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Computational finance 1999 : selection of papers presented at Computational Finance '99 at the Stern School of Business, New York University, in January 1999

Abu-Mostafa, Yaser S. (ed.) - 2000

Persistent link: https://www.econbiz.de/10001387916

Special issue: Symposium on computational finance

Jensen, Bjarne Astrup (contributor) - 2000

Persistent link: https://www.econbiz.de/10001653140

Linear optimal weighting estimator (LOWE) for efficient parallel hybridization of load forecasts

Chahkotahi, Fatemeh; Khashei, Mehdi - In: Journal of modelling in management 17 (2022) 3, pp. 1028-1048

Persistent link: https://www.econbiz.de/10013362699

Conditional heteroscedasticity models with time-varying parameters : estimation and asymptotics

Pourkhanali, Armin; Keith, Jonathan; Zhang, Xibin - 2020

Persistent link: https://www.econbiz.de/10012697180

Discrete forecast reconciliation

Zhang, Bohan; Panagiotelis, Anastasios; Kang, Yanfei - In: European journal of operational research : EJOR 318 (2024) 1, pp. 143-153

Persistent link: https://www.econbiz.de/10015047691

Sparse multiple index models for high-dimensional nonparametric forecasting

Palihawadana, Nuwani K.; Hyndman, Rob J.; Wang, Xiaoqian - 2024

Persistent link: https://www.econbiz.de/10015073818

An evolutionary bootstrap approach to neural network pruning and generalization

LeBaron, Blake Dean - 1997

Persistent link: https://www.econbiz.de/10000979616

Non-linear error correction, asymmetric adjustment and cointegration

Escribano, Álvaro - In: Economic modelling 15 (1998) 2, pp. 197-216

Persistent link: https://www.econbiz.de/10001247645

Nonparametric tests for the independence of regressors and disturbances as specification tests

Johnson, David S. - In: The review of economics and statistics 79 (1997) 2, pp. 335-340

Persistent link: https://www.econbiz.de/10001222402

Time Series Forecasting With a Learning Algorithm : An Approximate Dynamic Programming Approach

Collado, Ricardo - 2019

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...

Persistent link: https://www.econbiz.de/10012894079