Param Silvapulle
Biographic Data
| ID | 8920103 |
|---|---|
| NAME | Param Silvapulle |
| GIVEN NAMES | Param |
| FAMILY NAME | Silvapulle |
| SIGNATURE | SILVAPULLE P |
| AFFILIATIONS | Monash University |
| ORCID | 0000-0002-1149-4515 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Forecasting a Nonstationary Time Series Using a Mixture of Stationary and Nonstationary Factors as Predictors
We develop a method for constructing prediction intervals for a nonstationary variable, such as GDP. The method uses a Factor Augmented Regression (FAR) model. The predictors in the model include a small number of factors generated to extract most of the information in a set of panel data on a large number of macroeconomic variables that are considered to be potential predictors. The novelty of this article is that it provides a method and justif…
Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models
This article introduces a new specification for the heterogenous autoregressive (HAR) model for the realized volatility of S&P 500 index returns. In this modeling framework, the coefficients of the HAR are allowed to be time-varying with unspecified functional forms. The local linear method with the cross-validation (CV) bandwidth selection is applied to estimate the time-varying coefficient HAR (TVC-HAR) model, and a bootstrap method is used to …
No prominent works on this page.
Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models
This article introduces a new specification for the heterogenous autoregressive (HAR) model for the realized volatility of S&P 500 index returns. In this modeling framework, the coefficients of the HAR are allowed to be time-varying with unspecified functional forms. The local linear method with the cross-validation (CV) bandwidth selection is applied to estimate the time-varying coefficient HAR (TVC-HAR) model, and a bootstrap method is used to …
Forecasting a Nonstationary Time Series Using a Mixture of Stationary and Nonstationary Factors as Predictors
We develop a method for constructing prediction intervals for a nonstationary variable, such as GDP. The method uses a Factor Augmented Regression (FAR) model. The predictors in the model include a small number of factors generated to extract most of the information in a set of panel data on a large number of macroeconomic variables that are considered to be potential predictors. The novelty of this article is that it provides a method and justif…
Econometrics (2 works) · Economics (2 works) · Mathematics (2 works) · Monetary Policy and Economic Impact (2 works) · Statistics (2 works) · Competitor analysis (1 work) · Computer Science (1 work) · Data set (1 work) · Estimation (1 work) · Factor analysis (1 work)