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Machine learning advances for time series forecasting
Masini, Ricardo P.; Medeiros, Marcelo C.; Mendes, Eduardo F. - 2020
series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to …
Persistent link: https://www.econbiz.de/10012390030
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Machine learning advances for time series forecasting
Masini, Ricardo P.; Medeiros, Marcelo C.; Mendes, Eduardo F. - In: Journal of economic surveys 37 (2023) 1, pp. 76-111
Persistent link: https://www.econbiz.de/10014287800
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Forecasting tourism demand to Catalonia : neural networks vs. time series models
Claveria, Oscar; Torra, Salvador - In: Economic modelling 36 (2014), pp. 220-228
Persistent link: https://www.econbiz.de/10010412358
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A self-organizing map analysis of survey-based agents' expectations before impending shocks for model selection : the case of the 2008 financial crisis
Claveria, Oscar; Monte, Enric; Torra, Salvador - In: International economics : a journal published by CEPII … 146 (2016), pp. 40-58
Persistent link: https://www.econbiz.de/10011623524
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Forecasting economic activity using a neural network in uncertain times : Monte Carlo evidence and application to the German GDP
Holtemöller, Oliver; Kozyrev, Boris - Leibniz-Institut für Wirtschaftsforschung Halle - 2024
In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN …
Persistent link: https://www.econbiz.de/10014496850
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Forecasting inflation expectations from the CESifo World Economic Survey : an empirical application in inflation targeting countries
Zárate-Solano, Héctor M.; Zapata-Sanabria, Daniel R. - 2018
across countries by using a two-step approach that selects the most accurate linear or non-linear forecasting method for each … outperform ARIMA linear models for longer forecasting horizons. This holds true for countries with both soft and brisk changes of … expectations. However, when forecasting one step ahead, the performance between the two methods is similar. …
Persistent link: https://www.econbiz.de/10011913189
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Forecasting Realized Volatility with Linear and Nonlinear Models
McAleer, Michael; Medeiros, Marcelo C. - 2010
In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from...
Persistent link: https://www.econbiz.de/10011807392
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Forecasting the market risk premium with artificial neural networks
Oikonomikou, Leoni Eleni - 2016 - Revised version: November 2015
forecasting model is tested with data from the US stock market. The proposed model-based forecasting method aims to capture … patterns in the data that improve the forecasting accuracy of the Market Risk Premium in the tested market and indicates …
Persistent link: https://www.econbiz.de/10011454074
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Forecasting the Albanian time series with linear and nonlinear univariate models
Vika, Blerina; Vika, Ilir - 2021
Persistent link: https://www.econbiz.de/10012792581
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Modelling and Simulation: An Overview
McAleer, Michael; Chan, Felix; Oxley, Les - 2013
underwriters and issuing firms in the Japanese corporate bond market, stochastic life table forecasting: a time-simultaneous fan …
Persistent link: https://www.econbiz.de/10010326266