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Author: Andrés Fernández Martin Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In this paper, the authors empirically assess the extent to which early release inefficiency and definitional change affect prediction precision. In particular, they carry out a series of ex-ante prediction experiments in order to examine: the marginal predictive content of the revision process, the trade-offs associated with predicting different releases of a variable, the importance of particular forms of definitional change, which the authors call "definitional breaks," and the rationality of early releases of economic variables. An important feature of our rationality tests is that they are based solely on the examination of ex-ante predictions, rather than being based on in-sample regression analysis, as are many tests in the extant literature. Their findings point to the importance of making real-time datasets available to forecasters, as the revision process has marginal predictive content, and because predictive accuracy increases when multiple releases of data are used when specifying and estimating prediction models. The authors also present new evidence that early releases of money are rational, whereas prices and output are irrational. Moreover, they find that regardless of which release of our price variable one specifies as the "target" variable to be predicted, using only "first release" data in model estimation and prediction construction yields mean square forecast error (MSFE) "best" predictions. On the other hand, models estimated and implemented using "latest available release" data are MSFE-best for predicting all releases of money. The authors argue that these contradictory findings are due to the relevance of definitional breaks in the data generating processes of the variables that they examine. In an empirical analysis, they examine the real-time predictive content of money for income, and they find that vector autoregressions with money do not perform significantly worse than autoregressions, when predicting output during the last 20 years.
Author: Andrés Fernández Martin Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In this paper, the authors empirically assess the extent to which early release inefficiency and definitional change affect prediction precision. In particular, they carry out a series of ex-ante prediction experiments in order to examine: the marginal predictive content of the revision process, the trade-offs associated with predicting different releases of a variable, the importance of particular forms of definitional change, which the authors call "definitional breaks," and the rationality of early releases of economic variables. An important feature of our rationality tests is that they are based solely on the examination of ex-ante predictions, rather than being based on in-sample regression analysis, as are many tests in the extant literature. Their findings point to the importance of making real-time datasets available to forecasters, as the revision process has marginal predictive content, and because predictive accuracy increases when multiple releases of data are used when specifying and estimating prediction models. The authors also present new evidence that early releases of money are rational, whereas prices and output are irrational. Moreover, they find that regardless of which release of our price variable one specifies as the "target" variable to be predicted, using only "first release" data in model estimation and prediction construction yields mean square forecast error (MSFE) "best" predictions. On the other hand, models estimated and implemented using "latest available release" data are MSFE-best for predicting all releases of money. The authors argue that these contradictory findings are due to the relevance of definitional breaks in the data generating processes of the variables that they examine. In an empirical analysis, they examine the real-time predictive content of money for income, and they find that vector autoregressions with money do not perform significantly worse than autoregressions, when predicting output during the last 20 years.
Author: Andrés Fernández Publisher: ISBN: Category : Economic forecasting Languages : en Pages : 35
Book Description
In this paper, the authors empirically assess the extent to which early release inefficiency and definitional change affect prediction precision. In particular, they carry out a series of ex-ante prediction experiments in order to examine: the marginal predictive content of the revision process, the trade-offs associated with predicting different releases of a variable, the importance of particular forms of definitional change, which the authors call "definitional breaks," and the rationality of early releases of economic variables. An important feature of our rationality tests is that they are based solely on the examination of ex-ante predictions, rather than being based on in-sample regression analysis, as are many tests in the extant literature. Their findings point to the importance of making real-time datasets available to forecasters, as the revision process has marginal predictive content, and because predictive accuracy increases when multiple releases of data are used when specifying and estimating prediction models. The authors also present new evidence that early releases of money are rational, whereas prices and output are irrational. Moreover, they find that regardless of which release of our price variable one specifies as the "target" variable to be predicted, using only "first release" data in model estimation and prediction construction yields mean square forecast error (MSFE) "best" predictions. On the other hand, models estimated and implemented using "latest available release" data are MSFE-best for predicting all releases of money. The authors argue that these contradictory findings are due to the relevance of definitional breaks in the data generating processes of the variables that they examine. In an empirical analysis, they examine the real-time predictive content of money for income, and they find that vector autoregressions with money do not perform significantly worse than autoregressions, when predicting output during the last 20 years.
Author: Maria L. Rizzo Publisher: CRC Press ISBN: 1420010719 Category : Reference Languages : en Pages : 412
Book Description
Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditiona
Author: Kristoffer Bjärkefur Publisher: World Bank Publications ISBN: 1464816956 Category : Business & Economics Languages : en Pages : 388
Book Description
Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University
Author: Md. Atiqur Rahman Ahad Publisher: Springer Science & Business Media ISBN: 1447147308 Category : Computers Languages : en Pages : 132
Book Description
Human action analysis and recognition is a relatively mature field, yet one which is often not well understood by students and researchers. The large number of possible variations in human motion and appearance, camera viewpoint, and environment, present considerable challenges. Some important and common problems remain unsolved by the computer vision community. However, many valuable approaches have been proposed over the past decade, including the motion history image (MHI) method. This method has received significant attention, as it offers greater robustness and performance than other techniques. This work presents a comprehensive review of these state-of-the-art approaches and their applications, with a particular focus on the MHI method and its variants.
Author: Michael O’Grady Publisher: Springer ISBN: 3319044060 Category : Computers Languages : en Pages : 347
Book Description
This book constitutes the refereed proceedings of the workshops co-located with the 4th International Joint Conference on Ambient Intelligence, AmI 2013, held in Dublin, Ireland, in December 2013. The 33 revised full papers presented were carefully reviewed and selected from numerous submissions to the following workshops: 5th International Workshop on Intelligent Environments Supporting Healthcare and Well-being (WISHWell’13) 3d International workshop on Pervasive and Context-Aware Middleware (PerCAM’13), 2nd International Workshop on Adaptive Robotic Ecologies (ARE'13), International Workshop on Aesthetic Intelligence (AxI'13), First International Workshop on Uncertainty in Ambient Intelligence (UAmI13). The papers are organized in topical sections on intelligent environments supporting healthcare and well-being; adaptive robotic ecologies; uncertainty in ambient intelligence; aesthetic intelligence; pervasive and context-aware middleware.
Author: Avery Plaw Publisher: Routledge ISBN: 1000317722 Category : Political Science Languages : en Pages : 226
Book Description
This book analyses the arrival of emerging and traditional information and technology for public and economic use in Latin America. It focuses on the governmental, economic and security issues and the study of the complex relationship between citizens and government. The book is divided into three parts: • ‘Digital data and privacy, prospects and barriers’ centers on the debates among the right of privacy and the loss of intimacy in the Internet, • ‘Homeland security and human rights’ focuses on how novel technologies such as drones and autonomous weapons systems reconfigure the strategies of police authorities and organized crime, • ‘Labor Markets, digital media and emerging technologies’ emphasize the legal, economic and social perils and challenges caused by the increased presence of social media, blockchain-based applications, artificial intelligence and automation technologies in the Latin American economy. This first volume in a two volume set will be important reading for scholars and students of governance in Latin American, the protection of human rights and the use of technology to combat crime and the new advances of digital economy in the region.
Author: Harvinder Singh Saini Publisher: Springer Nature ISBN: 9811520437 Category : Technology & Engineering Languages : en Pages : 639
Book Description
This book features a collection of high-quality, peer-reviewed research papers presented at the 7th International Conference on Innovations in Computer Science & Engineering (ICICSE 2019), held at Guru Nanak Institutions, Hyderabad, India, on 16–17 August 2019. Written by researchers from academia and industry, the book discusses a wide variety of industrial, engineering, and scientific applications of the emerging techniques in the field of computer science.
Author: Albert Bifet Publisher: MIT Press ISBN: 0262346052 Category : Computers Languages : en Pages : 255
Book Description
A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.
Author: Andrés Fernández Martin
Author: Andrés Fernández Martin
Author: Andrés Fernández
Author: Maria L. Rizzo
Author: Federal Reserve Bank of Philadelphia
Author: Kristoffer Bjärkefur
Author: Md. Atiqur Rahman Ahad
Author: Michael O’Grady
Author: Avery Plaw
Author: Harvinder Singh Saini
Author: Albert Bifet