scholarly journals Time-varying stock return predictability: the Eurozone case

2015 ◽  
pp. 28-38
Author(s):  
Nuno Silva
Keyword(s):  
Statistical Tests ◽  
Economic Value ◽  
Return Predictability ◽  
Time Varying ◽  
Stock Return Predictability ◽  
Varying Coefficients ◽  
Time Varying Parameters ◽  
Constant Coefficients ◽  
Time Varying Coefficients ◽  
Better Than

In this paper, we test the existence of predictability in eleven Eurozone stock markets, using both regressions with constant coefficients and with time-varying coefficients. Our results show that there is statistical evidence of predictability in some countries. The economic value of the forecasting models is much stronger than what could be inferred, based on the statistical tests. A meanvariance investor could have obtained substantial utility gains in most countries. Overall, models with time-varying parameters perform slightly better than models with constant coefficients.http://dx.doi.org/10.14195/2183‑203X_41_4

Time Varying Hydrodynamics Identification of a Flexible Riser Under Multi-Frequency Vortex-Induced Vibrations

2017 ◽  
Author(s):  
Chang Liu ◽  
Shixiao Fu ◽  
Mengmeng Zhang ◽  
Haojie Ren
Keyword(s):  
High Frequency ◽  
Coupling Effect ◽  
Time Varying ◽  
Flexible Riser ◽  
Force Coefficients ◽  
Basic Frequency ◽  
Varying Coefficients ◽  
Time Varying Parameters ◽  
Frequency Coupling ◽  
Time Varying Coefficients

In this paper, Forgetting Factor Recursive Least Squares (FF-LS) method is proposed to identify time-varying vortex-induced force coefficients of the flexible riser under multi-frequency VIV. FF-LS method introduces the forgetting factor, which gives more weight to the data closer to the present. This modification improves the method’s sensitivity to the time-varying parameters, and enables it to identify the time-varying parameters under multi-frequency coupling. In this paper, the mass-spring-dashpot model is used to verify FF-LS method’s ability to accurately identify time-varying parameters under multi-frequency coupling. Then, this methodology is used to identify the vortex-induced force coefficients of flexible riser at the basic frequency when vortex-induced vibration occurs. Identified coefficients are consistent with the result obtained from Least Squares method, which indicates that the proposed FF-LS method retrogrades to the Least Squares method when VIV response and the vortex-induced force contains only one single frequency. Finally, this methodology is used to identify the time-varying vortex-induced force coefficients of a flexible riser under VIV considering the coupling effect between the basic frequency and high frequency. The vortex-induced force reconstructed from identified time-varying coefficients is consistent with real vortex-induced force, which verifies the validity and applicability of this methodology in identifying time-varying vortex-induced force coefficients considering multi-frequency coupling effect. The results show that when the flexible riser is subjected to multi-frequency VIV, its vortex-induced force coefficients change periodically, the time-averaged values of these time-varying coefficients are different from the vortex-induced force coefficients at the basic frequency, which results from the coupling effect between the basic frequency and high frequency. Linear superposition of vortex-induced force coefficient at basic frequency and high frequency are different from time-varying coefficients considering coupling effect of multi-frequency and cannot reconstruct vortex-induced force correctly.

G-7 INFLATION FORECASTS: RANDOM WALK, PHILLIPS CURVE OR WHAT ELSE?

2007 ◽  
Vol 11 (1) ◽  
pp. 1-30 ◽  
Author(s):  
FABIO CANOVA
Keyword(s):  
Economic Theory ◽  
Random Walk ◽  
Turning Point ◽  
Phillips Curve ◽  
Time Varying ◽  
Varying Coefficients ◽  
Inflation Forecasts ◽  
Forecasting Performance ◽  
Time Varying Coefficients ◽  
Better Than

This paper compares the forecasting performance of some leading models of inflation for G-7 countries. We show that bivariate and trivariate models suggested by economic theory or statistical analysis are not much better than univariate ones. Phillips curve specifications fit well into this class. Improvements in both the MSE of the forecasts and turning point prediction are obtained with time-varying coefficients models, which exploit international interdependencies. The performance of the latter class of models is stable throughout the 1990s.

scholarly journals The VIT Transform Approach to Discrete-Time Signals and Linear Time-Varying Systems

Eng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 99-125
Author(s):  
Edward W. Kamen
Keyword(s):  
Discrete Time ◽  
Initial Conditions ◽  
Linear Time ◽  
Order Term ◽  
Initial Time ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Signals ◽  
Time Varying Systems ◽  
Time Varying Coefficients

A transform approach based on a variable initial time (VIT) formulation is developed for discrete-time signals and linear time-varying discrete-time systems or digital filters. The VIT transform is a formal power series in z−1, which converts functions given by linear time-varying difference equations into left polynomial fractions with variable coefficients, and with initial conditions incorporated into the framework. It is shown that the transform satisfies a number of properties that are analogous to those of the ordinary z-transform, and that it is possible to do scaling of z−i by time functions, which results in left-fraction forms for the transform of a large class of functions including sinusoids with general time-varying amplitudes and frequencies. Using the extended right Euclidean algorithm in a skew polynomial ring with time-varying coefficients, it is shown that a sum of left polynomial fractions can be written as a single fraction, which results in linear time-varying recursions for the inverse transform of the combined fraction. The extraction of a first-order term from a given polynomial fraction is carried out in terms of the evaluation of zi at time functions. In the application to linear time-varying systems, it is proved that the VIT transform of the system output is equal to the product of the VIT transform of the input and the VIT transform of the unit-pulse response function. For systems given by a time-varying moving average or an autoregressive model, the transform framework is used to determine the steady-state output response resulting from various signal inputs such as the step and cosine functions.

scholarly journals Estimating latent group structure in time-varying coefficient panel data models

2019 ◽  
Author(s):  
Jia Chen
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Clustering Method ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Latent Group ◽  
Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

scholarly journals Gray’s Time-Varying Coefficients Model for Posttransplant Survival of Pediatric Liver Transplant Recipients with a Diagnosis of Cancer

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Yi Ren ◽  
Chung-Chou H. Chang ◽  
Gabriel L. Zenarosa ◽  
Heather E. Tomko ◽  
Drew Michael S. Donnell ◽  
...  
Keyword(s):  
Liver Transplant ◽  
Failure Time ◽  
Penalized Splines ◽  
Time Varying ◽  
Piecewise Constant ◽  
Varying Coefficients ◽  
Covariate Effects ◽  
Pediatric Liver ◽  
Time Varying Coefficients ◽  
Pediatric Liver Transplant

Transplantation is often the only viable treatment for pediatric patients with end-stage liver disease. Making well-informed decisions on when to proceed with transplantation requires accurate predictors of transplant survival. The standard Cox proportional hazards (PH) model assumes that covariate effects are time-invariant on right-censored failure time; however, this assumption may not always hold. Gray’s piecewise constant time-varying coefficients (PC-TVC) model offers greater flexibility to capture the temporal changes of covariate effects without losing the mathematical simplicity of Cox PH model. In the present work, we examined the Cox PH and Gray PC-TVC models on the posttransplant survival analysis of 288 pediatric liver transplant patients diagnosed with cancer. We obtained potential predictors through univariable(P<0.15)and multivariable models with forward selection(P<0.05)for the Cox PH and Gray PC-TVC models, which coincide. While the Cox PH model provided reasonable average results in estimating covariate effects on posttransplant survival, the Gray model using piecewise constant penalized splines showed more details of how those effects change over time.

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