Økonometri og økonomisk statistik

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.

This book covers Big Data, Machine Learning, and Artificial Intelligences related technologies and how these technologies can enable design, development, and delivery of customer-focused financial services to both corporate and retail customers and how to extend the benefits to the financially excluded sections of society.

This book presents contemporary issues and challenges in finance and risk management in a time of rapid transformation due to technological advancements. It includes research articles based on financial and economic data and intends to cover the emerging role of analytics in financial management, asset management, and risk management.

This book will learn you how to create SAS code, in a way easy to understand with the use of best practises for SAS programming.The audience for this book can be new students that want to take advantage of a real programming environment for the SAS language, and not just the point-and-click environments available. Also for python or R users that need some of the advanced datamanagement facilities in SAS.But other readers are also welcome to take advantage of this book.

This book provides an analytical and computational approach to solving and simulating the Mahalanobis model and the papers surrounding it. The book comes up, perhaps for the first time, with a holistic examination of an important growth model that emerged out of India in the 1950s. It contains detailed derivations of the Mahalanobis model and the several critiques and extensions surrounding it with an organized synthesis of the main results. Computationally, the book simulates the model and its many variants, thus making it accessible to a wider audience. Advanced undergraduates and beginning graduate students in the fields of Economics, Mathematics, and Statistics will gain immensely from understanding both the mathematical aspects as well as the computational aspects of the Mahalanobis model. In the absence of a single 'go-to' source on all aspects of the model -- analytical and computational -- this book is a definitive volume on the Mahalanobis model that has allthe derivations of all the papers surrounding the model, its dissents and critiques, and extensions as in the wage goods model suggested by Vakil and Brahmananda.

Printed in b&w.¿Introductory Business Statistics is designed to meet the scope and sequence requirements of the one-semester statistics course for business, economics, and related majors. Core statistical concepts and skills have been augmented with practical business examples, scenarios, and exercises. The result is a meaningful understanding of the discipline, which will serve students in their business careers and real-world experiences.

Printed in color.¿Introductory Business Statistics is designed to meet the scope and sequence requirements of the one-semester statistics course for business, economics, and related majors. Core statistical concepts and skills have been augmented with practical business examples, scenarios, and exercises. The result is a meaningful understanding of the discipline, which will serve students in their business careers and real-world experiences.

This book overviews latest ideas and developments in financial econometrics, with an emphasis on how to best use prior knowledge (e.g., Bayesian way) and how to best use successful data processing techniques from other application areas (e.g., from quantum physics). The book also covers applications to economy-related phenomena ranging from traditionally analyzed phenomena such as manufacturing, food industry, and taxes, to newer-to-analyze phenomena such as cryptocurrencies, influencer marketing, COVID-19 pandemic, financial fraud detection, corruption, and shadow economy. This book will inspire practitioners to learn how to apply state-of-the-art Bayesian, quantum, and related techniques to economic and financial problems and inspire researchers to further improve the existing techniques and come up with new techniques for studying economic and financial phenomena. The book will also be of interest to students interested in latest ideas and results.

"An original, bold, and convincing argument for a cap on wealth by the philosopher who coined the term "limitarianism" that invites us to a radical reimagining of our world"--

Forecasting from multi-equation models has very rarely been the focus in econometric literature. In response, this book presents a range of methodologies to approach this complex field and offers readers essential information on forecasting from multi-equation econometric micromodels.In the twentieth century, significant interest in econometric macromodels emerged. These multi-equation models are mostly systems of interdependent equations, most often used to describe the national economies of various countries. The book analyzes econometric forecasting procedures and illustrates them with empirical examples that are based on real economic (mostly business-derived) data. The procedure of forecast building from systems of interdependent equations is presented for two categories of econometric models: models with a feedback effect and models with closed-loop links between interdependent variables. The forecasts obtained via this technique are compared with the results derived from reduced-form equations of the respective econometric model. The author also generalizes the rules of the reduced-recursive (helical, iterative) procedure application, against the backdrop of the proposed method of forecast building from reduced-form equations of systems of interdependent equations. Given its scope, the book will appeal not only to PhD students and researchers, but also undergraduate students and academics in general.

How might one determine if a financial institution is taking risk in a balanced and productive manner? A powerful tool to address this question is economic capital, which is a model-based measure of the amount of equity that an entity must hold to satisfactorily offset its risk-generating activities. This book, with a particular focus on the credit-risk dimension, pragmatically explores real-world economic-capital methodologies and applications. It begins with the thorny practical issues surrounding the construction of an (industrial-strength) credit-risk economic-capital model, defensibly determining its parameters, and ensuring its efficient implementation. It then broadens its gaze to examine various critical applications and extensions of economic capital; these include loan pricing, the computation of loan impairments, and stress testing. Along the way, typically working from first principles, various possible modelling choices and related concepts are examined. The end result is a useful reference for students and practitioners wishing to learn more about a centrally important financial-management device.

This book aims at providing an empirical understanding of the main drivers affecting investors¿ preferences in financing new ventures through equity crowdfunding (ECF) and determining fundraising campaign success. ECF is increasing in prominence as a route for new ventures in obtaining external financial resources. To raise capital, entrepreneurs are required to convey quality signals of their proposals with real-time information and knowledge sharing. This book advances knowledge in entrepreneurial finance by investigating the factors that affect individuals¿ decisions to participate in ECF. The authors adopt a data mining approach to extract publicly available information from a multitude of crowdfunding platforms across different countries, producing a unique dataset. The book uses an innovative hybrid analysis to generate knowledge patterns creating data-driven models on one hand, and on the other test research hypotheses adoptingstatistical models to investigate empirical evidence in line, or in contrast, with the extant literature. The book also integrates organizational theories to examine the extent to which ECF platform managers follow a strategy of isomorphism in their choice of information disclosure. The final part of the book discusses how signals are interpreted by investors, how these affect financing preferences, and ultimately the successful completion of a fundraising campaign. The book will be of interest to academics and practitioners in entrepreneurial finance, FinTech, and investment behaviour.

This book presents a selection of peer-reviewed contributions on the latest developments in time series analysis and forecasting, presented at the 7th International Conference on Time Series and Forecasting, ITISE 2021, held in Gran Canaria, Spain, July 19-21, 2021. It is divided into four parts. The first part addresses general modern methods and theoretical aspects of time series analysis and forecasting, while the remaining three parts focus on forecasting methods in econometrics, time series forecasting and prediction, and numerous other real-world applications. Covering a broad range of topics, the book will give readers a modern perspective on the subject.The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics,statistics and econometrics.

Seasonality in economic time series can "obscure" movements of other components in a series that are operationally more important for economic and econometric analyses. In practice, one often prefers to work with seasonally adjusted data to assess the current state of the economy and its future course.This book presents a seasonal adjustment program called CAMPLET, an acronym of its tuning parameters, which consists of a simple adaptive procedure to extract the seasonal and the non-seasonal component from an observed series. Once this process is carried out, there will be no need to revise these components at a later stage when new observations become available.The authors describe the main features of CAMPLET, evaluate the outcomes of CAMPLET and X-13ARIMA-SEATS in a controlled simulation framework using a variety of data generating processes, and illustrate CAMPLET and X-13ARIMA-SEATS with three time series: US non-farm payroll employment, operational income of Ahold and real GDP in the Netherlands. Furthermore they show how CAMPLET performs under the COVID-19 crisis, and its attractiveness in dealing with daily data.This book appeals to scholars and students of econometrics and statistics, interested in the application of statistical methods for empirical economic modeling.

Are you ready to take control of your financial future? If you're looking to build long-term wealth and achieve financial freedom, then real estate investing could be your ticket to success.But the world of real estate can be overwhelming, and making a mistake can be costly. That's where this book comes in. With years of experience in the real estate market, the author has created a step-by-step guide to help you achieve your investing goals.In this comprehensive guide, you'll learn everything you need to know to confidently invest in real estate. From analyzing properties and identifying lucrative opportunities to managing your investments and maximizing profits, this book covers all the essential strategies and techniques you need to succeed.You'll discover the most common mistakes investors make and how to avoid them, along with aggressive and conservative strategies for investing. Plus, you'll learn alternative approaches and tips to help you build a rewarding passive income stream.So if you're ready to take your financial future into your own hands, get your copy today. It's time to start building the life you've always wanted.

This Open Access Brief presents the KAPSARC Global Energy Macroeconometric Model (KGEMM). KGEMM is a policy analysis tool for examining the impacts of domestic policy measures and global economic and energy shocks on the Kingdom of Saudi Arabia. The model has eight blocks (real sector, fiscal, monetary, external sector, price, labor and wages, energy, population, and age cohorts) that interact with each other to represent the Kingdom¿s macroeconomy and energy linkages. It captures New Keynesian demand-side features anchored to medium-run equilibrium and long-run aggregate supply. It applies a cointegration and equilibrium correction modeling (ECM) methodology to time series data to estimate the model¿s behavioral equations in the framework of Autometrics, a general-to-specific econometric modeling strategy. Hence, the model combines ¿theory-driven¿ approach with ¿data-driven¿ approach.The Brief begins with an introduction to the theoretical framework of the model and the KGEMM methodology and then walks the reader through the structure of the model and its behavioral equations. The book closes with simulations showing the application of the model. Providing a detailed introduction to a cutting-edge, robust predictive model, this Brief will be of great use to researchers and policymakers interested in macroeconomics, energy economics, econometrics, and more specifically, the economy of Saudi Arabia.