Trending time-varying coefficient time series models with serially correlated errors

2007 ◽  
Vol 136 (1) ◽  
pp. 163-188 ◽  
Author(s):  
Zongwu Cai
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Time Varying ◽  
Correlated Errors ◽  
Varying Coefficient

scholarly journals Wavelet-M-Estimation for Time-Varying Coefficient Time Series Models

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xingcai Zhou ◽  
Fangxia Zhu
Keyword(s):  
Time Series ◽  
Asymptotic Properties ◽  
Time Series Models ◽  
Time Varying ◽  
Bahadur Representation ◽  
Conditional Quantile ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
M Estimation ◽  
Time Varying Coefficients

This paper proposes wavelet-M-estimation for time-varying coefficient time series models by using a robust-type wavelet technique, which can adapt to local features of the time-varying coefficients and does not require the smoothness of the unknown time-varying coefficient. The wavelet-M-estimation has the desired asymptotic properties and can be used to estimate conditional quantile and to robustify the usual mean regression. Under mild assumptions, the Bahadur representation and the asymptotic normality of wavelet-M-estimation are established.

scholarly journals Estimating the Level of the Brazilian Yield Curve Using the Time-Varying Coefficient Model GAS (2,2) with Gamma Distribution

2019 ◽  
Vol 11 (9) ◽  
pp. 1
Author(s):  
Daiane Rodrigues dos Santos ◽  
Tiago Costa Ribeiro ◽  
Marco Aurélio Sanfins
Keyword(s):  
Time Series ◽  
Gamma Distribution ◽  
Yield Curve ◽  
Developing Economies ◽  
Evaluation Criteria ◽  
Score Function ◽  
Predictive Distribution ◽  
Time Varying ◽  
Varying Coefficient ◽  
Time Series Modelling

The level of the yield curve is strongly associated with a very important macroeconomic variable for developing economies: the inflation. Therefore, it becomes relevant for economic studies the development of a time series model that can accurately predict this variable. This article proposes the estimation and prediction of the yield curve level using the GAS (Generalized Autoregressive Score) class of time-varying coefficient models. The formulation of these models facilitates a general framework for time series modelling presenting a series of advantages, including the possibility of specifying any conditional distribution deemed appropriate for the yield curve level. In addition, the complete structure of the predictive distribution is transported to the mechanism that updates the time-varying parameters, via score function. When analyzing the evaluation criteria, the measures of adherence, and both Wilcoxon and Diebold & Mariano tests, it was verified that the adjustment of the GAS model (2,2) with gamma distribution to the series containing the Brazilian Yield Curve level of January 2006 and February 2017 presented a satisfactory result.

scholarly journals Comparing Cross-Section and Time-Series Factor Models

2019 ◽  
Vol 33 (5) ◽  
pp. 1891-1926 ◽  
Author(s):  
Eugene F Fama ◽  
Kenneth R French
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Factor Models ◽  
Constant Factor ◽  
Time Series Models ◽  
Time Varying ◽  
Oxford University ◽  
Regression Approach ◽  
Fama And French ◽  
Published Paper

Abstract We use the cross-section regression approach of Fama and MacBeth (1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (2015). Time-series models that use only cross-section factors provide better descriptions of average returns than time-series models that use time-series factors. This is true when we impose constant factor loadings and when we use time-varying loadings that are natural for time-series factors and time-varying loadings that are natural for cross-section factors. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Sign in / Sign up

Export Citation Format

Share Document