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Chapter 13 Forecasting Seasonal Time Series

Ghysels, Eric; Osborn, Denise R.; Rodrigues, Paulo M.M. - 2006

integrated models and deterministic seasonality models. As well as examining how forecasts are computed in each case, the …. Section 3 discusses less traditional models, specifically nonlinear seasonal models and models for seasonality in variance …. Such nonlinear models primarily concentrate on interactions between seasonality and the business cycle, either using a …

Persistent link: https://www.econbiz.de/10014023693

Forecasting HICP package holidays with forward-looking booking data

Schnorrenberger, Richard; Schwind, Patrick; Wieland, … - 2024

exhibit strong seasonality, notable volatility, and methodological breaks. We present two modelling strategies for predicting …

Persistent link: https://www.econbiz.de/10015079886

Seasonal adjustment of the Spanish sales daily data

Cuevas, Ángel; Ledo, Ramiro; Quilis, Enrique M. - In: SERIEs : Journal of the Spanish Economic Association 12 (2021) 4, pp. 687-708

We present a procedure to perform seasonal adjustment over daily sales data. The model adjusts daily information from the Immediate Supply of Information System for Value Added Tax declaration forms compiled by the Spanish Tax Agency. The procedure performs signal extraction and forecasting at...

Persistent link: https://www.econbiz.de/10012694357

Time series forecasting using a moving average model for extrapolation of number of tourist

Ivanovski, Zoran; Milenkovski, Ace; Narasanov, Zoran - In: UTMS journal of economics / University of Tourism and … 9 (2018) 2, pp. 121-132

Time series is a collection of observations made at regular time intervals and its analysis refers to problems in correlations among successive observations. Time series analysis is applied in all areas of statistics but some of the most important include macroeconomic and financial time series....

Persistent link: https://www.econbiz.de/10012178433

Modelling the dynamics of stock market in the Gulf Cooperation Council countries : evidence on persistence to shocks

Boubaker, Heni; Saidane, Bassem; Zorgati, Mouna Ben Saad - In: Financial innovation : FIN 8 (2022), pp. 1-22

-linearity, and multiple seasonality or time-varying correlations. Our study indicates that the joint dual long-memory process can …

Persistent link: https://www.econbiz.de/10013272684

Linear identification of linear rational-expectations models by exogenous variables reconciles Lucas and Sims

Zadrozny, Peter A. - 2022

Linear rational-expectations models (LREMs) are conventionally "forwardly" estimated as follows. Structural coefficients are restricted by economic restrictions in terms of deep parameters. For given deep parameters, structural equations are solved for "rational-expectations solution" (RES)...

Persistent link: https://www.econbiz.de/10013465436

The Power of Text-based Indicators in Forecasting the Italian Economic Activity

Aprigliano, Valentina; Emiliozzi, Simone; Guaitoli, Gabriele - 2021

Can we use newspaper articles to forecast economic activity? Our answer is yes and, to this end, we propose a brand new economic dictionary in Italian with valence shifters, and we apply it to a corpus of about two million articles from four popular newspapers. We produce a set of high-frequency...

Persistent link: https://www.econbiz.de/10013226486

Forecasting Aggregate Demand : Analytical Comparison of Top-Down and Bottom-Up Approaches in a Multivariate Exponential Smoothing Framework

Sbrana, Giacomo - 2013

Forecasting aggregate demand is a crucial matter in all industrial sectors. In this paper, we provide the analytical prediction properties of top-down (TD) and bottom-up (BU) approaches when forecasting aggregate demand, using multivariate exponential smoothing as demand planning framework. We...

Persistent link: https://www.econbiz.de/10013072596

Mixed Frequency Structural VARs

Foroni, Claudia - 2014

A mismatch between the time scale of a structural VAR (SVAR) model and that of the time series data used for its estimation can have serious consequences for identification, estimation and interpretation of the impulse response functions. However, the use of mixed frequency data, combined with a...

Persistent link: https://www.econbiz.de/10013060376

Pooling-Based Data Interpolation and Backdating

Marcellino, Massimiliano - 2014

Pooling forecasts obtained from different procedures typically reduces the mean square forecast error and more generally improves the quality of the forecast. In this paper we evaluate whether pooling interpolated or backdated time series obtained from different procedures can also improve the...

Persistent link: https://www.econbiz.de/10014062151