Economic and Business Forecasting, Silvia John

Автор: Clements, Michael P.; Hendry, David F.
Название: The Oxford Handbook of Economic Forecasting
ISBN: 0195398645 ISBN-13(EAN): 9780195398649
Издательство: Oxford Academ
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Цена: 161040.00 T
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Описание: This Handbook provides up-to-date coverage of both new developments and well-established fields in the sphere of economic forecasting. The chapters are written by world experts in their respective fields, and are authoritative yet accessible.

Автор: Harvey, Andrew C.
Название: Forecasting, structural time series models and the kalman filter
ISBN: 0521405734 ISBN-13(EAN): 9780521405737
Издательство: Cambridge Academ
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Цена: 40120.00 T
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Описание: This book is concerned with modelling economic and social time series and with addressing the special problems which the treatment of such series pose. It is unique in its use of Kalman filtering with econometric and time series modelling.

Автор: Galimkair Mutanov
Название: Mathematical Methods and Models in Economic Planning, Management and Budgeting
ISBN: 3662451417 ISBN-13(EAN): 9783662451410
Издательство: Springer
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Цена: 121110.00 T
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Описание: This book describes a system of mathematical models and methods that can be used to analyze real economic and managerial decisions and to improve their effectiveness.

Автор: Ummer EK
Название: Basic Mathematics for Economics, Business and Finance
ISBN: 0415664209 ISBN-13(EAN): 9780415664202
Издательство: Taylor&Francis
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Цена: 81650.00 T
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Описание: Most of the graduate programs and journal articles in economics, business and finance use high level mathematical techniques and tools. This book will be appropriate to meet graduate mathematical requirements and help to prepare students to read and understand the content. It can help to formulate a strong foundation for their graduate studies in these subjects.

Автор: Bell
Название: Economic Time Series
ISBN: 143984657X ISBN-13(EAN): 9781439846575
Издательство: Taylor&Francis
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Цена: 132710.00 T
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Описание: Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time series modeling and seasonal adjustment, as is reflected both in the contents of the chapters and in their authorship, with contributors coming from academia and government statistical agencies. For easier perusal and absorption, the contents have been grouped into seven topical sections: Section I deals with periodic modeling of time series, introducing, applying, and comparing various seasonally periodic models Section II examines the estimation of time series components when models for series are misspecified in some sense, and the broader implications this has for seasonal adjustment and business cycle estimation Section III examines the quantification of error in X-11 seasonal adjustments, with comparisons to error in model-based seasonal adjustments Section IV discusses some practical problems that arise in seasonal adjustment: developing asymmetric trend-cycle filters, dealing with both temporal and contemporaneous benchmark constraints, detecting trading-day effects in monthly and quarterly time series, and using diagnostics in conjunction with model-based seasonal adjustment Section V explores outlier detection and the modeling of time series containing extreme values, developing new procedures and extending previous work Section VI examines some alternative models and inference procedures for analysis of seasonal economic time series Section VII deals with aspects of modeling, estimation, and forecasting for nonseasonal economic time series By presenting new methodological developments as well as pertinent empirical analyses and reviews of established methods, the book provides much that is stimulating and practically useful for the serious researcher and analyst of economic time series.

Автор: Brooks
Название: Real Estate Modelling and Forecasting
ISBN: 0521873398 ISBN-13(EAN): 9780521873390
Издательство: Cambridge Academ
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Цена: 99270.00 T
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Описание: Assuming only a basic understanding of econometrics, this book introduces and explains a broad range of quantitative techniques that are relevant for the analysis of real estate data. It includes numerous detailed examples, giving readers the confidence they need to estimate and interpret their own models.

Автор: Franses
Название: Time Series Models for Business and Economic Forecasting
ISBN: 0521520916 ISBN-13(EAN): 9780521520911
Издательство: Cambridge Academ
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Цена: 49630.00 T
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Описание: With a new author team contributing decades of practical experience, this fully updated second edition textbook summarises the most critical decisions, techniques and steps in creating effective forecasting models. Includes all new theoretical and practical exercises geared at guiding students through the steps of creating forecasting models on their own.

Автор: Diebold Francis
Название: Yield Curve Modeling and Forecasting?
ISBN: 0691146802 ISBN-13(EAN): 9780691146805
Издательство: Wiley
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Цена: 47520.00 T
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Описание: Offers an understanding of the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, and valuing capital goods. This title contains essential tools for academics, central banks, and more.

Автор: Franses
Название: Time Series Models for Business and Economic Forecasting
ISBN: 0521817706 ISBN-13(EAN): 9780521817707
Издательство: Cambridge Academ
Рейтинг:
Цена: 99270.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With a new author team contributing decades of practical experience, this fully updated second edition textbook summarises the most critical decisions, techniques and steps in creating effective forecasting models. Includes all new theoretical and practical exercises geared at guiding students through the steps of creating forecasting models on their own.

Автор: Baum
Название: An Introduction to Modern Econometrics Using Stata
ISBN: 1597180130 ISBN-13(EAN): 9781597180139
Издательство: Taylor&Francis
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Цена: 88800.00 T
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Описание:

Integrating a contemporary approach to econometrics with the powerful computational tools offered by Stata, An Introduction to Modern Econometrics Using Stata focuses on the role of method-of-moments estimators, hypothesis testing, and specification analysis and provides practical examples that show how the theories are applied to real data sets using Stata.

As an expert in Stata, the author successfully guides readers from the basic elements of Stata to the core econometric topics. He first describes the fundamental components needed to effectively use Stata. The book then covers the multiple linear regression model, linear and nonlinear Wald tests, constrained least-squares estimation, Lagrange multiplier tests, and hypothesis testing of nonnested models. Subsequent chapters center on the consequences of failures of the linear regression model's assumptions. The book also examines indicator variables, interaction effects, weak instruments, underidentification, and generalized method-of-moments estimation. The final chapters introduce panel-data analysis and discrete- and limited-dependent variables and the two appendices discuss how to import data into Stata and Stata programming.

Presenting many of the econometric theories used in modern empirical research, this introduction illustrates how to apply these concepts using Stata. The book serves both as a supplementary text for undergraduate and graduate students and as a clear guide for economists and financial analysts.

Автор: Elliott Graham, Timmermann Allan
Название: Economic Forecasting
ISBN: 0691140138 ISBN-13(EAN): 9780691140131
Издательство: Wiley
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Цена: 84480.00 T
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Описание: Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. Economi