Økonometri og økonomisk statistik

In this book, author Matheus Facure explains the untapped potential of causal inference for estimating impacts and effects.

Praktisk statistisk metode for økonomer indeholder en beskrivelse af de praktisk statistiskeværktøjer, som studerende ved samfundsøkonomiskeuddannelser bør kende.Bogen indøver grundlæggende begreber og beregnings metoder som: Procentvise stigninger,gennemsnitlige årlige vækstrater, uvejede og vejede gennemsnit, logaritmiskskala, indekstal, mål for spredning og ulighed,standardberegninger samt pris- og mængdeindeks, herunder kædede værdier.Der er desuden medtaget afsnit om beholdnings-og strømstørrelser, sæsonkorrektion, deflatering og nutidsværdi. Metoderne illustreresmed konkrete og simple taleksempler.Også udformning af tabeller og figurer behandles,og der gives gode råd og vink om udarbejdelsen af opgavebesvarelser. Bogen sigter først og fremmest mod anvendelseved universiteter, handelshøjskoler og handelsskoler,men kan bruges alle steder, hvor man beskæftiger sig med økonomisk-statistiske emner.

Delve into the captivating world of 'Unicornomics' with Dr. Stephanie Trail. From the historical roots to future trends, this exploration unravels the secrets of unicorn companies, their disruptive models, funding strategies, global impacts, and the challenges and triumphs that define their unprecedented success in modern business landscapes.

This book presents the econometric foundations and applications of multi-dimensional panels, including modern methods of big data analysis. In light of the big data revolution and the emergence of higher dimensional panel data sets, it provides new results to synthesize existing knowledge on the field. The first, theoretical part of the volume is providing the econometric foundations to deal with these new high-dimensional panel data sets. It not only synthesizes our current knowledge, but mostly, presents new research results. The second empirical part of the book provides insight into the most relevant applications in this area. These chapters are a mixture of surveys and new results, always focusing on the econometric problems and feasible solutions.This second extended and revised edition provides an update of all existent chapters to reflect on new developments in the area as well as several new chapters on topics such as machine learning, nonparametric models,networks, and multi-dimensional panels in health economics. The book serves as a standard reference work, a textbook for graduate students in economics, and a source of background material for professionals conducting empirical studies.

United Nations Correspondent quoted" I learned a lot from the book, was amazed by all milestones for the nation, and admired the transformation that hasBook reviewed for India Power, World Top5, Economy Growth, Best Of India Stocks, Best Of India Information Technology, Best Of India Tech Talent, Best Of India Ecommerce, Best Of India Exports, Best Of India Engineering, Best Of India Tourism, Best Of India Cuisine, Best Of India been happening in India, enjoyed every part of the book. Excellent" Economy United Nations Correspondent quote" I learned a lot from the book, was amazed by all milestones for the nation, and admired the transformation that hasIn short, this book is a comprehensive account of the distinguishing characteristics of India as a nation, its strengths, and accomplishments; an invaluable resource for anyone-- investors, businesses, travelers, or for teachers ---relevant for course adoption, in fact for anyone interested in India's fast-paced growth over a few decades.

Delve into the captivating world of 'Unicornomics' with Dr. Stephanie Trail. From the historical roots to future trends, this exploration unravels the secrets of unicorn companies, their disruptive models, funding strategies, global impacts, and the challenges and triumphs that define their unprecedented success in modern business landscapes.

Book Publication Date: Dec 13, 2023. Printed in full colorIntroductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts.The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. A detailed transition guide is available for instructors.

Book Publication Date: Dec 13, 2023. Printed in b&w.Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts.The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. A detailed transition guide is available for instructors.

This book provides readers with an analysis of econometric methods using statistical information to examine actual aspects of Japan's regional economy. Beginning with ways of ascertaining the economy of a region's production factors, the book describes methods for creating new data using various regional statistical data that link regions within prefectures.The topics addressed here are some of the most pressing issues in Japan's regional economy. These include economic ripple effects due to social capital and public investment, economic evaluation of the environment for sustainable growth, the impacts of overtourism congestion phenomenon, and the measurement of the total fertility rate of female employees in industry.The book provides a way to comprehend the actual conditions of Japan's regions and the level of regional economic sectoral effects and regional externalities¿an analytical method to obtain the information required for policy making. Combining socially related fields on a wide range of issues in Japan¿s regional economy, the detailed studies are aimed especially at researchers and policymakers working on econometric analysis research based on economic theory.

A Simple Method for Predicting Covariance Matrices of Financial Returns makes three contributions. First, it proposes a new method for predicting the time-varying covariance matrix of a vector of financial returns, building on a specific covariance estimator suggested by Engle in 2002. The second contribution proposes a new method for evaluating a covariance predictor, by considering the regret of the log-likelihood over some time period such as a quarter. The third contribution is an extensive empirical study of covariance predictors. The authors compare their method to other popular predictors, including rolling window, exponentially weighted moving average (EWMA) and generalized autoregressive conditional heteroscedastic (GARCH) type methods. After an introduction, Section 2 describes some common predictors, including the one that this method builds on. Section 3 introduces the proposed covariance predictor. Section 4 discusses methods for validating covariance predictors that measure both overall performance and reactivity to market changes. Section 5 describes the data used in the authors' first empirical studies and the results are provided in Section 6. The authors then discuss some extensions of and variations on the method, including realized covariance prediction (Section 7), handling large universes via factor models (Section 8), obtaining smooth covariance estimates (Section 9), and using the authors' covariance model to generate simulated returns (Section 10).

This book is a stand-alone component of the Data Literacy Fundamentals course: https://dataliteracy.com/courses/data-literacy-fundamentals/. A digital copy of the book is included with the course.The vast majority of people in the world today do not receive a formal education that adequately prepares them for the level of data literacy required of them in their careers and by their communities. As a result, many are being left behind by the transition to data-driven dialogues and decisions all around them, and they're seeking ways to break down the barriers that are preventing them from participating.Data Literacy Fundamentals by Ben Jones covers foundational topics such as the overall goal of data, various ways of measuring and categorizing the world, five different forms of data analysis and when they apply, pros and cons related to how we display data in tabular or graphic form, and the way teams work together to convert data into insight.This book has been written for anyone who is just getting started with data and who wants to feel more confident in their understanding of what it is, what it isn't, and what it's used for. This invaluable resource will cure you of your "dataphobia", teach you the basic concepts of data, and set you on a path of learning that will ultimately result in fluency in the language of data.Praise for Data Literacy Fundamentals:"There has never been a more important time in business, public service, media or our personal lives to realize there is a new literacy in town. Data literacy. An equalizing force in society, it is a ticket to ride. And it is based on a previously unnamed language - that of 'speaking data'. With Data Literacy Fundamentals, Ben Jones offers a great service - that of beginning to demystify the basics and setting the foundation. A must-read for anyone starting the journey to grow their own data literacy." -Valerie Logan, CEO of the DATA LODGE"Ben Jones has a remarkable gift to convey the power of data and analytics, through the journey that is data literacy. Readers of this book will gain insight, knowledge, and power to improve their data and analytics journey." - Jordan Morrow, VP of Data, Design, & Management Skills, Pluralsight

This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

This book explores the potential for renewable energy development and the adoption of sustainable production processes in Latin America and the Caribbean. By examining the energy transition process, the impact of environmental degradation, and the relationship between renewable energy sources and economic growth, the effects of increased globalisation and liberalisation in this part of the world are analysed. Particular attention is given to renewable energy investment, the energy-economics growth nexus, the impact of trade openness, and the mitigation of carbon emissions. This book aims to highlight econometric techniques that can be used to tackle issues relating to globalisation, the energy transition, and environmental degradation. It will be relevant to researchers and policymakers interested in energy and environmental economics.

Business Statistics of the United States is a comprehensive and practical collection of data from as early as 1913 that reflects the nation's economic performance.

"This text provides a state-of-the-art treatment of distributional regression, accompanied by real-world examples from diverse areas of application. Maximum likelihood, Bayesian and machine learning approaches are covered in-depth and contrasted, providing an integrated perspective on GAMLSS for researchers in statistics and other data-rich fields"--

Das Arbeitsbuch behandelt einen Querschnitt mathematischer Themen, die bei Bachelorstudiengängen wie Betriebswirtschafts- und Volkswirtschaftslehre, aber auch bei interdisziplinären Studiengängen mit einem Wirtschaftsanteil, wie Sozialökonomie, benötigt werden.Angefangen beim Mittelstufenschulstoff werden schrittweise die für das Studium wichtigen Themen und Methoden mit entsprechenden ökonomischen Anwendungen und Begriffen wie folgt erarbeitet:Das Thema, die Methode und zugehörige Begriffe werden eingeführt. Das methodische Vorgehen wird anhand einer Beispielaufgabe erläutert.Das Erlernen und Üben der Methode wird durch Aufgaben mit ausführlichen Lösungen ermöglicht.Verknüpft werden die mathematischen Themen jeweils mit Erläuterungen zu ökonomischen Begriffen und ökonomischen Anwendungen. Diese sind entweder als Anwendungsaufgaben mit Lösungen in den Abschnitten integriert oder werden als eigenständige Abschnittebehandelt.

Poverty and inequality have pervaded British society to this day, but this has not always been self-evident to contemporaries - popular understandings have depended on existing knowledge. Inequality Knowledge provides the first detailed history of the numbers about the gap between rich and poor. It shows how they were produced, used, and suppressed at times, and how activists, scientists, and journalists eventually wrestled control over the figures from the state. The book traces the making and the politics of statistical knowledge about economic inequality in the United Kingdom from the post-war era to the 1990s. What kind of knowledge was available to contemporaries about socio-economic disparities in Britain and how they evolved over time? How was this knowledge produced and by whom? What did policy makers and civil servants know about the extent of poverty and inequality in British society and to what extent did they take the distributional impact of their social and fiscal policies into account? Far from just a technical matter, inequality knowledge had far-reaching implications for key debates and the wider political culture in contemporary Britain. Historicizing inequality knowledge speaks to a long tradition of historical research about social class divisions and cultural representations of economic disparities in twentieth-century Britain.

This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in 'big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics?As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice. 

Financial economics refers to a subfield of economics that studies the allocation and utilization of resources in markets. It typically entails the development of sophisticated models to test the variables influencing a specific decision. Financial economics is crucial in recognizing risks, making investment decisions, and determining the value of assets or securities. It has two basic aspects which include risk management and diversification, and the current value. Econometrics is a branch of economics that involves the study of economic data using mathematical and statistical techniques. There are two types of econometrics, namely, applied econometrics and theoretical econometrics. Null hypothesis testing, regression models, R-squared, t-tests, p-values, etc. are the some of the major techniques used in econometrics. It can also be utilized to make predictions about future financial or economic trends. This book elucidates the concepts and innovative models around prospective developments with respect to financial economics and econometrics. With its detailed analyses and data, it will prove immensely beneficial to professionals and students involved in these areas at various levels.