This paper provides an overview of the core techniques for measuring technical efficiency and productivity based on the concept of a best practice frontier. It discusses the historical development of these methods, from Farrell's 1957 proposal to measure technical inefficiency as the deviation from a frontier isoquant to the recent advancements in semiparametric econometrics and non-parametric methods like Data Envelopment Analysis (DEA). The paper also explores the statistical properties of DEA estimators and the use of bootstrapping for constructing confidence intervals. Additionally, it reviews the extension of frontier techniques to measure productivity growth, including both parametric and non-parametric approaches. The parametric approach, based on stochastic frontier analysis, decomposes productivity change into technical change, technical efficiency change, and scale change. The non-parametric approach, linked to the Malmquist index, measures productivity change by comparing a firm's position in two adjacent time periods relative to a best practice frontier. The paper concludes by discussing the strengths and limitations of these methods and their applications in empirical research.This paper provides an overview of the core techniques for measuring technical efficiency and productivity based on the concept of a best practice frontier. It discusses the historical development of these methods, from Farrell's 1957 proposal to measure technical inefficiency as the deviation from a frontier isoquant to the recent advancements in semiparametric econometrics and non-parametric methods like Data Envelopment Analysis (DEA). The paper also explores the statistical properties of DEA estimators and the use of bootstrapping for constructing confidence intervals. Additionally, it reviews the extension of frontier techniques to measure productivity growth, including both parametric and non-parametric approaches. The parametric approach, based on stochastic frontier analysis, decomposes productivity change into technical change, technical efficiency change, and scale change. The non-parametric approach, linked to the Malmquist index, measures productivity change by comparing a firm's position in two adjacent time periods relative to a best practice frontier. The paper concludes by discussing the strengths and limitations of these methods and their applications in empirical research.