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Robust estimation and inflation forecasting

Silvapulle, Paramsothy; Hewarathna, Ramya - In: Applied economics 34 (2002) 18, pp. 2277-2282

Persistent link: https://www.econbiz.de/10001716723

A comparison of Australian inflation forecasts

Hewarathna, Ramya - 1997

Persistent link: https://www.econbiz.de/10000985403

Local logit regression for loan recovery rate

Sopitpongstorn, Nithi; Silvapulle, Paramsothy; Gao, Jiti; … - In: Journal of banking & finance 126 (2021), pp. 1-14

Persistent link: https://www.econbiz.de/10012820172

Time series forecasting using a mixture of stationary and nonstationary predictors

Hannadige, Sium Bodha; Gao, Jiti; Silvapulle, Mervyn J.; … - 2021

Persistent link: https://www.econbiz.de/10012614548

Statistical modelling and forecast evaluation of the impact of extreme temperatures on wheat crops in North Western Victoria

Bailey, Natalia; Hochman, Zvi; Mao, Yufeng; Silvapulle, … - 2020

Persistent link: https://www.econbiz.de/10012607686

Forecasting a nonstationary time series with a mixture of stationary and nonstationary factors as predictors

Hannadige, Sium Bodha; Gao, Jiti; Silvapulle, Mervyn J.; … - 2020

Persistent link: https://www.econbiz.de/10012607687

Panel data analysis of multi-factor capital asset pricing models

Makwasha, Tariro; Wright, Jill; Silvapulle, Paramsothy - In: Applied economics 51 (2019) 60, pp. 6459-6475

Persistent link: https://www.econbiz.de/10012197351

Forecasting a nonstationary time series using a mixture of stationary and nonstationary factors as predictors

Hannadige, Sium Bodha; Gao, Jiti; Silvapulle, Mervyn J.; … - In: Journal of business & economic statistics : JBES ; a … 42 (2024) 1, pp. 122-134

Persistent link: https://www.econbiz.de/10014449839

Forecasting inflation from the term structure of interest rates

Hewarathna, Ramya; Silvapulle, Param - 1998

Persistent link: https://www.econbiz.de/10001378676

Forecasting a Nonstationary Time Series with a Mixture of Stationary and Nonstationary Factors as Predictors

Hannadige, Sium - 2020

This paper develops a method for forecasting a nonstationary time series, such as GDP, using a set of high-dimensional panel data as predictors. To this end, we use what is known as a factor augmented regression [FAR] model that contains a small number of estimated factors as predictors; the...

Persistent link: https://www.econbiz.de/10012834890