The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients

2005 ◽  
Vol 30 (1) ◽  
pp. 33-53 ◽  
Author(s):  
Hany S. Guirguis ◽  
Christos I. Giannikos ◽  
Randy I. Anderson
Keyword(s):  
Asset Pricing ◽  
Housing Market ◽  
Time Varying ◽  
Varying Coefficients ◽  
The Us ◽  
Time Varying Coefficients

Co-Movements of US and Asian Equity Markets: Evidence from Asymmetric and Time-Varying Coefficients

2014 ◽  
Vol 17 (04) ◽  
pp. 1450021 ◽  
Author(s):  
Bharat Kolluri ◽  
Susan Machuga ◽  
Mahmoud Wahab
Keyword(s):  
Piecewise Linear ◽  
Equity Markets ◽  
Time Varying ◽  
Conditional Mean ◽  
Varying Coefficients ◽  
The Us ◽  
Sharpe Ratios ◽  
Time Varying Coefficients ◽  
Movement Parameters ◽  
Performance Ranking

We examine co-movements of nine Asian equity markets with both the US and Japan with special interest in distinguishing co-movements during periods of positive returns from those during periods of negative returns. A discrete asymmetric piecewise linear conditional mean returns specification is adopted to generate asymmetric co-movement parameters for periods of positive and negative returns. Conditional heteroskedasticity is modeled using GARCH and EGARCH specifications. Predicted conditional volatilities are used to generate alternative estimates of asymmetric and time-varying co-movement parameters. Conditional mean returns from asymmetric and symmetric conditional mean return models along with GARCH and EGARCH volatilities are used to generate estimates of asymmetric and symmetric conditional (ex-ante) Sharpe ratios. Asian markets returns and volatilities show a clear tendency to move more with the US than with Japan; and their co-movements with negative US returns far exceed their co-movements with positive US returns, thereby suggesting that any diversification benefits into Asian equities are likely to manifest themselves more during periods of positive than negative US returns. Conditional asymmetric Sharpe ratios exceed conditional symmetric Sharpe ratios; however, and more importantly, performance-ranking differs depending on whether asymmetric versus symmetric conditional Sharpe ratios are used. Asymmetric conditional Sharpe ratios suggest that India (followed by Malaysia) offers the best return/risk tradeoff, with the least favorable market is South Korea (using GARCH) and Philippines (using EGARCH).

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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