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Analysis Of Panel Vector Error Correction Models Using Maximum Likelihood The Bootstrap And Canonical Correlation Estimators
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Book Synopsis Analysis of Panel Vector Error Correction Models Using Maximum Likelihood, the Bootstrap, and Canonical-correlation Estimators by : Richard G. Anderson
Download or read book Analysis of Panel Vector Error Correction Models Using Maximum Likelihood, the Bootstrap, and Canonical-correlation Estimators written by Richard G. Anderson and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "In this paper, we examine the use of Box-Tiao's (1977) canonical correlation method as an alternative to likelihood-based inferences for vector error-correction models. It is now well-known that testing of cointegration ranks based on Johansen's (1995) ML-based method suffers from severe small sample size distortions. Furthermore, the distributions of empirical economic and financial time series tend to display fat tails, heteroskedasticity and skewness that are inconsistent with the usual distributional assumptions of likelihood-based approach. The testing statistic based on Box-Tiao's canonical correlations shows promise as an alternative to Johansen's ML-based approach for testing of cointegration rank in VECM models"--Federal Reserve Bank of St. Louis web site.
Book Synopsis The New Palgrave Dictionary of Economics by :
Download or read book The New Palgrave Dictionary of Economics written by and published by Springer. This book was released on 2016-05-18 with total page 7493 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Simulation Comparing Bootstrap and Maximum Likelihood Estimates of Scale-value Standard Errors in Multidimensional Scaling by : Se-Kang Kim
Download or read book A Simulation Comparing Bootstrap and Maximum Likelihood Estimates of Scale-value Standard Errors in Multidimensional Scaling written by Se-Kang Kim and published by . This book was released on 1999 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Effect of Model Selection Uncertainty on the Error Bands for Impulse Response Functions in Vector Error Correction Models by : Islam Azzam
Download or read book The Effect of Model Selection Uncertainty on the Error Bands for Impulse Response Functions in Vector Error Correction Models written by Islam Azzam and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conventional asymptotic and bootstrap methods for finite-order autoregressive models condition on the estimated lag-order of the model, which is later, used to construct the error bands for impulse response functions. Even if the estimated lag order is believed to be correct, this procedure ignores the sampling uncertainty of the lag order. An earlier study by Kilian (1998) introduced an endogenous lag order bootstrap algorithm that reflected the true extent of sampling uncertainty in the regression estimates. Applications of Kilian's method to vector autoregressive (VAR) and vector error correction (VEC) assumed that the true cointegration rank is known. This paper modifies the application of kilian's method on VEC models by endogenizing the cointegration rank besides the lag order. Monte Carlo simulations results from two U.S. economy models show that ignoring cointegration rank uncertainty may seriously undermine the coverage accuracy of bootstrap confidence intervals for VEC impulse response estimates. Endogenizing the cointegration rank choice is shown to improve coverage accuracy at low additional computational cost.
Book Synopsis Multivariate Panel Models with Individual Effects in the Error Structure by : Lawrence S. Mayer
Download or read book Multivariate Panel Models with Individual Effects in the Error Structure written by Lawrence S. Mayer and published by . This book was released on 1984 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Consistency of Least Squares Estimators in Error Correction Models by : James H. Stock
Download or read book The Consistency of Least Squares Estimators in Error Correction Models written by James H. Stock and published by . This book was released on 1984 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Likelihood Based Inference for an Identifiable Fractional Vector Error Correction Model by : Federico Carlini
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
Book Synopsis Estimation of Nonlinear Error Correction Models by :
Download or read book Estimation of Nonlinear Error Correction Models written by and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Determining the Accuracy of Maximum Likelihood Parameter Estimates with Colored Residuals by :
Download or read book Determining the Accuracy of Maximum Likelihood Parameter Estimates with Colored Residuals written by and published by . This book was released on 1994 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Determination of Vector Error Correction Models in High Dimensions by : Chong Liang
Download or read book Determination of Vector Error Correction Models in High Dimensions written by Chong Liang and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We provide a shrinkage type methodology which allows for simultaneous model selection and estimation of vector error correction models (VECM) when the dimension is large and can increase with sample size. Model determination is treated as a joint selection problem of cointegrating rank and autoregressive lags under respective practically valid sparsity assumptions. We show consistency of the selection mechanism by the resulting Lasso-VECM estimator under very general assumptions on dimension, rank and error terms. Moreover, with computational complexity of a linear programming problem only, the procedure remains computationally tractable in high dimensions. We demonstrate the effectiveness of the proposed approach by a simulation study and an empirical application to recent CDS data after the financial crisis.
Book Synopsis Vector Error Correction Models with Stationary and Nonstationary Variables by : Pu Chen
Download or read book Vector Error Correction Models with Stationary and Nonstationary Variables written by Pu Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vector error correction models (VECM) have become a standard tool in empirical economics for analysing nonstationary time series data because they combine two key concepts in economics: equilibrium and dynamic adjustment in one single model. The current standard VECM procedure is restricted to time series data with the same degree of integration, i.e. all I(1) variables. Time series data with different degrees of integration, on the other hand, are frequently encountered in empirical studies, necessitating the simultaneous handling of I(1) and I(0) time series. In this paper, the standard VECM is extended to accommodate mixed I(1) and I(0) variables. The mixed VECM conditions are derived, and a test and estimation of the mixed VECM are presented as a result.
Book Synopsis A Full Heteroscedastic One-way Error Components Model Allowing for Unbalanced Panel by : Bernard Lejeune
Download or read book A Full Heteroscedastic One-way Error Components Model Allowing for Unbalanced Panel written by Bernard Lejeune and published by . This book was released on 2004 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Vector Error Correction Index Model by : Gianluca Cubadda
Download or read book The Vector Error Correction Index Model written by Gianluca Cubadda and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis K-Step Bootstrap Bias Correction for Fixed Effects Estimators in Nonlinear Panel Models by : Yixiao Sun
Download or read book K-Step Bootstrap Bias Correction for Fixed Effects Estimators in Nonlinear Panel Models written by Yixiao Sun and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fixed effects estimators in nonlinear panel models with fixed T usually suffer from inconsistency because of the incidental parameters problem first noted by Neyman and Scott (1948). Moreover, even though T grows at the same rate as n, they are asymptotically biased and therefore the associated confidence interval has a large coverage error. This paper proposes a k-step parametric bootstrap bias corrected estimator. We prove that our estimator is asymptotically normal and is centered at the true parameter if T grows faster than ∛n. In addition to bias correction, we construct a confidence interval with a double bootstrap procedure, and Monte Carlo experiments confirm that the error in coverage probability of our CI's is smaller than those of the alternatives. We also propose bias correction for average marginal effects.
Book Synopsis A Comparison of Estimators in Hierarchical Linear Modeling by : Ayesha Nneka Delpish
Download or read book A Comparison of Estimators in Hierarchical Linear Modeling written by Ayesha Nneka Delpish and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Automated Estimation of Vector Error Correction Models by : Zhipeng Liao
Download or read book Automated Estimation of Vector Error Correction Models written by Zhipeng Liao and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model selection and associated issues of post-model selection inference present well known challenges in empirical econometric research. These modeling issues are manifest in all applied work but they are particularly acute in multivariate time series settings such as cointegrated systems where multiple interconnected decisions can materially affect the form of the model and its interpretation. In cointegrated system modeling, empirical estimation typically proceeds in a stepwise manner that involves the determination of cointegrating rank and autoregressive lag order in a reduced rank vector autoregression followed by estimation and inference. This paper proposes an automated approach to cointegrated system modeling that uses adaptive shrinkage techniques to estimate vector error correction models with unknown cointegrating rank structure and unknown transient lag dynamic order. These methods enable simultaneous order estimation of the cointegrating rank and autoregressive order in conjunction with oracle-like efficient estimation of the cointegrating matrix and transient dynamics. As such they offer considerable advantages to the practitioner as an automated approach to the estimation of cointegrated systems. The paper develops the new methods, derives their limit theory, reports simulations and presents an empirical illustration with macroeconomic aggregates.
Book Synopsis Improved Inference for Spatial and Panel Models by : Min Seong Kim
Download or read book Improved Inference for Spatial and Panel Models written by Min Seong Kim and published by . This book was released on 2011 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 1 is "Heteroskedasticity and Spatiotemporal Dependence Robust Inference for Linear Panel Models with Fixed Effects." This chapter studies robust inference for linear panel models with fixed effects in the presence of heteroskedasticity and spatiotemporal dependence of unknown forms. We propose a bivariate kernel covariance estimator, which is flexible to nest existing estimators as special cases with certain choices of bandwidths. For distributional approximations, we consider two different types of asymptotics. When the level of smoothing is assumed to increase with the sample size, the proposed estimator is consistent and the associated Wald statistic converges to a chi square distribution. We show that our covariance estimator improves upon existing estimators in terms of robustness and efficiency. When we assume the level of smoothing to be held fixed, the covariance estimator has a random limit and we show by asymptotic expansion that the limiting distribution of the test statistic depends on the bandwidth parameters, the kernel function, and the number of restrictions being tested. As this distribution is nonstandard, we establish the validity of an F-approximation to this distribution, which greatly facilitates the test. For optimal bandwidth selection, we propose a procedure based on the upper bound of asymptotic mean square error criterion. The flexibility of our estimator and proposed bandwidth selection procedure make our estimator adaptive to the dependence structure in data. This adaptiveness automates the selection of covariance estimator. That is, our estimator reduces to the existing estimators which are designed to cope with the particular dependence structures. Simulation results show that the F-approximation and the adaptiveness work reasonably well. Chapter 2 is "Spatial Heteroskedasticity and Autocorrelation Consistent Estimation of Covariance Matrix". This chapter considers spatial heteroskedasticity and autocorrelation consistent (spatial HAC) estimation of covariance matrices of parameter estimators. We generalize the spatial HAC estimators introduced by Kelejian and Prucha (2007) to apply to linear and nonlinear spatial models with moment conditions. We establish its consistency, rate of convergence and asymptotic truncated mean squared error (MSE). Based on the asymptotic truncated MSE criterion, we derive the optimal bandwidth parameter and suggest its data dependent estimation procedure using a parametric plug-in method. The finite sample performances of the spatial HAC estimator are evaluated via Monte Carlo simulation. Chapter 3 is "k-step Bootstrap Bias Correction for Fixed Effects Estimator in Nonlinear Panel Models." Fixed effects estimators in nonlinear panel models with fixed T usually suffer from inconsistency because of the incidental parameters problem first noted by Neyman and Scott (1948). Moreover, even though T grows at the same rate as n, they are asymptotically biased and therefore the associated confidence interval has a large coverage error. This chapter proposes a k-step parametric bootstrap bias corrected estimator. We prove that our estimator is asymptotically normal and is centered at the true parameter if T grows faster than n to a third power. In addition to bias correction, we construct a confidence interval with a double bootstrap procedure, and Monte Carlo experiments confirm that the error in coverage probability of our CI's is smaller than those of the alternatives. We also propose bias correction for average marginal effects.
