Written in EnglishRead online
|Statement||Peter Hackl, ed.|
|Contributions||Hackl, Peter., International Institute for Applied Systems Analysis.|
|LC Classifications||HB3730 .S735 1989|
|The Physical Object|
|Pagination||xix, 488 p. :|
|Number of Pages||488|
|ISBN 10||3540514546, 0387514546|
|LC Control Number||89019719|
Download Statistical analysis and forecasting of economic structural change
Statistical Analysis and Forecasting of Economic Structural Change Softcover reprint of the original 1st ed.
Edition by Peter Hackl (Editor) › Visit Amazon's Peter Hackl Page. Find all the books, read about the author, and more. Format: Paperback. Statistical Analysis and Forecasting of Economic Structural Change. Editors (view affiliations) About this book. Introduction.
Inthe University of Bonn (FRG) and the International Institute for Applied System Analysis (IIASA) in Laxenburg (Austria), created a joint research group to analyze the relationship between economic growth. Inthe University of Bonn (FRG) and the International Institute for Applied System Analysis (IIASA) in Laxenburg (Austria), created a joint research group to analyze the relationship between economic growth and structural change.
The research team was to examine the commodity composition as. Statistical Analysis and Forecasting of Economic Structural Change Gordon J. Anderson, Grayham E.
Mizon (auth.), Professor Dr. Peter Hackl (eds.) Inthe University of Bonn (FRG) and the International Institute for Applied System Analysis (IIASA) in Laxenburg (Austria), created a joint research group to analyze the relationship between. Statistical analysis and forecasting of economic structural change.
Berlin ; New York: Springer-Verlag, © (OCoLC) Material Type: Conference publication, Internet resource: Document Type: Book, Internet Resource: All Authors / Contributors: Peter Hackl; International Institute for Applied Systems Analysis. A Survey.- 15 Nonparametric Estimation of Time-Varying Parameters.- 16 Latent Variables in Regression Analysis.- 17 Structural Change and Time Series Analysis.- 18 Thresholds, Stability, Nonlinear Forecasting and Irregularly Sampled Data.- 19 Forecasting in Situations of Structural Change: A General Approach.- 20 Updating Parameters of Linear.
Vlll Statistical Analysis and Forecasting ofEconomic Structural Change arity by applying local stationary autoregressive processes. Finally, in Chap presents an empirical study, "Investment, tax ation, and econometric policy evaluation: Some evidence of the Lucas critique", which.
Structural change is a fundamental concept in economic model building. Statistics and econometrics provide the tools for identification of change, for estimating the onset of a change, for assessing its extent and relevance.
Statistics and econometrics also have de veloped models that are suitable. Inthe International Institute for Applied Systems Analysis (IIASA) in Laxen burg/ Austria initiated an ambitious project on "Economic Growth and Structural Change".
Keywords Statistik Statistische Statistical analysis and forecasting of economic structural change book Strukturwandel Wachstum calculus econometrics equilibrium forecasting inequality integration modeling poverty regression statistical.
A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time.
Thus it is a sequence of discrete-time data. Time Series analysis can be useful to see how a given asset, security or economic variable changes over time. of economic forecasting include selecting the fore-castingmodel(s)appropriatefortheproblemathand, assessing and communicating the uncertainty asso-ciated with a forecast, and guarding against model instability.
Time Series Models for Economic Forecasting Broadly speaking, statistical approaches to economic. In econometrics and statistics, a structural break is an unexpected change over time in the parameters of regression models, which can lead to huge forecasting errors and unreliability of the model in general.
This issue was popularised by David Hendry, who argued that lack of stability of coefficients frequently caused forecast failure, and therefore we must routinely test for structural. Read "Statistical analysis and forecasting of economic structural change.
Hack (ed.) Springer‐Verlag, Berlin, Heidelberg, New York, London, Paris, Tokyo, Hong Kong,pp. xix + ISBN 0‐‐‐6; hardbound, Journal of Applied Econometrics" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
In statistical terms, a structural break occurs when a time series’ underlying probability distribution changes. Change point detection (or analysis) process which aims to identify when these changes or shifts happened, often by using an algorithm to compare statistical properties of the original and new distributions.
Using Input-Output Analysis to Measure U.S. Economic Structural Change Over a 24 Year Period Jiemin Guo and Mark A. Planting WP AugustPaper presented at: The 13th International Conference on Input-Output Techniques, Macerata, Italy AugustThe views expressed in this paper are solely those of the author and.
Economic forecasting is made difficult by economic complexity, which implies non-linearities (multiple interactions and discontinuities) and unknown structural changes (the continuous change in the distribution of economic variables).
The forecast methodology aims at addressing these challenges. The algorithm is said to be “adaptive. Top Four Types of Forecasting Methods. There are four main types of forecasting methods that financial analysts Financial Analyst Job Description The financial analyst job description below gives a typical example of all the skills, education, and experience required to be hired for an analyst job at a bank, institution, or corporation.
Perform financial forecasting, reporting, and operational. Forecast combinations have received little attention in the oil price forecasting literature to date.
We demonstrate that over the last 20 years suitably constructed real-time forecast combinations would have been systematically more accurate than the no-change forecast. Statistics: Time series analysis, sequential analysis, change-point analysis, reinforcement learning Development economics: Endogenous structural transformation Books: 1.
Lai, T. L., and Xing, H. () Structural change as an alternative to long memory in financial time series. Advances in Econometrics (H. Carter and T. Fomby, eds. This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice.
David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and.
From Economic and Business Forecasting. Full book available for the premium on effective economic analysis, especially the identification of fying the possibility of a structural change in an economic time series to test if the past history of a variable would be different over time.
a modern time series assessment of “a statistical model for sunspot activity” by c. granger () Personal Comments on Yoon's Discussion of My Paper A New Class of Tail-dependent Time-Series Models and Its Applications in Financial Time Series.
Statistical Analysis and Forecasting of Economic. Peyton Cook received his Ph.D. in Statistics from Oklahoma State University in He has published papers on the theory of Bayesian Statistical Inference as well as papers applying statistics to business and biology. His current interest is Henstock-Kurzweil integration.
In Hackl, P. (ed.), Statistical Analysis and Forecasting of Economic Structural Change, () Density Estimation for Statistics and Data Analysis. Chapman & Hall. Speckman, P. () Kernel smoothing in partial linear models. Journal of Royal Statistical Society Series B 50, In book: A Companion to Economic Forecasting (pp.1 - 18) a key role in the statistical treatment of structural time series models.
Economic Forecasting provides an analysis of the current. Downloadable. The literature on structural breaks focuses on ex post identification of break points that may have occurred in the past.
While this question is important, a more challenging problem facing econometricians is to provide forecasts when the data generating process is unstable. The purpose of this paper is to provide a general methodology for forecasting in the presence of model.
Time‐series analysis, forecasting and econometric modelling: The structural econometric modelling, time‐series analysis (SEMTSA) approach. Arnold Zellner.
he held appointments in economics at the University of Washington in Seattle and the University of Wisconsin in Madison. His current and past research has been concentrated on. of economics in which economic theory and statistical method are fused in the analysis of numerical and institutional data” [Hood and Koopmans (, p.
xv)]. Today econo-mists refer to models that combine explicit economic theories with statistical models as structural econometric models. Structural Change and Economic Dynamics publishes articles about theoretical and applied, historical and methodological aspects of structural change in economic systems.
The journal publishes work analyzing dynamics and structural change in economic, technological, institutional. ETC Applied Forecasting for Business and Economics. Reliable forecasts of business and economic variables must often be obtained against a backdrop of structural change in markets and the economy.
This unit introduces methods suitable for forecasting in these circumstances including the decomposition of time series, exponential smoothing. "Forecasting Inflation with Gradual Regime Shifts and Exogenous Information," Working Paper Series European Central Bank. Hendry, David F., and Kirstin Hubrich ().
"Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, vol. 29, no. 2, pp. Economic Forecasting • Macroeconomic forecasting models are asked to perform well in a complex environment with numerous interrelated actions and decisions occurring simultaneously by heterogeneous agents with competing and conflicting agendas.
• Modern economies are evolving; there is both gradual and sudden structural change and shifts due to. In economics, a structural break might occur when there is a war, or a major change in government policy, or some equally sudden event.
But that is not very often, and we know about it when it happens. We don’t need to test if there was a structural break in – there was, because it was the end of World War 2. A n econometric model is one of the tools economists use to forecast future developments in the economy.
In the simplest terms, econometricians measure past relationships among such variables as consumer spending, household income, tax rates, interest rates, employment, and the like, and then try to forecast how changes in some variables will affect the future course of others.
However, the performance of the Naive 1 model declines when it has to deal with sudden structural change and longer-term forecasting (Chan et al.,Witt et al., ). The Naive 2 (or constant change) model is another widely used but simple model employed when there is a constant trend present in the data.
The general-to-specific approach to econometric modeling and forecasting has a clear strategy in model specification, estimation, and selection and is demonstrated in the context of international tourism demand.
The Board staff employs a variety of formal models, both structural and purely statistical, in its forecasting efforts. However, the forecasts of inflation (and of other key macroeconomic variables) that are provided to the Federal Open Market Committee are developed through an eclectic process that combines model-based projections, anecdotal.
This paper provides a how-to guide to model-based forecasting and monetary policy analysis. It describes a simple structural model, along the lines of those in use in a number of central banks.
This workhorse model consists of an aggregate demand (or IS) curve, a price-setting. Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts.
The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged.
2 theory plays a larger role than facilitating forecast-ing—it also helps explain the underlying structural relations in the economy. The application of theory, mathematical reason-ing, and statistical technique to establish the actual value of these structural relations is called econometrics.
Forecasting is.The aim of this paper is to assess whether explicitly modelling structural change increases the accuracy of macroeconomic forecasts. In principle, allowing for structural changes may improve the forecast performance for at least two reasons.
First, it allows capturing and exploiting changes in. This paper considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model, previously estimated by Dees, di Mauro, Pesaran, and Smith () and Dees, Holly, Pesaran, and Smith () over the period Q1–Q4, is used to generate out-of-sample forecasts one .