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Artificial neural networks for predicting real estate prices
Núñez Tabales, Julia M.; Caridad y Ocerin, José María; … - In: Revista de Métodos Cuantitativos para la Economía y … 15 (2013), pp. 29-44
Econometric models, in the estimation of real estate prices, are a useful and realistic approach for buyers and for …
Persistent link: https://www.econbiz.de/10011307187
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Artificial Neural Networks for Predicting Real Estate Prices || Redes neuronales artificiales para la predicción de precios inmobiliarios
Núñez Tabales, Julia M.; Ocerin, Caridad y; María, José - In: Revista de Métodos Cuantitativos para la Economía y … 15 (2013) 1, pp. 29-44
Econometric models, in the estimation of real estate prices, are a useful and realistic approach for buyers and for …
Persistent link: https://www.econbiz.de/10010665024
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A Multi-Task Deep Learning Model for Inflation Forecasting : Dynamic Phillips Curve Neural Network
Proner, Robert - 2023
Managing inflation is vital for a stable economy, but forecasting remains challenging. ML methods, like neural networks, have shown promise in forecasting inflation and other macroeconomic variables. In this paper, I propose DPCNet, a deep multi-task learning model, to jointly forecast inflation...
Persistent link: https://www.econbiz.de/10014354498
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Measuring tanker market future risk with the use of FORESIM
Lyridis, Dimitrios; Manos, Nikolaos; Zacharioudakis, … - In: Spoudai : journal of economics and business 67 (2017) 1, pp. 38-53
Future market risk has always been a critical question in decision support processes. FORESIM is a simulation technique that models shipping markets (developed recently). In this paper we present the application of this technique in order to obtain useful information regarding future values of...
Persistent link: https://www.econbiz.de/10011661763
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A comparative analysis of alternative univariate time series models in forecasting Turkish inflation
Catik, A. Nazif; Karacuka, Mehmet - 2011
This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at...
Persistent link: https://www.econbiz.de/10009125642
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Estimating nonlinear heterogeneous agents models with neural networks
Kase, Hanno; Melosi, Leonardo; Rottner, Matthias - 2022
Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models … predictions of the model. We leverage the recent advancements in machine learning to develop a solution and estimation method … method is much more efficient than existing global solution methods and that the estimation converges to the true parameter …
Persistent link: https://www.econbiz.de/10013257224
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Chapter 8 Forecasting economic variables with nonlinear models
Teräsvirta, Timo - 2006
building are parameter estimation and evaluation that are also briefly considered. There are two possibilities of generating …
Persistent link: https://www.econbiz.de/10014023698
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Tackling large outliers in macroeconomic data with vector artificial neural network autoregression
Polito, Vito; Zhang, Yunyi - 2021
We develop a regime switching vector autoregression where artificial neural networks drive time variation in the coefficients of the conditional mean of the endogenous variables and the variance covariance matrix of the disturbances. The model is equipped with a stability constraint to ensure...
Persistent link: https://www.econbiz.de/10012668293
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Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation
Moshiri, Saeed; Cameron, Norman Edward; Scuse, David - 2013
The back-propagation neural network (BPN) model has been the most popular form of artificial neural network model used for forecasting, particularly in economics and finance. It is a static (feed-forward) model which has a learning process in both hidden and output layers. In this paper, we...
Persistent link: https://www.econbiz.de/10014217731
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Neural Network versus Econometric Models in Forecasting Inflation
Moshiri, Saeed; Cameron, Norman Edward - 2013
Artificial neural network modeling has recently attracted much attention as a new technique for estimation and …
Persistent link: https://www.econbiz.de/10014217738