Download or read book Likelihood Based Inference in Nonlinear Error Correction Models written by Anders Rahbek and published by . This book was released on 2008 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a class of vector nonlinear error correction models where the transfer function (or loadings) of the stationary relationships is nonlinear. This includes in particular the smooth transition models. A general representation theorem is given which establishes the dynamic properties of the process in terms of stochastic and deterministic trends as well as stationary components. In particular, the behaviour of the cointegrating relations is described in terms of geometric ergodicity. Despite the fact that no deterministic terms are included, the process will have both stochastic trends and a linear trend in general. Gaussian likelihood-based estimators are considered for the long-run cointegration parameters, and the short-run parameters. Asymptotic theory is provided for these and it is discussed to what extend asymptotic normality and mixed normaity can be found. A simulation study reveals that cointegration vectors and the shape of the adjustment are quite accurately estimated by maximum likelihood, while at the same time there is very little information about some of the individual parameters entering the adjustment function.
Download or read book Likelihood based Inference in Cointegrated Vector Autoregressive Models written by Søren Johansen and published by Oxford University Press, USA. This book was released on 1995 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is concerned with the statistical analysis of multivariate systems of non-stationary time series of type I. It applies the concepts of cointegration and common trends in the framework of the Gaussian vector autoregressive model.
Download or read book Likelihood Based Inference for an Identifiable Fractional Vector Error Correction Model written by Federico Carlini and published by . This book was released on 2018 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the Fractional Vector Error Correction model proposed in Avarucci (2007), which is characterized by a richer lag structure than the models proposed in Granger (1986) and Johansen (2008, 2009). In particular, we discuss the properties of the model of Avarucci (2007) (FECM) in comparison to the model of Johansen (2008, 2009) (FCVAR). Both models generate the same class of processes, but the properties of the two models are different. First, opposed to the model of Johansen (2008, 2009), the model of Avarucci has a convenient nesting structure, which allows for testing the number of lags and the cointegration rank exactly in the same way as in the standard I(1) cointegration framework of Johansen (1995) and hence might be attractive for econometric practice. Second, we find that the model of Avarucci (2007) is almost free from identification problems, contrary to the model of Johansen (2008, 2009) and Johansen and Nielsen (2012), which identification problems are discussed in Carlini and Santucci de Magistris (2017). However, due to a larger number of parameters, the estimation of the FECM model of Avarucci (2007) turns out to be more complicated. Therefore, we propose a 4-step estimation procedure for this model that is based on the switching algorithm employed in Carlini and Mosconi (2014), together with the GLS procedure of Mosconi and Paruolo (2014). We check the performance of the proposed estimation procedure in finite samples by means of a Monte Carlo experiment and we prove the asymptotic distribution of the estimators of all the parameters. The solution of the model has been previously derived in Avarucci (2007), while testing for the rank has been discussed in Lasak and Velasco (for cointegration strength >0.5) and Avarucci and Velasco (for cointegration strength
Download or read book Likelihood Based Inference for Nonlinear Models with Both Individual and Time Effects written by Yutao Sun and published by . This book was released on 2016 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a bias correction method for nonlinear models with both individual and time effects. Under the presence of the incidental parameter problem, the maximum likelihood estimator derived from such models may be severely biased. Our method produces an approximation to an infeasible log-likelihood function that is not exposed to the incidental parameter problem. The maximizer derived from the approximating function serves as a bias-corrected estimator that is asymptotically unbiased when the sequence N=T converges to a constant. The proposed method is general in several perspectives. The method can be extended to models with multiple fixed effects and can be easily modified to accommodate dynamic models.
Download or read book Likelihood Based Inference in Nonlinear Regression Model Using the P and R Approach written by Anne-Sophie Tocquet and published by . This book was released on 1997 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonlinear Econometric Modeling in Time Series written by William A. Barnett and published by Cambridge University Press. This book was released on 2000-05-22 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.
Download or read book Measurement Error in Nonlinear Models written by Raymond J. Carroll and published by CRC Press. This book was released on 1995-07-06 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides an up-to-date discussion of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function problems. The text concentrates on the general ideas and strategies of estimation and inference rather than being concerned with a specific problem. Measurement error occurs in many fields, such as biometry, epidemiology and economics. In particular, the book contains a large number of epidemiological examples. An outline of strategies for handling progressively more difficult problems is also provided.
Download or read book Handbook of Financial Time Series written by Torben Gustav Andersen and published by Springer Science & Business Media. This book was released on 2009-04-21 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Financial Time Series gives an up-to-date overview of the field and covers all relevant topics both from a statistical and an econometrical point of view. There are many fine contributions, and a preamble by Nobel Prize winner Robert F. Engle.
Download or read book Testing and Inference in Nonlinear Cointegrating Vector Error Correction Models written by Dennis Kristensen and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Measurement Error in Nonlinear Models written by Raymond J. Carroll and published by CRC Press. This book was released on 2006-06-21 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and ex
Download or read book Likelihood Based Inference in Cointegrated Vector Autoregressive Models written by Soren Johansen and published by . This book was released on with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book In All Likelihood written by Yudi Pawitan and published by OUP Oxford. This book was released on 2013-01-17 with total page 543 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on a course in the theory of statistics this text concentrates on what can be achieved using the likelihood/Fisherian method of taking account of uncertainty when studying a statistical problem. It takes the concept ot the likelihood as providing the best methods for unifying the demands of statistical modelling and the theory of inference. Every likelihood concept is illustrated by realistic examples, which are not compromised by computational problems. Examples range from a simile comparison of two accident rates, to complex studies that require generalised linear or semiparametric modelling. The emphasis is that the likelihood is not simply a device to produce an estimate, but an important tool for modelling. The book generally takes an informal approach, where most important results are established using heuristic arguments and motivated with realistic examples. With the currently available computing power, examples are not contrived to allow a closed analytical solution, and the book can concentrate on the statistical aspects of the data modelling. In addition to classical likelihood theory, the book covers many modern topics such as generalized linear models and mixed models, non parametric smoothing, robustness, the EM algorithm and empirical likelihood.
Download or read book Identification and Inference of Nonlinear Models Using Two Samples with Aribrary Measurement Errors written by Xiaohong Chen and published by . This book was released on 2006 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers identification and inference of a general latent nonlinear model using two samples, where a covariate contains arbitrary measurement errors in both samples, and neither sample contains an accurate measurement of the corresponding true variable. The primary sample consists of some dependent variables, some error-free covariates and an error-ridden covariate, where the measurement error has unknown distribution and could be arbitrarily correlated with the latent true values. The auxiliary sample consists of another noisy measurement of the mismeasured covariate and some error-free covariates. We first show that a general latent nonlinear model is nonparametrically identified using the two samples when both could have nonclassical errors, with no requirement of instrumental variables nor independence between the two samples. When the two samples are independent and the latent nonlinear model is parameterized, we propose sieve quasi maximum likelihood estimation (MLE) for the parameter of interest, and establish its root-n consistency and asymptotic normality under possible misspecification, and its semiparametric efficiency under correct specification. We also provide a sieve likelihood ratio model selection test to compare two possibly misspecified parametric latent models. A small Monte Carlo simulation and an empirical example are presented.
Download or read book Issues in General Economic Research and Application 2011 Edition written by and published by ScholarlyEditions. This book was released on 2012-01-09 with total page 875 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues in General Economic Research and Application: 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about General Economic Research and Application. The editors have built Issues in General Economic Research and Application: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about General Economic Research and Application in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in General Economic Research and Application: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.
Download or read book Statistical Inference in Nonlinear Models written by Geraldo da Silva e Souza and published by . This book was released on 1979 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimation and hypothesis testing are considered for a system of simultaneous, monlinear, implicit equations. These problems are studied in a general setting. A given objective function, the pseudo likelihood, defines an estimator. Conditions are set forth such that this estimator is consistent and asymptotically normaly distributed. The Wald's test and analogs of the lagrange multiplier test and the likelihood ratio test are derived from this estimator and their null and non-null distributions are given. To illustrate the theory, results are applied in three instances: maximum likelihood estimation in simultaneous nonlinear systems, single equation nonlinear explicit models, and seemingly unrelated nonlinear regression models.
Download or read book Empirical Likelihood based Inference in Linear Errors in Covariables Models with Validation Data written by Qihua Wang and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Non likelihood Based Methods for the Estimation and Inference in Econometric Models written by Hyeonseok Park and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation aims to address estimation and inference in econometric models when the likelihood-based estimations may not be applicable. Chapter 1 proposes simple, robust estimation and inference methods for the transition matrix of a high-dimensional semiparametric Gaussian copula vector autoregressive (VAR) process with unknown, possibly fat-tailed marginal distributions. In this model, the observable variable is a monotonic transformation of the latent variable, and the latent variable follows the Gaussian VAR process. Since the marginal distribution is unknown, conventional approaches that use the sample variance and auto-covariances such as OLS are not applicable. This chapter circumvents the problem by constructing the rank estimators of the variance and auto-covariance matrices of the latent process. This chapter derives rates of convergence of the estimator based on which we develop de-biased inference for Granger causality. Chapter 2 develops a simple, robust method for the estimation and inference in structural models using sliced distances between empirical and model-induced quantile functions (distribution functions). In state-space models, observable variables could be driven by fewer latent variables. This causes stochastic singularity, and the likelihood function does not exist. For the models with parameter-dependent support such as in the one-sided and two-sided models, the likelihood function may not be smooth depending on the parameter. Therefore, the asymptotic theory for MLE may not be robust to the parameter. We handle these issues using sliced distances since they are well-defined for stochastic singular models and models with parameter-dependent support. In contrast to MLE and likelihood-based inference, we show that under mild regularity conditions, our estimator is asymptotically normally distributed, leading to simple inference regardless of the possible presence of ”stochastic singularity” and parameter-dependent supports. Furthermore, our estimator applies to generative models with intractable likelihood functions but from which one can easily draw synthetic samples. We provide simulation results based on a stochastic singular state-space model, a term structure model, and an auction model.
