Structural estimation by K-class methods and reduced form forecasting

  • 207 Pages
  • 4.82 MB
  • 915 Downloads
  • English
by
Center for Naval Analyses , Arlington, Va
Economic forecasting -- Mathematical models., Equations, Simultan
Statement[by] George F. Brown, Jr.
Series[Center for Naval Analyses] Professional paper, no. 98, Professional paper (Center for Naval Analyses) ;, 98.
Classifications
LC ClassificationsAS36.C333 A26 no. 98, HB3730 A26 no. 98
The Physical Object
Paginationvii, 207, 6 p.
ID Numbers
Open LibraryOL5080853M
LC Control Number74151841

BibTeX @MISC{Brown03structuralestimation, author = {George F. Brown and Presented George and F. Brown and F. Brown}, title = {STRUCTURAL ESTIMATION BY K-CLASS METHODS AND REDUCED FORM FORECASTING}, year = {}}.

for something to be reduced form according to this definition you need to write down a structural model this actually has content-you can sometimes use reduced form models to simulate a policy that has never been implemented (as often reduced form parameters are structural in the sense that they are policy invariant)File Size: KB.

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 Cited by: An estimation strategy is then developed based on both the reduced form and the structural model approach.

Description Structural estimation by K-class methods and reduced form forecasting PDF

Using data for Hawaii longline fishery we find that less patient captains seem to have. We next consider another limited-information estimation method. Two-stage least squares. The 2SLS estimator uses the unrestricted reduced-form estimate P, the equation-by-equation OLS estimates of the π’s, which accounts for its popularity.

The mechanics of the 2SLS method can be described simply. A consequence of the prevalence of underidentified structures is that the least squares reduced from equations are likely to be the best forecasting equations.

Institutional Access Log. The forecasting techniques are applied to monthly inflation series of 21 OECD countries and it is found that average forecasting methods in general perform better than using forecasts based on a. In the first step of MAPA, as shown in Fig. 1, K time series are produced through time series temporal aggregation.

Forecasting. After constructing the aggregate Y [k] series, we fit appropriate forecasting models to each one. It is desirable to consider the different time series components that appear at each aggregation level, instead of the forecasts, so as to distinguish the Cited by: Reduced forms can (in principle) be used to identify structural parameters, in which cased you are still performing structural estimation, just through using the reduced form.

Another way to look at this is that structural models are generally deductive, whereas reduced forms tend to be used as part of some greater inductive reasoning. this book. (The full set from the book can also be downloaded under Course Documents, if desired.) Hyndman, R.J. and Athanasopoulos, G.

Forecasting: principles and practice. has recently superseded the latter book. Hence, some of the File Size: 1MB. There are two alternative approaches to the business of estimating the structural model and of extracting its components.

The first approach, which is described as the canonical approach, is to estimate the parameters of the reduced-form ARIMA model. From these parameters, the Wiener–. Economics Non-Parametric and ML Approaches to Structural Estimation Problems Course Description This course explores themes at the intersection of machine learning and structural estimation problems in econometrics.

Structural estimation problems arise when taking economic structures to data. the outset. After a brief subsection on estimation via the reduced form ARIMA model, the relationship between the various methods is discussed and a parallel is drawn with ML procedures for ARIMA models, i.e.

exact ML, conditional sum of squares (CSS) and so on.

Download Structural estimation by K-class methods and reduced form forecasting FB2

The last time domain ML estimation procedure described is the EM algorithm. This provides. Structural estimation.

Structural estimation is a technique for estimating deep "structural" parameters of theoretical economic models.

Details Structural estimation by K-class methods and reduced form forecasting PDF

The term is inherited from the simultaneous equations model. In this sense "structural estimation" is contrasted with "reduced-form estimation", which is the statistical relationship between observed variables.

Econometric Modelling with Time Series This book provides a general framework for specifying, estimating and testing time series econometric models.

Special emphasis is given to estimation by maxi-mum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation File Size: KB. In this paper we develop efficient Bayesian estimation methods for time-varying parame-ter dynamic factor models.

That is, we estimate unobserved factors coming from a dynamic factor model under a general and flexible form of structural breaks in the coefficients and volatilities. estimation methods. The Generalized Method of Moments approach is introduced in section 4.

The second part of the chapter focuses on econometric models and applications of these three estimation methods. Section 5 is devoted to time series models.

We study both univariate and multivariate models. Simultaneous equation. Classification of Forecast Methods 2 Conceptual Framework of a Forecast System 3 Choice of a Particular Forecast Model 4 Forecast Criteria 5 Outline of the Book 5 2 THE REGRESSION MODEL AND ITS APPLICATION IN FORECASTING 8 The Regression Model 9 Linear and Nonlinear Models, Structural Econometric Modeling and Time Series Analysis F.

Palm Economische Faculteit Vrije Universiteit Postbus MC Amsterdam ABSTRACT We discuss the Structural Econometric Modeling and Time Series Analysis (SEMTSA) approach put forward by Zellner and Palm, which provides a synthesis of econometric and time series methods in modeling economic time by: 8.

Forecasting: methods and applications Wiley/Hamilton series in management and administration Management Series Wiley Series on Personality Processes Volume 12 of (TIMS studies in the management sciences) Authors: Spyros G. Makridakis, Steven C.

Wheelwright: Contributor: Steven C. Wheelwright: Edition: 2, illustrated: Publisher: Wiley, /5(2). method leads to better out-of-sample forecasts than a range of alternative methods. Keywords: Structural Breaks, Forecasting, Hierarchical hidden Markov Chain, Bayesian Model Averag-ing. JEL Classifications: C, C, C ∗We thank two anonymous referees, the editor, Bernard Salanie, as well as Frank Diebold, Graham Elliott, John.

Combining Methods of Non-structural Estimation Combining Structural and Non-structural Methods Case Study Purchases of Consumer Durables The Role of Judgment in Forecasting Surveys of Sentiment and Buying Plans Sentiment Index for Prospective Home Buyers The Role of Consensus Forecasts This comprehensive and authoritative resource provides full, unabridged text of the complete Internal Revenue Code in two volumes.

CCH offers this tax information in a timely and reliable manner that business and tax professionals have come to expect and appreciate. This Winter Edition of Internal Revenue Code reflects all new statuatory tax changes through Januaryincluding the 5/5(1).

METHODS 4/1 The forecasting scenario 4/2 Averaging methods 4/2/1 The mean 42/2 Moving averages 4/3 Exponential smoothing methods 4 Single exponential smoothing 4/3/2 Single exponential smoothing: an adaptive approach 4/3/3 Hofs linear method 4/3/4 Holt-Winters' trend and seasonality method   This approach achieves a better estimation of the different time series components, through temporal aggregation, and reduces the importance of model selection through forecast combination.

An empirical evaluation of the proposed framework demonstrates significant improvements in forecasting accuracy, especially for long-term forecasts. 1 contents foreword 10 introduction 11 the example: a very basic model 14 15 chapter 1: notations and definitions 15 the model as a set of equations 15 the elements in a model 15 variables: endogenous and exogenous 15 equations: behavioral and identities 17 parameters 20 the random term 21 residuals versus errors Since accurate forecasting requires more than just inserting historical data into a model, Forecasting: Methods and Applications, 3/e, adopts a managerial, business orientation.

Integrated throughout this text is the innovative idea that explaining the past is not adequate for predicting the by: Forecasting and predicting To many authors, forecasting and prediction are equivalent. Some authors distinguish the terms: prediction is the technical word, forecasting relates predictions to the substance-matter environment.

Clementsand Hendrydefine: predictability is a theoretical property—unconditional and conditional distributions File Size: KB. of such methods to the estimation of stochastic volatility models, see Kim (). The forecasting procedure is then relatively simple and can be carried out in a straightforward fashion once the model has been estimated.

Simulations show that the estimation method provides very reliable results in. This review provides an overview of forecasting methods that can help researchers forecast in the presence of nonstationarities caused by instabilities.

The emphasis of the review is both theoretical and applied, and we provide several examples of interest to economists. We show that modeling instabilities can help, but it depends on how they are modeled.

We also demonstrate how to robustify a Cited by: 7. Handbook of Financial Analysis, Forecasting, and Modeling book. Read reviews from world’s largest community for readers. This comprehensive handbook give /5(9).Forecasting and model averaging with structural breaks Anwen Yin Iowa State University Forecasting and model averaging with structural breaks by Anwen Yin metric models, estimation method, out-of-sample forecast procedure and forecast com-bination methods.

Sectionpresents the data and our empirical by: 1.In other words, accurate forecasting requires more than just the fitting of models to historical data.

Inside, readers will Known from its last editions as the "Bible of Forecasting", the third edition of this authoritative text has adopted a new approach-one that is as new as the latest trends in the field: "Explaining the past is not adequate 4/5.