The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.

In recent years, interest in rigorous impact evaluation has grown tremendously in policy-making, economics, public health, social sciences and international relations. Evidence-based policy-making has become a recurring theme in public policy, alongside greater demands for accountability in public policies and public spending, and requests for independent and rigorous impact evaluations for policy evidence. Froelich and Sperlich offer a comprehensive and up-to-date approach to quantitative impact evaluation analysis, also known as causal inference or treatment effect analysis, illustrating the main approaches for identification and estimation: experimental studies, randomization inference and randomized control trials (RCTs), matching and propensity score matching and weighting, instrumental variable estimation, difference-in-differences, regression discontinuity designs, quantile treatment effects, and evaluation of dynamic treatments. The book is designed for economics graduate courses but can also serve as a manual for professionals in research institutes, governments, and international organizations, evaluating the impact of a wide range of public policies in health, environment, transport and economic development.

This selection of articles emerged from different works presented "The Art of Semiparametrics" conference in 2003 in Berlin. It offers a collection of individual works that together show the large spectrum of semiparametric statistics. The book combines theoretical contributions with more applied and empirical studies. Although each article represents an original contribution to its own field, all are written in a self-contained way that may be read by non-experts.

The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.

Das UEbungsbuch stellt eine ausgesuchte Sammlung von Problemstellungen und Loesungen bereit, die durch eine Formelsammlung mit den wichtigsten im Buch verwendeten Formeln abgerundet wird. Zusatzlich wird ein umfangreiches Set von Programmen in R zur Verfugung gestellt, die zur Aufgabenstellung und Loesung geschrieben wurden. Der Anhang des Buches beinhaltet daher auch eine kurze Einfuhrung in die Statistik-Software R. Der Inhalt, Organisation inklusive Kapitelaufteilung orientiert sich an dem bei Springer erschienenem Werk "Statistik fur Bachelor- und Masterstudenten: Eine Einfuhrung fur Wirtschafts- und Sozialwissenschaftler"

Das Buch fuhrt in die wesentlichen statistischen Konzepte und Ideen ein und erlautert anhand von Beispielen detailliert deren Umsetzung. Der Stil ist, anders als bei den meisten Konkurrenzwerken, betont locker gehalten - ohne dabei auf eine exakte Darstellung zu verzichten. Das Buch ist speziell auf die Bedurfnisse von Anfangern im Fach Statistik zugeschnitten und fur Bachelor- und Masterstudenten aller Disziplinen geeignet - auch zum Selbststudium."

In recent years, interest in rigorous impact evaluation has grown tremendously in policy-making, economics, public health, social sciences and international relations. Evidence-based policy-making has become a recurring theme in public policy, alongside greater demands for accountability in public policies and public spending, and requests for independent and rigorous impact evaluations for policy evidence. Frölich and Sperlich offer a comprehensive and up-to-date approach to quantitative impact evaluation analysis, also known as causal inference or treatment effect analysis, illustrating the main approaches for identification and estimation: experimental studies, randomization inference and randomized control trials (RCTs), matching and propensity score matching and weighting, instrumental variable estimation, difference-in-differences, regression discontinuity designs, quantile treatment effects, and evaluation of dynamic treatments. The book is designed for economics graduate courses but can also serve as a manual for professionals in research institutes, governments, and international organizations, evaluating the impact of a wide range of public policies in health, environment, transport and economic development.