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.

In recent years nonlinearities have gained increasing importance in economic and econometric research, particularly after the financial crisis and the economic downturn after 2007. This book contains theoretical, computational and empirical papers that incorporate nonlinearities in econometric models and apply them to real economic problems. It intends to serve as an inspiration for researchers to take potential nonlinearities in account. Researchers should be aware of applying linear model-types spuriously to problems which include non-linear features. It is indispensable to use the correct model type in order to avoid biased recommendations for economic policy.

This book deals with the application of wavelet and spectral methods for the analysis of nonlinear and dynamic processes in economics and finance. It reflects some of the latest developments in the area of wavelet methods applied to economics and finance. The topics include business cycle analysis, asset prices, financial econometrics, and forecasting. An introductory paper by James Ramsey, providing a personal retrospective of a decade's research on wavelet analysis, offers an excellent overview over the field.

​This publication provides insight into the agricultural sector. It illustrates new tendencies in agricultural economics and dynamics (interrelationship with other sectors in rural zones and multifunctionality) and the implications of the World Trade Organization negotiations in the international trade of agricultural products. Due to environmental problems, availability of budget, consumer preferences for food safety and pressure from the World Trade Organization, there are many changes in the agricultural sector. This book addresses those new developments and provides insights into possible future developments. The agricultural activity is an economic sector that is fundamental for a sustainable economic growth of every country. However, this sector has many particularities, namely those related with some structural problems (many farms with reduced dimension, sometimes lack of vocational training of the farmers, difficulties of put the farmers together in associations and cooperatives), variations of the productions and prices over the year and some environmental problems derived from the utilization of pesticides and fertilizers.

This volume systematically details both the basic principles and new developments in Data Envelopment Analysis (DEA), offering a solid understanding of the methodology, its uses, and its potential. New material in this edition includes coverage of recent developments that have greatly extended the power and scope of DEA and have lead to new directions for research and DEA uses. Each chapter accompanies its developments with simple numerical examples and discussions of actual applications. The first nine chapters cover the basic principles of DEA, while the final seven chapters provide a more advanced treatment.

In the era of Big Data our society is given the unique opportunity to understand the inner dynamics and behavior of complex socio-economic systems. Advances in the availability of very large databases, in capabilities for massive data mining, as well as progress in complex systems theory, multi-agent simulation and computational social science open the possibility of modeling phenomena never before successfully achieved. This contributed volume from the Perm Winter School address the problems of the mechanisms and statistics of the socio-economics system evolution with a focus on financial markets powered by the high-frequency data analysis.

This book provides a detailed introduction to the theoretical and methodological foundations of production efficiency analysis using benchmarking. Two of the more popular methods of efficiency evaluation are Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA), both of which are based on the concept of a production possibility set and its frontier. Depending on the assumed objectives of the decision-making unit, a Production, Cost, or Profit Frontier is constructed from observed data on input and output quantities and prices. While SFA uses different maximum likelihood estimation techniques to estimate a parametric frontier, DEA relies on mathematical programming to create a nonparametric frontier. Yet another alternative is the Convex Nonparametric Frontier, which is based on the assumed convexity of the production possibility set and creates a piecewise linear frontier consisting of a number of tangent hyper planes. Three of the papers in this volume provide a detailed and relatively easy to follow exposition of the underlying theory from neoclassical production economics and offer step-by-step instructions on the appropriate model to apply in different contexts and how to implement them. Of particular appeal are the instructions on (i) how to write the codes for different SFA models on STATA, (ii) how to write a VBA Macro for repetitive solution of the DEA problem for each production unit on Excel Solver, and (iii) how to write the codes for the Nonparametric Convex Frontier estimation. The three other papers in the volume are primarily theoretical and will be of interest to PhD students and researchers hoping to make methodological and conceptual contributions to the field of nonparametric efficiency analysis.

Economists can use computer algebra systems to manipulate symbolic models, derive numerical computations, and analyze empirical relationships among variables. Maxima is an open-source multi-platform computer algebra system that rivals proprietary software. Maxima's symbolic and computational capabilities enable economists and financial analysts to develop a deeper understanding of models by allowing them to explore the implications of differences in parameter values, providing numerical solutions to problems that would be otherwise intractable, and by providing graphical representations that can guide analysis. This book provides a step-by-step tutorial for using this program to examine the economic relationships that form the core of microeconomics in a way that complements traditional modeling techniques. Readers learn how to phrase the relevant analysis and how symbolic expressions, numerical computations, and graphical representations can be used to learn from microeconomic models. In particular, comparative statics analysis is facilitated. Little has been published on Maxima and its applications in economics and finance, and this volume will appeal to advanced undergraduates, graduate-level students studying microeconomics, academic researchers in economics and finance, economists, and financial analysts.

From the Introduction: This volume is dedicated to the remarkable career of Professor Peter Schmidt and the role he has played in mentoring us, his PhD students. Peter's accomplishments are legendary among his students and the profession. Each of the papers in this Festschrift is a research work executed by a former PhD student of Peter's, from his days at the University of North Carolina at Chapel Hill to his time at Michigan State University. Most of the papers were presented at The Conference in Honor of Peter Schmidt, June 30 - July 2, 2011. The conference was largely attended by his former students and one current student, who traveled from as far as Europe and Asia to honor Peter. This was a conference to celebrate Peter's contribution to our contributions. By "our contributions" we mean the research papers that make up this Festschrift and the countless other publications by his students represented and not represented in this volume. Peter's students may have their families to thank for much that is positive in their lives. However, if we think about it, our professional lives would not be the same without the lessons and the approaches to decision making that we learned from Peter. We spent our days together at Peter's conference and the months since reminded of these aspects of our personalities and life goals that were enhanced, fostered, and nurtured by the very singular experiences we have had as Peter's students. We recognized in 2011 that it was unlikely we would all be together again to celebrate such a wonderful moment in ours and Peter's lives and pledged then to take full advantage of it. We did then, and we are now in the form of this volume.

Presents recent developments of probabilistic assessment of systems dependability based on stochastic models, including graph theory, finite state automaton and language theory, for both dynamic and hybrid contexts.

Handbook of Econometrics, Volume 7A, examines recent advances in foundational issues and "hot" topics within econometrics, such as inference for moment inequalities and estimation of high dimensional models. With its world-class editors and contributors, it succeeds in unifying leading studies of economic models, mathematical statistics and economic data. Our flourishing ability to address empirical problems in economics by using economic theory and statistical methods has driven the field of econometrics to unimaginable places. By designing methods of inference from data based on models of human choice behavior and social interactions, econometricians have created new subfields now sufficiently mature to require sophisticated literature summaries.

With a new author team contributing decades of practical experience, this fully updated and thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time series analysis. Taking a practical and example-driven approach, this textbook summarises the most critical decisions, techniques and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirely new set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends, seasonality, aberrant observations, conditional heteroskedasticity and ARCH models, non-linearity and multivariate time series, making this a complete practical guide. A companion website with downloadable datasets, exercises and lecture slides rounds out the full learning package.

The series is designed to bring together those mathematicians who are seriously interested in getting new challenging stimuli from economic theories with those economists who are seeking effective mathematical tools for their research. A lot of economic problems can be formulated as constrained optimizations and equilibration of their solutions. Various mathematical theories have been supplying economists with indispensable machineries for these problems arising in economic theory. Conversely, mathematicians have been stimulated by various mathematical difficulties raised by economic theories.

The research and its outcomes presented here focus on spatial sampling of agricultural resources. The authors introduce sampling designs and methods for producing accurate estimates of crop production for harvests across different regions and countries. With the help of real and simulated examples performed with the open-source software R, readers will learn about the different phases of spatial data collection. The agricultural data analyzed in this book help policymakers and market stakeholders to monitor the production of agricultural goods and its effects on environment and food safety.

This is a book on deterministic and stochastic Growth Theory and the computational methods needed to produce numerical solutions. Exogenous and endogenous growth models are thoroughly reviewed. Special attention is paid to the use of these models for fiscal and monetary policy analysis. Modern Business Cycle Theory, the New Keynesian Macroeconomics, the class of Dynamic Stochastic General Equilibrium models, can be all considered as special cases of models of economic growth, and they can be analyzed by the theoretical and numerical procedures provided in the textbook. Analytical discussions are presented in full detail. The book is self contained and it is designed so that the student advances in the theoretical and the computational issues in parallel. EXCEL and Matlab files are provided on an accompanying website (see Preface to the Second Edition) to illustrate theoretical results as well as to simulate the effects of economic policy interventions. The structure of these program files is described in "Numerical exercise"-type of sections, where the output of these programs is also interpreted. The second edition corrects a few typographical errors and improves some notation.

Increasing concerns regarding the world's natural resources and sustainability continue to be a major issue for global development. As a result several political initiatives and strategies for green or resource-efficient growth both on national and international levels have been proposed. A core element of these initiatives is the promotion of an increase of resource or material productivity. This dissertation examines material productivity developments in the OECD and BRICS countries between 1980 and 2008. By applying the concept of convergence stemming from economic growth theory to material productivity the analysis provides insights into both aspects: material productivity developments in general as well potentials for accelerated improvements in material productivity which consequently may allow a reduction of material use globally. The results of the convergence analysis underline the importance of policy-making with regard to technology and innovation policy enabling the production of resource-efficient products and services as well as technology transfer and diffusion.

The purpose of this book is to establish a connection between the traditional field of empirical economic research and the emerging area of empirical financial research and to build a bridge between theoretical developments in these areas and their application in practice. Accordingly, it covers broad topics in the theory and application of both empirical economic and financial research, including analysis of time series and the business cycle; different forecasting methods; new models for volatility, correlation and of high-frequency financial data and new approaches to panel regression, as well as a number of case studies. Most of the contributions reflect the state-of-art on the respective subject. The book offers a valuable reference work for researchers, university instructors, practitioners, government officials and graduate and post-graduate students, as well as an important resource for advanced seminars in empirical economic and financial research.

This book presents a novel approach to time series econometrics, which studies the behavior of nonlinear stochastic processes. This approach allows for an arbitrary dependence structure in the increments and provides a generalization with respect to the standard linear independent increments assumption of classical time series models. The book offers a solution to the problem of a general semiparametric approach, which is given by a concept called C-convolution (convolution of dependent variables), and the corresponding theory of convolution-based copulas. Intended for econometrics and statistics scholars with a special interest in time series analysis and copula functions (or other nonparametric approaches), the book is also useful for doctoral students with a basic knowledge of copula functions wanting to learn about the latest research developments in the field.

This book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of the problem. Chapter 2 presents the classical or frequentist approach to the problem as well as several alternative procedures and their properties. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations. It also discusses a significance Bayesian procedure. Lastly, Chapter 4 examines the pure likelihood approach. Various real-data examples and computer simulations are provided throughout the text.

This textbook provides a coherent introduction to the main concepts and methods of one-parameter statistical inference. Intended for students of Mathematics taking their first course in Statistics, the focus is on Statistics for Mathematicians rather than on Mathematical Statistics. The goal is not to focus on the mathematical/theoretical aspects of the subject, but rather to provide an introduction to the subject tailored to the mindset and tastes of Mathematics students, who are sometimes turned off by the informal nature of Statistics courses. This book can be used as the basis for an elementary semester-long first course on Statistics with a firm sense of direction that does not sacrifice rigor. The deeper goal of the text is to attract the attention of promising Mathematics students.

This book assesses how efficient primary and upper primary education is across different states of India considering both output oriented and input oriented measures of technical efficiency. It identifies the most important factors that could produce differential efficiency among the states, including the effects of central grants, school-specific infrastructures, social indicators and policy variables, as well as state-specific factors like per-capita net-state-domestic-product from the service sector, inequality in distribution of income (Gini coefficient), the percentage of people living below the poverty line and the density of population. The study covers the period 2005-06 to 2010-11 and all the states and union territories of India, which are categorized into two separate groups, namely: (i) General Category States (GCS); and (ii) Special Category States (SCS) and Union Territories (UT). It uses non-parametric Data Envelopment Analysis (DEA) and obtains the Technology Closeness Ratio (TCR), measuring whether the maximum output producible from an input bundle by a school within a given group is as high as what could be produced if the school could choose to join the other group. The major departure of this book is its approach to estimating technical efficiency (TE), which does not use a single frontier encompassing all the states and UT, as is done in the available literature. Rather, this method assumes that GCS, SCS and UT are not homogeneous and operate under different fiscal and economic conditions.

This book is a comprehensive introduction of the reader into the simulation and modelling techniques and their application in the management of organisations. The book is rooted in the thorough understanding of systems theory applied to organisations and focuses on how this theory can apply to econometric models used in the management of organisations. The econometric models in this book employ linear and dynamic programming, graph theory, queuing theory, game theory, etc. and are presented and analysed in various fields of application, such as investment management, stock management, strategic decision making, management of production costs and the lifecycle costs of quality and non-quality products, production quality Management, etc.

This edited book contains several state-of-the-art papers devoted to econometrics of risk. Some papers provide theoretical analysis of the corresponding mathematical, statistical, computational, and economical models. Other papers describe applications of the novel risk-related econometric techniques to real-life economic situations. The book presents new methods developed just recently, in particular, methods using non-Gaussian heavy-tailed distributions, methods using non-Gaussian copulas to properly take into account dependence between different quantities, methods taking into account imprecise ("fuzzy") expert knowledge, and many other innovative techniques. This versatile volume helps practitioners to learn how to apply new techniques of econometrics of risk, and researchers to further improve the existing models and to come up with new ideas on how to best take into account economic risks.

Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic modeling. However, the inherent limitation of game theory when dealing with many player games encourages the use of multi-agent systems for modeling economic phenomena. The artificial intelligence techniques used to model economic data include: multi-layer perceptron neural networks radial basis functions support vector machines rough sets genetic algorithm particle swarm optimization simulated annealing multi-agent system incremental learning fuzzy networks Signal processing techniques are explored to analyze economic data, and these techniques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and controling inflation, option pricing, modeling economic growth as well as portfolio optimization are examined. The relationship between economic dependency and interstate conflict is explored, and knowledge on how economics is useful to foster peace - and vice versa - is investigated. Economic Modeling Using Artificial Intelligence Methods deals with the issue of causality in the non-linear domain and applies the automatic relevance determination, the evidence framework, Bayesian approach and Granger causality to understand causality and correlation. Economic Modeling Using Artificial Intelligence Methods makes an important contribution to the area of econometrics, and is a valuable source of reference for graduate students, researchers and financial practitioners.

Though globalisation of the world economy is currently a powerful force, people’s international mobility appears to still be very limited. The goal of this book is to improve our knowledge of the true effects of migration flows. It includes contributions by prominent academic researchers analysing the socio-economic impact of migration in a variety of contexts: interconnection of people and trade flows, causes and consequences of capital remittances, understanding the macroeconomic impact of migration and the labour market effects of people’s flows. The latest analytical methodologies are employed in all chapters, while interesting policy guidelines emerge from the investigations. The style of the volume makes it accessible for both non-experts and advanced readers interested in this hot topic of today’s world.