Methods and perspectives to model and measure productivity and efficiency have made a number of important advances in the last decade. Using the standard and innovative formulations of the theory and practice of efficiency and productivity measurement, Robin C. Sickles and Valentin Zelenyuk provide a comprehensive approach to productivity and efficiency analysis, covering its theoretical underpinnings and its empirical implementation, paying particular attention to the implications of neoclassical economic theory. A distinct feature of the book is that it presents a wide array of theoretical and empirical methods utilized by researchers and practitioners who study productivity issues. An accompanying website includes methods, programming codes that can be used with widely available software like MATLAB (R) and R, and test data for many of the productivity and efficiency estimators discussed in the book. It will be valuable to upper-level undergraduates, graduate students, and professionals.

Performance Analysis: Economic Foundations and Trends provides a relatively concise overview of Efficiency and Productivity Analysis-a very important field of research and practice, spanning over and engaging with many disciplines, most prominently Economics (theoretical and applied), Statistics (and therefore Econometrics), Operations Research (OR) and Management Science (MS), as well as Business Analytics and Business Information Systems, Computer Science and Engineering, etc. Methods developed in this field became very popular in practice for analyzing the efficiency of various economic systems: firms or its distinct departments, branches or plants, entire industries or sub-industries, countries, regions or provinces, as well as various groups or unions of countries, such as APEC, EU, OECD, etc. Performance Analysis: Economic Foundations and Trends aims to complement and update the existing literature, aiming to be fairly broad, yet with some rigor, and yet also be relatively concise, with numerous references where more details can be found. The author provides a foundation that an interested reader may find useful before learning more, whether it be in terms of theory or in terms of empirical work involving any estimator, and whether it is the DEA or its alternatives, such as Stochastic Frontier Analysis (SFA) or a symbiosis of them. This makes this part useful by itself, even if the reader decides not to pursue with DEA, SFA or any other particular approach.