This paper discusses identification, specification, estimation and forecasting for a general class of periodic … formulations are introduced for exact maximum likelihood estimation, component estimation and forecasting. Identification issues …

limited to short-term predictions, the value of Google data for forecasting purposes is episodic, and the improvements in … forecasting accuracy are only modest. The results, obtained by (pseudo) out-of-sample forecast comparison, are robust to a state … limitations of using big data to predict economic indicators. …

data on the forecasting performance of the model is assessed. The Google data is found to yield modest improvements in … forecasting accuracy of the model. To the author’s knowledge, this is the first time the forecasting performance of the Google … mode of the posterior distribution of the hyperparameters, and this is found to improve the out-of-sample forecasting …

In this report we document the ETLAnow project. ETLAnow is a model for forecasting with big data. At the moment, it …

as benchmark. Finally, we replicate the forecasting experiment including as predictors both an indicator of unemployment …

in its ability to accurately capture clusters and preserve or enhance forecasting accuracy. For a high …

, researchers around the world are constantly developing new forecasting models to successfully predict the unemployment rate. This … article presents a new model that combines two deep learning methodologies used for time series forecasting to find the future …