This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models. The authors provide a detailed and extensive study of impulse responses and forecasting in the stationary and non-stationary context, considering small sample correction, volatility and the impact of different orders of integration. Models with expectations are considered along with alternate methods such as Singular Spectrum Analysis (SSA), the Kalman Filter and Structural Time Series, all in relation to cointegration. Using single equations methods to develop topics, and as examples of the notion of cointegration, Burke, Hunter, and Canepa provide direction and guidance to the now vast literature facing students and graduate economists.

Coverage has been extended to include recent topics. The book again presents a unified treatment of economic theory, with the method of maximum likelihood playing a key role in both estimation and testing. Exercises are included and the book is suitable as a general text for final-year undergraduate and postgraduate students.

The main objective of this book is to develop a strategy and policy measures to enhance the formalization of the shadow economy in order to improve the competitiveness of the economy and contribute to economic growth; it explores these issues with special reference to Serbia. The size and development of the shadow economy in Serbia and other Central and Eastern European countries are estimated using two different methods (the MIMIC method and household-tax-compliance method). Micro-estimates are based on a special survey of business entities in Serbia, which for the first time allows us to explore the shadow economy from the perspective of enterprises and entrepreneurs. The authors identify the types of shadow economy at work in business entities, the determinants of shadow economy participation, and the impact of competition from the informal sector on businesses. Readers will learn both about the potential fiscal effects of reducing the shadow economy to the levels observed in more developed countries and the effects that formalization of the shadow economy can have on economic growth.

Time Series: A First Course with Bootstrap Starter provides an introductory course on time series analysis that satisfies the triptych of (i) mathematical completeness, (ii) computational illustration and implementation, and (iii) conciseness and accessibility to upper-level undergraduate and M.S. students. Basic theoretical results are presented in a mathematically convincing way, and the methods of data analysis are developed through examples and exercises parsed in R. A student with a basic course in mathematical statistics will learn both how to analyze time series and how to interpret the results. The book provides the foundation of time series methods, including linear filters and a geometric approach to prediction. The important paradigm of ARMA models is studied in-depth, as well as frequency domain methods. Entropy and other information theoretic notions are introduced, with applications to time series modeling. The second half of the book focuses on statistical inference, the fitting of time series models, as well as computational facets of forecasting. Many time series of interest are nonlinear in which case classical inference methods can fail, but bootstrap methods may come to the rescue. Distinctive features of the book are the emphasis on geometric notions and the frequency domain, the discussion of entropy maximization, and a thorough treatment of recent computer-intensive methods for time series such as subsampling and the bootstrap. There are more than 600 exercises, half of which involve R coding and/or data analysis. Supplements include a website with 12 key data sets and all R code for the book's examples, as well as the solutions to exercises.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

Decision-theoretic ideas can structure the process of inference together with the decision-making that inference supports. Statistical decision theory is the sub-discipline of statistics which explores and develops this structure. Typically, discusion of decision theory within one discipline does not recognise that other disciplines may have considered the same or similar problems. This text, Volume 9 in the prestigious Kendall's Library of Statistics, provides an overview of the main ideas and concepts of statistical decision theory and sets it within the broader concept of decision theory, decision analysis and decision support as they are practised in many disciplines beyond statistics - including artificial intelligence, economics, operational research, philosophy and psychology.

Who decides how official statistics are produced? Do politicians have control or are key decisions left to statisticians in independent statistical agencies? Interviews with statisticians in Australia, Canada, Sweden, the UK and the USA were conducted to get insider perspectives on the nature of decision making in government statistical administration. While the popular adage suggests there are 'lies, damned lies and statistics', this research shows that official statistics in liberal democracies are far from mistruths; they are consistently insulated from direct political interference. Yet, a range of subtle pressures and tensions exist that governments and statisticians must manage. The power over statistics is distributed differently in different countries, and this book explains why. Differences in decision-making powers across countries are the result of shifting pressures politicians and statisticians face to be credible, and the different national contexts that provide distinctive institutional settings for the production of government numbers.

In nonparametric and high-dimensional statistical models, the classical Gauss-Fisher-Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.

This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many 'random effects'. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write-up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to 'translate' their skills with more traditional models to a Bayesian framework, will benefit greatly from the lessons in this text.

Computable general equilibrium (CGE) models play an important role in supporting public-policy making on such issues as trade, climate change and taxation. This significantly revised volume, keeping pace with the next-generation standard CGE model, is the only undergraduate-level introduction of its kind. The volume utilizes a graphical approach to explain the economic theory underlying a CGE model, and provides results from simple, small-scale CGE models to illustrate the links between theory and model outcomes. Its eleven hands-on exercises introduce modelling techniques that are applied to real-world economic problems. Students learn how to integrate their separate fields of economic study into a comprehensive, general equilibrium perspective as they develop their skills as producers or consumers of CGE-based analysis.

As one of the first texts to take a behavioral approach to macroeconomic expectations, this book introduces a new way of doing economics. Roetheli uses cognitive psychology in a bottom-up method of modeling macroeconomic expectations. His research is based on laboratory experiments and historical data, which he extends to real-world situations. Pattern extrapolation is shown to be the key to understanding expectations of inflation and income. The quantitative model of expectations is used to analyze the course of inflation and nominal interest rates in a range of countries and historical periods. The model of expected income is applied to the analysis of business cycle phenomena such as the great recession in the United States. Data and spreadsheets are provided for readers to do their own computations of macroeconomic expectations. This book offers new perspectives in many areas of macro and financial economics.

Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao. In the first few chapters of this book, new theoretical panel and time series results are presented, exploring JIVE estimators, HAC, HAR and various sandwich estimators, as well as asymptotic distributions for using information criteria to distinguish between the unit root model and explosive models. Other chapters address topics such as structural breaks or growth empirics; auction models; and semiparametric methods testing for common vs. individual trends. Three chapters provide novel empirical approaches to applied problems, such as estimating the impact of survey mode on responses, or investigating how cross-sectional and spatial dependence of mortgages varies by default rates and geography. In the final chapters, Cheng Hsiao offers a forward-focused discussion of the role of big data in economics. For any researcher of econometrics, this is an unmissable volume of the most current and engaging research in the field.

Nanak Kakwani and Hyun Hwa Son make use of social welfare functions to derive indicators of development relevant to specific social objectives, such as poverty- and inequality-reduction. Arguing that the measurement of development cannot be value-free, the authors assert that if indicators of development are to have policy relevance, they must be assessed on the basis of the social objectives in question. This study develops indicators that are sensitive to both the level and the distribution of individuals' capabilities. The idea of the social welfare function, defined in income space, is extended to the concept of the social well-being function, defined in capability space. Through empirical analysis from selected developing countries, with a particular focus on Brazil, the authors shape techniques appropriate to the analysis of development in different dimensions. The focus of this evidence-based policy analysis is to evaluate alternative policies affecting the capacities of people to enjoy a better life.

* A useful guide to financial product modeling and to minimizing business risk and uncertainty * Looks at wide range of financial assets and markets and correlates them with enterprises' profitability * Introduces advanced and novel machine learning techniques in finance such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and applies them to analyze finance data sets * Real world applicable examples to further understanding

This book examines whether continuous-time models in frictionless financial economies can be well approximated by discrete-time models. It specifically looks to answer the question: in what sense and to what extent does the famous Black-Scholes-Merton (BSM) continuous-time model of financial markets idealize more realistic discrete-time models of those markets? While it is well known that the BSM model is an idealization of discrete-time economies where the stock price process is driven by a binomial random walk, it is less known that the BSM model idealizes discrete-time economies whose stock price process is driven by more general random walks. Starting with the basic foundations of discrete-time and continuous-time models, David M. Kreps takes the reader through to this important insight with the goal of lowering the entry barrier for many mainstream financial economists, thus bringing less-technical readers to a better understanding of the connections between BSM and nearby discrete-economies.

This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included.

Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time series.

If you know a little bit about financial mathematics but don't yet know a lot about programming, then C++ for Financial Mathematics is for you. C++ is an essential skill for many jobs in quantitative finance, but learning it can be a daunting prospect. This book gathers together everything you need to know to price derivatives in C++ without unnecessary complexities or technicalities. It leads the reader step-by-step from programming novice to writing a sophisticated and flexible financial mathematics library. At every step, each new idea is motivated and illustrated with concrete financial examples. As employers understand, there is more to programming than knowing a computer language. As well as covering the core language features of C++, this book teaches the skills needed to write truly high quality software. These include topics such as unit tests, debugging, design patterns and data structures. The book teaches everything you need to know to solve realistic financial problems in C++. It can be used for self-study or as a textbook for an advanced undergraduate or master's level course.

How to Divide When There Isn't Enough develops a rigorous yet accessible presentation of the state-of-the-art for the adjudication of conflicting claims and the theory of taxation. It covers all aspects one may wish to know about claims problems: the most important rules, the most important axioms, and how these two sets are related. More generally, it also serves as an introduction to the modern theory of economic design, which in the last twenty years has revolutionized many areas of economics, generating a wide range of applicable allocations rules that have improved people's lives in many ways. In developing the theory, the book employs a variety of techniques that will appeal to both experts and non-experts. Compiling decades of research into a single framework, William Thomson provides numerous applications that will open a large number of avenues for future research.

Space is a crucial variable in any economic activity. Spatial Economics is the branch of economics that explicitly aims to incorporate the space dimension in the analysis of economic phenomena. From its beginning in the last century, Spatial Economics has contributed to the understanding of the economy by developing plenty of theoretical models as well as econometric techniques having the "space" as a core dimension of the analysis.This edited volume addresses the complex issue of Spatial Economics from an applied point of view. This volume is part of a more complex project including another edited volume (Spatial Economics Volume I: Theory) collecting original papers which address Spatial Economics from a theoretical perspective.

Space is a crucial variable in any economic activity. Spatial Economics is the branch of economics that explicitly aims to incorporate the space dimension in the analysis of economic phenomena. From its beginning in the last century, Spatial Economics has contributed to the understanding of the economy by developing plenty of theoretical models as well as econometric techniques having the "space" as a core dimension of the analysis. This edited volume addresses the complex issue of Spatial Economics from a theoretical point of view. This volume is part of a more complex project including another edited volume (Spatial Economics Volume II: Applications) collecting original papers which address Spatial Economics from an applied perspective.

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.

From the 1980s onward, income inequality increased in many advanced countries. It is very difficult to account for the rise in income inequality using the standard labour supply/demand explanation. Fiscal redistribution has become less effective in compensating increasing inequalities since the 1990s. Some of the basic features of redistribution can be explained through the optimal tax framework developed by J. A. Mirrlees in 1971. This Element surveys some of the earlier results in linear and nonlinear taxation and produces some new numerical results. Given the key role of capital income in the overall income inequality, it also considers the optimal taxation of capital income. It examines empirically the relationship between the extent of redistribution and the components of the Mirrlees framework. The redistributive role of factors such as publicly provided private goods, public employment, endogenous wages in the overlapping generations model and income uncertainty are analysed.

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and contacts research frontiers in multivariate time series modeling and forecasting. It presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation, including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. It illustrates the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, environmental science, and finance. Along with core models and methods, the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems. It also demonstrates the growth of time series analysis into new application areas in recent years, and contacts recent and relevant modeling developments and research challenges. New in the second edition: Expanded on aspects of core model theory and methodology. Multiple new examples and exercises. Detailed development of dynamic factor models. Updated discussion and connections with recent and current research frontiers.