Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models PDF Download
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Author: Liudas Giraitis
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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In this article, we introduce the general setting of a multivariate time series autoregressive model with stochastic time-varying coefficients and time-varying conditional variance of the error process. This allows modelling VAR dynamics for non-stationary time series and estimation of time-varying parameter processes by the well-known rolling regression estimation techniques. We establish consistency, convergence rates, and asymptotic normality for kernel estimators of the paths of coefficient processes and provide pointwise valid standard errors. The method is applied to a popular seven-variable dataset to analyse evidence of time variation in empirical objects of interest for the DSGE (dynamic stochastic general equilibrium) literature.
Author: Liudas Giraitis
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this article, we introduce the general setting of a multivariate time series autoregressive model with stochastic time-varying coefficients and time-varying conditional variance of the error process. This allows modelling VAR dynamics for non-stationary time series and estimation of time-varying parameter processes by the well-known rolling regression estimation techniques. We establish consistency, convergence rates, and asymptotic normality for kernel estimators of the paths of coefficient processes and provide pointwise valid standard errors. The method is applied to a popular seven-variable dataset to analyse evidence of time variation in empirical objects of interest for the DSGE (dynamic stochastic general equilibrium) literature.
Author: Mohamed Ahkim
Publisher:
ISBN:
Category :
Languages : en
Pages : 114
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Author: Juan J. Dolado
Publisher: Emerald Group Publishing
ISBN: 1803828315
Category : Business & Economics
Languages : en
Pages : 203
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Book Description
Both parts of Volume 44 of Advances in Econometrics pay tribute to Fabio Canova for his major contributions to economics over the last four decades.
Author: Jinglan Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 168
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Book Description
Consider a varying coefficient model (Hastie and Tibshirani, 1993), where the coefficient is unknown but is dynamic in the sense that it is a function of a certain covariate. In some cases, the covariate is a special variable 'time'. Motivated by the need for varying-coefficient vector time series models (Jiang, 1999) and varying-coefficient partially linear models (Fan, Huang, and Li, 2007), we are primarily interested in time-varying coefficient models for continuous multivariate time series data and continuous longitudinal data. The challenge is how to simultaneously display serial, clustering, and multivariate attributes of the data set, to which the routinely assumed two-level and univariate response models are not able to apply. We approach this problem by a flexible new model called multiple response hierarchical time-varying mixed-effects model. So far, the thesis has focused on two responses. Extension to >2 responses involves no fundamentally new ideas. The model first uses varying-coefficient parameters for accurately describing the dynamic of the series. The new covariance matrix is decomposed into between-response correlation structure of random cluster effect and correlation structure between measurement errors. By allowing shared cluster effects the model allows for characterizing homogeneity in repeated measurements in the same cluster. By allowing for time dependent error terms, it is possible to model the correlation induced by within-subject variation. We adopt a similar approach of Fan and Gijbels (1996), where we first propose local linear regression estimators for the varying coefficients, and then obtain random effect prediction by maximizing the profile likelihood with a closed-form solution. Asymptotic results give good insight into the properties of estimators. It is shown that estimates are consistent. We also conduct the model comparison, it turns out that the proposed methods outperform the traditional univariate response models, nonparametric models, and linear mixed effects models in both predicting the response and estimating the coefficient surface based on simulation studies. Finally, we have applied this model to a real-world study on the price-volume relation of NASDAQ stock market data.
Author: Michael Thomas Braun
Publisher:
ISBN:
Category : Longitudinal method
Languages : en
Pages : 184
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Author: Yang Wang
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Category :
Languages : en
Pages :
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Author: Yifeng He
Publisher:
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Category :
Languages : en
Pages :
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Author: Gunky Kim
Publisher:
ISBN:
Category : Multivariate analysis
Languages : en
Pages : 27
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Author: Robert Bartels
Publisher:
ISBN: 9780646027210
Category : Analysis of variance
Languages : en
Pages : 24
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Author: D.F. Nicholls
Publisher: Springer Science & Business Media
ISBN: 1468462733
Category : Mathematics
Languages : en
Pages : 160
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Book Description
In this monograph we have considered a class of autoregressive models whose coefficients are random. The models have special appeal among the non-linear models so far considered in the statistical literature, in that their analysis is quite tractable. It has been possible to find conditions for stationarity and stability, to derive estimates of the unknown parameters, to establish asymptotic properties of these estimates and to obtain tests of certain hypotheses of interest. We are grateful to many colleagues in both Departments of Statistics at the Australian National University and in the Department of Mathematics at the University of Wo110ngong. Their constructive criticism has aided in the presentation of this monograph. We would also like to thank Dr M. A. Ward of the Department of Mathematics, Australian National University whose program produced, after minor modifications, the "three dimensional" graphs of the log-likelihood functions which appear on pages 83-86. Finally we would like to thank J. Radley, H. Patrikka and D. Hewson for their contributions towards the typing of a difficult manuscript. IV CONTENTS CHAPTER 1 INTRODUCTION 1. 1 Introduction 1 Appendix 1. 1 11 Appendix 1. 2 14 CHAPTER 2 STATIONARITY AND STABILITY 15 2. 1 Introduction 15 2. 2 Singly-Infinite Stationarity 16 2. 3 Doubly-Infinite Stationarity 19 2. 4 The Case of a Unit Eigenvalue 31 2. 5 Stability of RCA Models 33 2. 6 Strict Stationarity 37 Appendix 2. 1 38 CHAPTER 3 LEAST SQUARES ESTIMATION OF SCALAR MODELS 40 3.
