On the example of Afghanistan, I compare Sweden, Finland, and Denmark as case studies in which asylum is granted to women and girls on general risks of gender-based persecution in contrast to Germany and France as case studies for main destination countries of Afghan asylum-seekers absent of such policies. I explore factors towards policy in/action and provide outlooks for further lines of inquiry regarding anticipatory methods in forced migration.
Abortion is one of the major threats to the livestock industry, and it also poses significant threats to public health since some of the abortifacient agents are considered zoonotic. Chlamydia abortus (C. abortus), Coxiella burnetii (C. burnetii), Listeria monocytogenes (L. monocytogenes), and Cache Valley virus (CVV) are recognized as important zoonotic and abortifacient agents of reproductive failure in small ruminants. This study determined the prevalence of these agents in ovine and caprine foetuses in Türkiye. A total of 1 226 foetuses were collected from the sheep (n = 1 144) and goats (n = 82) from different flocks between 2012 and 2017. Molecular detection methods were used to detect C. abortus, C. burnetii, and L. monocytogenes DNA and CVV RNA in aborted foetuses. In this study, C. abortus was the most prevalent abortifacient agent among the investigated ovine (264/1144) and caprine (12/82) foetuses, followed by C. burnetii with a frequency of 2.8% (32/1144) and 8.5% (7/82) in ovine and caprine foetuses, respectively. L. monocytogenes DNA was detected in 28 (2.4%) and 2 (2.4%) of the ovine and caprine foetuses, respectively. However, CVV RNA was not detected. Although the predominant mixed infection was C. abortus and C. burnetii, mixed infection of C. abortus and L. monocytogenes, and C. burnetii and L. monocytogenes were also found. The information presented in this study contributes to the understanding of the roles of C. abortus, C. burnetii, L. monocytogenes, and CVV in abortions in small ruminants, and could be beneficial for developing more effective control strategies.
This chapter introduces the Bayesian approach. We define the key concepts that are needed to understand Bayesian inference and the comparison with frequentist inference. We show how these concepts can be applied in the linear time series models considered earlier and discuss the modern treatment of vector autoregression models from a Bayesian perspective.
This chapter considers the multivariate case, extending the univariate concepts to the vector time series case. We consider vector autoregressions from different points of view.
This chapter introduces the class of autoregressive moving average models and discusses their properties in special cases and in general. We provide alternative methods for the estimation of unknown parameters and describe the properties of the estimators. We discuss key issues like hypothesis testing and model selection.
This chapter is concerned with different approaches to accounting for trend and seasonal components. We consider both deterministic and stochastic approaches and show the overlap and contrast between these approaches. Estimation and inference are treated.
This chapter introduces the frequency-domain view and how this way of thinking can help with understanding periodic behavior and cycles. We define the spectral density function and how commonly used filters affect the spectral shape. We discuss estimation by the periodogram and smoothing methods.
In this chapter we consider the continuous-time setting. We consider some classical models and their estimation, and the more recent literature on high-frequency econometrics.
In this chapter we consider the question of forecasting. We consider model-based and ad hoc approaches to this question. We discuss the issue of forecast evaluation and comparison.