Econometric Analysis of Model Selection and Model Testing PDF Download

Author: M. Ishaq Bhatti
Publisher: Routledge
ISBN: 135194195X
Category : Business & Economics
Languages : en
Pages : 286

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Book Description
In recent years econometricians have examined the problems of diagnostic testing, specification testing, semiparametric estimation and model selection. In addition researchers have considered whether to use model testing and model selection procedures to decide the models that best fit a particular dataset. This book explores both issues with application to various regression models, including the arbitrage pricing theory models. It is ideal as a reference for statistical sciences postgraduate students, academic researchers and policy makers in understanding the current status of model building and testing techniques.

Author: Antonio Aznar Grasa
Publisher: Springer Science & Business Media
ISBN: 9401713588
Category : Business & Economics
Languages : en
Pages : 265

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Book Description
This book proposes a new methodology for the selection of one (model) from among a set of alternative econometric models. Let us recall that a model is an abstract representation of reality which brings out what is relevant to a particular economic issue. An econometric model is also an analytical characterization of the joint probability distribution of some random variables of interest, which yields some information on how the actual economy works. This information will be useful only if it is accurate and precise; that is, the information must be far from ambiguous and close to what we observe in the real world Thus, model selection should be performed on the basis of statistics which summarize the degree of accuracy and precision of each model. A model is accurate if it predicts right; it is precise if it produces tight confidence intervals. A first general approach to model selection includes those procedures based on both characteristics, precision and accuracy. A particularly interesting example of this approach is that of Hildebrand, Laing and Rosenthal (1980). See also Hendry and Richard (1982). A second general approach includes those procedures that use only one of the two dimensions to discriminate among models. In general, most of the tests we are going to examine correspond to this category.

Author: Christian Gourieroux
Publisher: Cambridge University Press
ISBN: 9780521471626
Category : Business & Economics
Languages : en
Pages : 544

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Book Description
This two-volume work aims to present as completely as possible the methods of statistical inference with special reference to their economic applications. The reader will find a description not only of the classical concepts and results of mathematical statistics, but also of concepts and methods recently developed for the specific needs of econometrics. The authors have sought to avoid an overly technical presentation and go to some lengths to encourage an intuitive understanding of the results by providing numerous examples throughout. The breadth of approaches and the extensive coverage of the two volumes provide for a thorough and entirely self-contained course in modern econometrics. Volume 1 provides an introduction to general concepts and methods in statistics and econometrics, and goes on to cover estimation and prediction. Volume 2 focuses on testing, confidence regions, model selection, and asymptotic theory.

Author: Jan Kmenta
Publisher: Academic Press
ISBN: 1483267342
Category : Business & Economics
Languages : en
Pages : 425

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Book Description
Evaluation of Econometric Models presents approaches to assessing and enhancing the progress of applied economic research. This book discusses the problems and issues in evaluating econometric models, use of exploratory methods in economic analysis, and model construction and evaluation when theoretical knowledge is scarce. The data analysis by partial least squares, prediction analysis of economic models, and aggregation and disaggregation of nonlinear equations are also elaborated. This text likewise covers the comparison of econometric models by optimal control techniques, role of time series analysis in econometric model evaluation, and hypothesis testing in spectral regression. Other topics include the relevance of laboratory experiments to testing resource allocation theory and token economy and animal models for the experimental analysis of economic behavior. This publication is intended for students and researchers interested in evaluating econometric models.

Author: Andrew M. Jones
Publisher: John Wiley & Sons
ISBN: 9780470841457
Category : Medical
Languages : en
Pages : 252

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Book Description
Given extensive use of individual level data in Health Economics, it has become increasingly important to understand the microeconometric techniques available to applied researchers. The purpose of this book is to give readers convenient access to a collection of recent contributions that contain innovative applications of microeconometric methods to data on health and health care. Contributions are selected from papers presented at the European Workshops on Econometrics and Health Economics and published in Health Economics. Topics covered include: * Latent Variables * Unobservable heterogeneity and selection problems * Count data and survival analysis * Flexible and semiparametric estimators for limited dependent variables * Classical and simulation methods for panel data * Publication marks the tenth anniversary of the Workshop series. Doctoral students and researchers in health economics and microeconomics will find this book invaluable. Researchers in related fields such as labour economics and biostatistics will also find the content of use.

Author:
Publisher: Springer
ISBN: 1349588024
Category : Law
Languages : en
Pages : 7493

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Book Description
The award-winning The New Palgrave Dictionary of Economics, 2nd edition is now available as a dynamic online resource. Consisting of over 1,900 articles written by leading figures in the field including Nobel prize winners, this is the definitive scholarly reference work for a new generation of economists. Regularly updated! This product is a subscription based product.

Author: David F. Hendry
Publisher: Princeton University Press
ISBN: 1400845653
Category : Business & Economics
Languages : en
Pages : 378

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Book Description
Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointegrated systems. In each setting, a statistical model is constructed to explain the observed variation in the data, with estimation and inference based on the likelihood function. Substantive issues are always addressed, showing how both statistical and economic assumptions can be tested and empirical results interpreted. Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied. Econometric Modeling is a self-contained introduction for advanced undergraduate or graduate students. Throughout, data illustrate and motivate the approach, and are available for computer-based teaching. Technical issues from probability theory and statistical theory are introduced only as needed. Nevertheless, the approach is rigorous, emphasizing the coherent formulation, estimation, and evaluation of econometric models relevant for empirical research.

Author: A. Smith
Publisher:
ISBN: 9781979581332
Category :
Languages : en
Pages : 250

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Book Description
Econometrics Toolbox provides functions for modeling economic data. You can select and estimate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate Bayesian linear regression, univariate ARIMAX/GARCH composite models with several GARCH variants, multivariate VARX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can use a variety of diagnostics for model selection, including hypothesis tests, unit root,stationarity, and structural change.A probabilistic time series model is necessary for a wide variety of analysis goals ,including regression inference, forecasting, and Monte Carlo simulation. When selecting a model, aim to find the most parsimonious model that adequately describes your data. Asimple model is easier to estimate, forecast, and interpret*Specification tests help you identify one or more model families that could plausiblydescribe the data generating process.*Model comparisons help you compare the fit of competing models, with penalties for complexity.*Goodness-of-fit checks help you assess the in-sample adequacy of your model, verify that all model assumptions hold, and evaluate out-of-sample forecast performance.Model selection is an iterative process. When goodness-of-fit checks suggest model assumptions are not satisfied-or the predictive performance of the model is not satisfactory-consider making model adjustments. Additional specification tests, model comparisons, and goodness-of-fit checks help guide this process..The most important content is the following:* Econometrics Toolbox Product Description* Econometric Modeling* Econometrics Toolbox Model Objects, Properties, and Methods* Stochastic Process Characteristics* Data Transformations* Data Preprocessing* Trend-Stationary vs. Difference-Stationary Processes* Nonstationary Processes* Trend Stationary* Difference Stationary* Specify Lag Operator Polynomials* Lag Operator Polynomial of Coefficients* Difference Lag Operator Polynomials* Nonseasonal Differencing* Nonseasonal and Seasonal Differencing* Time Series Decomposition* Moving Average Filter* Moving Average Trend Estimation* Parametric Trend Estimation* Hodrick-Prescott Filter* Using the Hodrick-Prescott Filter to Reproduce Their* Original Result* Seasonal Filters* Seasonal Adjusment* Seasonal Adjustment Using a Stable Seasonal Filter* Seasonal Adjustment Using S(n,m) Seasonal Filters* Box-Jenkins Methodology* Box-Jenkins Model Selection* Autocorrelation and Partial Autocorrelation* Theoretical ACF and PACF* Sample ACF and PACF* Ljung-Box Q-Test* Detect Autocorrelation* Engle's ARCH Test* Detect ARCH Effects* Unit Root Nonstationarity* Unit Root Tests* Assess Stationarity of a Time Series* Information Criteria* Model Comparison Tests* Likelihood Ratio Test* Lagrange Multiplier Test* Wald Test* Covariance Matrix Estimation* Conduct a Lagrange Multiplier Test* Conduct a Wald Test* Compare GARCH Models Using Likelihood Ratio Test* Check Fit of Multiplicative ARIMA Model* Goodness of Fit* Residual Diagnostics* Check Residuals for Normality* Check Residuals for Autocorrelation* Check Residuals for Conditional Heteroscedasticity* Check Predictive Performance* Nonspherical Models* Plot a Confidence Band Using HAC Estimates* Change the Bandwidth of a HAC Estimator* Check Model Assumptions for Chow Test* Power of the Chow Test

Author: Francesco Esposito
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
In this thesis, we investigate model selection in a general setting and perform several exercises in financial econometrics. We present the multi-hypothesis testing (MHT) framework, with which we design different type of model comparisons. We distinguish between test of model performance significance, of relative and absolute model performance and apply our framework to market risk forecasting model, to latent factor jump-diffusion models employed for the estimation of the statistical measure of an equity index, as well as to equity option pricing models. We develop original tests and, with regard to the proper exercise of model selection from an initial battery of models without any reference to a benchmark model, we combine the MHT approach with the model confidence set (MCS) to deliver a novel test of model comparison that is performed along with the established version of the MCS, as well as with an alternative simplified new MCS test that are detailed in the course of this work. We collect empirical evidence concerning model comparison in several subjects. With respect to market risk forecasting models, we have found that models capturing volatility clustering or targeting directly an auto-correlated conditional distribution percentile, perform better than the target model set and in particular, better than the historical simulation, widely employed by practitioners, and better than the so called RiskMetrics model. With respect to the equity index data dynamics, we have found that the popular affine jump-diffusion model requires a CEV augmentation to perform appropriately and that those models are slightly overperformed by an alternative stochastic volatility model, characterised by stochastic hazard with high frequency small jumps. The test performed over a large model set employed in the option pricing exercise points to a wide similarity of the results obtained by the many model specifications of the superior exponential volatility model, therefore suggesting a more careful adjustment of the model complexity. The model selection framework has proven very flexible in dealing with the varied collection of statistical problems. In particular, our main contribution represented by the generalised MHT based MCS test provides a method for model selection that is robust to finite sample distribution and that has the advantage of an adjustable tolerance for false rejections, allowing conservative to aggressive testing profiles.

Author: David F. Hendry
Publisher: MIT Press
ISBN: 0262324423
Category : Business & Economics
Languages : en
Pages : 387

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Book Description
A synthesis of the authors' groundbreaking econometric research on automatic model selection, which uses powerful computational algorithms and theory evaluation. Economic models of empirical phenomena are developed for a variety of reasons, the most obvious of which is the numerical characterization of available evidence, in a suitably parsimonious form. Another is to test a theory, or evaluate it against the evidence; still another is to forecast future outcomes. Building such models involves a multitude of decisions, and the large number of features that need to be taken into account can overwhelm the researcher. Automatic model selection, which draws on recent advances in computation and search algorithms, can create, and then empirically investigate, a vastly wider range of possibilities than even the greatest expert. In this book, leading econometricians David Hendry and Jurgen Doornik report on their several decades of innovative research on automatic model selection. After introducing the principles of empirical model discovery and the role of model selection, Hendry and Doornik outline the stages of developing a viable model of a complicated evolving process. They discuss the discovery stages in detail, considering both the theory of model selection and the performance of several algorithms. They describe extensions to tackling outliers and multiple breaks, leading to the general case of more candidate variables than observations. Finally, they briefly consider selecting models specifically for forecasting.