This is an introductory textbook on spatial analysis and spatial statistics through GIS. Each chapter presents methods and metrics, explains how to interpret results, and provides worked examples. Topics include: describing and mapping data through exploratory spatial data analysis; analyzing geographic distributions and point patterns; spatial autocorrelation; spatial clustering; geographically weighted regression and OLS regression; and spatial econometrics. The worked examples link theory to practice through a single real-world case study, with software and illustrated guidance. Exercises are solved twice: first through ArcGIS, and then GeoDa. Through a simple methodological framework the book describes the dataset, explores spatial relations and associations, and builds models. Results are critically interpreted, and the advantages and pitfalls of using various spatial analysis methods are discussed. This is a valuable resource for graduate students and researchers analyzing geospatial data through a spatial analysis lens, including those using GIS in the environmental sciences, geography, and social sciences.

Getting accurate data on less developed countries has created great problems for studying these areas. Yet until recently students of development economics have relied on standard econometrics texts, which assume a Western context. Econometrics and Data Analysis for Developing Countries solves this problem. It will be essential reading for all advanced students of development economics.

This important new dictionary - the first of its kind, now available in paperback - presents an accessible source of reference on the main concepts and techniques in econometrics. Featuring entries on all the major areas in theoretical econometrics, the dictionary will be used by students, both undergraduate and postgraduate, to aid their understanding of the subject. Sorted by alphabetical order, each entry is a short essay which is designed to present the essential points of a particular concept or technique and offer a concise guide to other relevant literature. Written in an accessible and discursive style, the book adopts non-technical language to make the topics accessible to those who need to know more about applied econometrics and the underlying econometric theory. It will be widely welcomed as an indispensable supplement to the standard textbook literature and will be particularly well suited to students following modular courses. An essential source of reference for both undergraduate and post graduate students, the dictionary will also be useful for professional economists seeking to keep abreast of the latest developments in econometrics.

The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially artificial intelligence methods, are used in the case studies. The publication presents both traditional and innovative methods on the theoretical level, always accompanied by a case study, i.e. their specific use in practice. Furthermore, a comprehensive comparative analysis of the individual methods is provided. The book is intended for readers from the ranks of academic staff, students of universities of economics, but also the scientists and practitioners dealing with the time series prediction. From the point of view of practical application, it could provide useful information for speculators and traders on financial markets, especially the commodity markets.

This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader's analytical capability.

This book proposes new solutions to the problem of poverty, and begins with providing analyses. It bases most of the analyses and solutions in the context of the digital era. The book also follows, in addition to a scientific distribution, a spatial-geographical one: analyses of countries of the European Union as well as South Africa, while it referring to two main variables, television and art, as agents of poverty alleviation. The book places particular focus on how poverty is understood in the framework of Industry 4.0. It introduces a new expanded Multidimensional Poverty Index with more than 20 dimensions; moreover, it provides a mathematically based solution for the disposal of perishable food. Finally, it does not disregard the crucial aspect of the issue of poverty: that of education planning. This book is of interest to specialists in poverty research, from students to professionals and from professors to activists, without excluding engineers.

Modelling trends and cycles in economic time series has a long history, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s. Several developments in econometrics then led to an overhaul of the techniques used to extract trends and cycles from time series. In this second edition, Terence Mills expands on the research in the area of trends and cycles over the last (almost) two decades, to highlight to students and researchers the variety of techniques and the considerations that underpin their choice for modelling trends and cycles.

This book develops the theory of productivity measurement using the empirical index number approach. The theory uses multiplicative indices and additive indicators as measurement tools, instead of relying on the usual neo-classical assumptions, such as the existence of a production function characterized by constant returns to scale, optimizing behavior of the economic agents, and perfect foresight. The theory can be applied to all the common levels of aggregation (micro, meso, and macro), and half of the book is devoted to accounting for the links existing between the various levels. Basic insights from National Accounts are thereby used. The final chapter is devoted to the decomposition of productivity change into the contributions of efficiency change, technological change, scale effects, and input or output mix effects. Applications on real-life data demonstrate the empirical feasibility of the theory. The book is directed to a variety of overlapping audiences: statisticians involved in measuring productivity change; economists interested in growth accounting; researchers relating macro-economic productivity change to its industrial sources; enterprise micro-data researchers; and business analysts interested in performance measurement.

This book provides the ultimate goal of economic studies to predict how the economy develops-and what will happen if we implement different policies. To be able to do that, we need to have a good understanding of what causes what in economics. Prediction and causality in economics are the main topics of this book's chapters; they use both more traditional and more innovative techniques-including quantum ideas -- to make predictions about the world economy (international trade, exchange rates), about a country's economy (gross domestic product, stock index, inflation rate), and about individual enterprises, banks, and micro-finance institutions: their future performance (including the risk of bankruptcy), their stock prices, and their liquidity. Several papers study how COVID-19 has influenced the world economy. This book helps practitioners and researchers to learn more about prediction and causality in economics -- and to further develop this important research direction.

This monograph addresses the methodological and empirical issues relevant for the development of sustainable agriculture, with a particular focus on Eastern Europe. It relates economic growth to the other dimensions of sustainability by applying integrated methods. The book comprises five chapters dedicated to the theoretical approaches towards sustainable rural development, productivity analysis, structural change analysis and environmental footprint. The book focuses on the transformations of the agricultural sector while taking into account economic, environmental, and social dynamics. The importance of agricultural transformations to the livelihood of the rural population and food security are highlighted. Further, advanced methodologies and frameworks are presented to fathom the underlying trends in different facets of agricultural production. The authors present statistical methods used for the analysis of agricultural sustainability along with applications for agriculture in the European Union. Additionally, they discuss the measures of efficiency, methodological approaches and empirical models. Finally, the book applies econometric and optimization techniques, which are useful for the estimation of the production functions and other representations of technology in the case of the European Union member states. Therefore, the book is a must-read for researchers and students of agricultural and production economics, as well as policy-makers and academia in general.

The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity risk, optimal order placement as well as high-frequency volatility. This book provides a state-of-the art overview on the major approaches in high-frequency econometrics, including univariate and multivariate autoregressive conditional mean approaches for different types of high-frequency variables, intensity-based approaches for financial point processes and dynamic factor models. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications to volatility and liquidity estimation, order book modelling and market microstructure analysis.

This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables. It will also benefit students using the methodology for the first time, and practitioners at private or public organizations who are interested in modeling different fragments of the conditional distribution of a given variable. The book pursues a practical approach with reference to energy markets, helping readers learn the main features of the technique more quickly. Emphasis is placed on the implementation details and the correct interpretation of the quantile regression coefficients rather than on the technicalities of the method, unlike the approach used in the majority of the literature. All applications are illustrated with R.

The University of Oxford has been and continues to be one of the most important global centres for economics. With six chapters on themes in Oxford economics and 24 chapters on the lives and work of Oxford economists, this volume shows how economics became established at the University, how it produced some of the world's best-known economists, including Francis Ysidro Edgeworth, Roy Harrod and David Hendry, and how it remains a global force for the very best in teaching and research in economics. With original contributions from a stellar cast, this volume provides economists - especially those interested in macroeconomics and the history of economic thought - with the first in-depth analysis of Oxford economics.

Originally published in 1979. An Input/output database is an information system carrying current data on the intermediate consumption of any product or service by all the specified major firms that consume it. This book begins with a survey of how the interrelationships of an economic system can be represented in a two-dimensional model which traces the output of each economic sector to all other sectors. It talks about how the use of such databases to identify major buyers and sellers can illuminate problems of economic policy at the national, regional, and corporate level and aid in analyzing factors affecting the control of inflation, energy use, transportation, and environmental pollution. The book discusses how advances in database technology, have brought to the fore such issues as the right to individual privacy, corporate secrecy, the public's right of access to stored data, and the use of such information for national planning in a free-enterprise society.

Originally published in 1989. ECESIS consists of 51 regional econometric models (one for each state and the District of Columbia) and a multiregional demographic model. Its distinguishing feature is the linking of sophisticated demographic accounts with sophisticated structural econometric models. This book, looking at how strong the interactions are between population dynamics and economic activity, determines to what extent the simultaneous economic-demographic interregional model provides improved projection and simulation properties over regional economic and demographic models used independently of one another.

This advanced undergraduate/graduate textbook teaches students in finance and economics how to use R to analyse financial data and implement financial models. It demonstrates how to take publically available data and manipulate, implement models and generate outputs typical for particular analyses. A wide spectrum of timely and practical issues in financial modelling are covered including return and risk measurement, portfolio management, option pricing and fixed income analysis. This new edition updates and expands upon the existing material providing updated examples and new chapters on equities, simulation and trading strategies, including machine learnings techniques. Select data sets are available online.

This book helps economists with the difficult task of constructing econometric models and will be especially useful to those taking courses in applied econometrics who need to learn how to evaluate the validity of the theories and techniques they are taught. The volume contains seventeen papers by the leading authorities in the field, divided into four groups, to each of which the editor provides an introduction. The whole volume is prefaced with an editorial discussion of the controversies of the subject. The methods critically discussed include the traditional ones, such as vector auto-regressions; Bayesian techniques; and the comprehensive modelling strategy advocated by reseachers at the London School of Economics. The papers vary in the degree of sophistication used, but anyone reading the book should gain a sound knowledge of the practical difficulties involved in model specification, evaluation, and interpretation.

This volume presents new methods and applications in longitudinal data estimation methodology in applied economic. Featuring selected papers from the 2020 the International Conference on Applied Economics (ICOAE 2020) held virtually due to the corona virus pandemic, this book examines interdisciplinary topics such as financial economics, international economics, agricultural economics, marketing and management. Country specific case studies are also featured.

This book offers a series of statistical tests to determine if the "crowd out" problem, known to hinder the effectiveness of Keynesian economic stimulus programs, can be overcome by monetary programs. It concludes there are programs that can do this, specifically "accommodative monetary policy." They were not used to any great extent prior to the Quantitative Easing program in 2008, causing the failure of many fiscal stimulus programs through no fault of their own. The book includes exhaustive statistical tests to prove this point. There is also a policy analysis section of the book. It examines how effectively the Federal Reserve's anti-crowd out programs have actually worked, to the extent they were undertaken at all. It finds statistical evidence that using commercial and savings banks instead of investment banks when implementing accommodating monetary policy would have markedly improved their effectiveness. This volume, with its companion volume Why Fiscal Stimulus Programs Fail, Volume 2: Statistical Tests Comparing Monetary Policy to Growth, provides 1000 separate statistical tests on the US economy to prove these assertions.

This book gives a thorough and systematic introduction to the latest research results about fuzzy decision-making method based on prospect theory. It includes eight chapters: Introduction, Intuitionistic fuzzy MADM based on prospect theory, QUALIFLEX based on prospect theory with probabilistic linguistic information, Group PROMETHEE based on prospect theory with hesitant fuzzy linguistic information, Prospect consensus with probabilistic hesitant fuzzy preference information, Improved TODIM based on prospect theory and the improved TODIM with probabilistic hesitant fuzzy information, etc. This book is suitable for the researchers in the fields of fuzzy mathematics, operations research, behavioral science, management science and engineering, etc. It is also useful as a textbook for postgraduate and senior-year undergraduate students of the relevant professional institutions of higher learning.

Applications of queueing network models have multiplied in the last generation, including scheduling of large manufacturing systems, control of patient flow in health systems, load balancing in cloud computing, and matching in ride sharing. These problems are too large and complex for exact solution, but their scale allows approximation. This book is the first comprehensive treatment of fluid scaling, diffusion scaling, and many-server scaling in a single text presented at a level suitable for graduate students. Fluid scaling is used to verify stability, in particular treating max weight policies, and to study optimal control of transient queueing networks. Diffusion scaling is used to control systems in balanced heavy traffic, by solving for optimal scheduling, admission control, and routing in Brownian networks. Many-server scaling is studied in the quality and efficiency driven Halfin-Whitt regime and applied to load balancing in the supermarket model and to bipartite matching in ride-sharing applications.

This book scientifically tests the assertion that accommodative monetary policy can eliminate the "crowd out" problem, allowing fiscal stimulus programs (such as tax cuts or increased government spending) to stimulate the economy as intended. It also tests to see if natural growth in th economy can cure the crowd out problem as well or better. The book is intended to be the largest scale scientific test ever performed on this topic. It includes about 800 separate statistical tests on the U.S. economy testing different parts or all of the period 1960 - 2010. These tests focus on whether accommodative monetary policy, which increases the pool of loanable resources, can offset the crowd out problem as well as natural growth in the economy. The book, employing the best scientific methods available to economists for this type of problem, concludes accommodate monetary policy could have, but until the quantitative easing program, Federal Reserve efforts to accommodate fiscal stimulus programs were not large enough to offset more than 23% to 44% of any one year's crowd out problem. That provides the science part of the answer as to why accommodative monetary policy didn't accommodate: too little of it was tried. The book also tests whether other increases in loanable funds, occurring because of natural growth in the economy or changes in the savings rate can also offset crowd out. It concludes they can, and that these changes tend to be several times as effective as accommodative monetary policy. This book's companion volume Why Fiscal Stimulus Programs Fail explores the policy implications of these results.

This book analyzes evolution of monetary policy in Rwanda since it was first implemented by the National Bank of Rwanda in 1964 when the bank was established. It contributes to the understanding of monetary policy which is formulation and implementation in different stages of development of a financial system that comprises the financial market (money market and capital market), financial intermediaries such as commercial banks, and the financial sector infrastructures such as payment systems and the credit reference bureau. The book breaks down applied empirical research on the assessment of key assumptions of a monetary targeting framework, namely the stability of money multiplier and money demand using econometrics of time series, through a number of case studies. Presenting a detailed empirical analysis of the monetary transmission mechanism, one of the most analyzed topics in central banks in advanced economies, this book is a valuable read for central bankers and other researchers of monetary policy, particularly in developing economies.

This handbook presents emerging research exploring the theoretical and practical aspects of econometric techniques for the financial sector and their applications in economics. By doing so, it offers invaluable tools for predicting and weighing the risks of multiple investments by incorporating data analysis. Throughout the book the authors address a broad range of topics such as predictive analysis, monetary policy, economic growth, systemic risk and investment behavior. This book is a must-read for researchers, scholars and practitioners in the field of economics who are interested in a better understanding of current research on the application of econometric methods to financial sector data.

Applied Econometrics: A Practical Guide is an extremely user-friendly and application-focused book on econometrics. Unlike many econometrics textbooks which are heavily theoretical on abstractions, this book is perfect for beginners and promises simplicity and practicality to the understanding of econometric models. Written in an easy-to-read manner, the book begins with hypothesis testing and moves forth to simple and multiple regression models. It also includes advanced topics:

Endogeneity and Two-stage Least Squares

Simultaneous Equations Models

Panel Data Models

Qualitative and Limited Dependent Variable Models

Vector Autoregressive (VAR) Models

Autocorrelation and ARCH/GARCH Models

Unit Root and Cointegration

The book also illustrates the use of computer software (EViews, SAS and R) for economic estimating and modeling. Its practical applications make the book an instrumental, go-to guide for solid foundation in the fundamentals of econometrics. In addition, this book includes excerpts from relevant articles published in top-tier academic journals. This integration of published articles helps the readers to understand how econometric models are applied to real-world use cases.

Table of Contents

1. Review of Estimation and Hypothesis Tests

2. Simple Linear Regression Models

3. Multiple Linear Regression Models

4. Dummy Explanatory Variables

5. More on Multiple Regression Analysis

6. Endogeneity and Two-Stage Least Squares Estimation

7. Models for Panel Data

8. Simultaneous Equations Models

9. Vector Autoregressive (VAR) Models

10. Autocorrelation and ARCH/GARCH

11. Unit Root, Cointegration and Error Correction Model

12. Qualitative and Limited Dependent Variable Models