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Investment Forecasting With Multivariate Linear Regression

Fuller, James R. - 2010

The procedure for estimating probabilities of future investment returns using time-shifted indexes is based on the simple principle that a multi-dimensional conditional probability distribution can be envisioned involving investment total returns (for a single investment or a fixed portfolio of...

Persistent link: https://www.econbiz.de/10014072195

lmForc : Linear Model Forecasting in R

Rayl, Nelson - 2022

these evaluation functions. Functions in the lmForc package are built around a new S4 class, Forecast, which introduces a … test model specifications and understand precisely how a model arrives at a forecast …

Persistent link: https://www.econbiz.de/10014030652

Investment Forecasting with Multivariate Linear Regression

Fuller, James R. - 2010

Persistent link: https://www.econbiz.de/10014198891

Forecasting the real prices of crude oil : what is the role of parameter instability?

Wang, Yudong; Hao, Xianfeng - In: Energy economics 117 (2023), pp. 1-20

Persistent link: https://www.econbiz.de/10014436646

Forecasting in factor augmented regressions under structural change

Massacci, Daniele; Kapetanios, George - In: International journal of forecasting 40 (2024) 1, pp. 62-76

Persistent link: https://www.econbiz.de/10014450259

Forecasting the real prices of crude oil : a robust weighted least squares approach

Wang, Yudong; Hao, Xianfeng - In: Energy economics 116 (2022), pp. 1-18

Persistent link: https://www.econbiz.de/10013542061

Testing for structural stability of factor augmented forecasting models

Corradi, Valentina; Swanson, Norman R. - In: Journal of econometrics 182 (2014) 1, pp. 100-118

Persistent link: https://www.econbiz.de/10010497112

Employing a generalized reduced gradient algorithm method to form combinations of steel price forecasts generated separately by ARIMA-TF and ANN models

Terregrossa, Salvatore Joseph; Şener, Uğur - In: Cogent economics & finance 11 (2023) 1, pp. 1-25

component model forecast weights, which is a novel approach introduced by this study. The data set employed includes a time … forecasts of all models (component and combining), mean absolute percentage forecast errors (MAPE) are calculated and model … their hedging decisions on a combination of forecasts generated by ARIMA-TF and ANN models, with the forecast weights …

Persistent link: https://www.econbiz.de/10014500535

Forecasting daily and monthly exchange rates with machine learning techniques

Plakandaras, Vasilios; Papadimitriou, Theophilos; … - In: Journal of forecasting 34 (2015) 7, pp. 560-573

Persistent link: https://www.econbiz.de/10011390457

Regularizing Bayesian predictive regressions

Feng, Guanhao; Polson, Nicholas G. - In: The journal of asset management 21 (2020) 7, pp. 591-608

Persistent link: https://www.econbiz.de/10012421072