Time series models

Time series models

Systems that examine series of historical data; sometimes used as a means of technical forecasting, by examining moving averages.
References in periodicals archive ?
On the basis of developed time series model we have found that best time series model for forecasting wheat production of Pakistan is ARIMA (1 2 2) because this model has lower AIC and SBIC as compared to other fitted time series models.
Also the traditional linear modeling approach is extended to nonlinear modeling by applying fractional order tests to spot and futures prices and structural time series models in the frequency domain and wavelet models in the time-frequency domain are employed as superior to previous time series and regression methods.
Geweke also details how models can be applied to specific problems, including linear models and policy choices, modeling with latent variables and missing data, prediction with time series models, and comparison and evaluation of models.
It is quite useful in time series models and it enriches the study of the dynamic relationship between variables in the context of univariate and multivariate autoregressive moving average (ARMA) models [Beguin et al., 1980; Lii, 1985; Tiao and Tsay, 1989; Claverie et al., 1990; Berlinet and Francq, 1994].
Chapter eleven deals with models of term structure of interest rates that, according to the authors, can be viewed as "time series models for the stochastic discount factor." The explosion of interest in this topic is apparently due to the practical need to price and hedge fixed-income derivative securities.
This graduate textbook introduces linear processes in terms of the Wold expansion and an infinite moving-average representation, examines the relationships between Wold expansions and state space representations, and reviews several methodologies for estimating time series models. Later chapters discuss nonlinear time series for analyzing financial instruments, the prediction of linear and nonlinear processes, nonstationary time series models, seasonal patterns, time series regression, the effects of data gaps on the analysis of long-memory processes, and non-Gaussian time series.
of London) takes a practical and accessible approach, focusing on statistical computing and applied data as he covers the principles of Bayesian inference, model comparison and choice, regression for metric outcomes, models for binary and count outcomes, random effect and latent variable models for multi-category outcomes, ordinal regression, discrete spatial data, time series models for discrete variables, hierarchical and panel data models and missing-data models.
It is argued that time series models provide better information than more traditional cross-sectional models, and the VAR modeling framework here avoids certain technical problems found in other time series approaches.
However other issues with using alternate benchmark source data remain: estimates from time series models are vulnerable to large errors at economic turning points, and issues of data validation and general comparability with UI data trends can be difficult to resolve.
This edition has a redesigned chapter on multivariate time series analysis with more on vector autoregressive (VAR) models and discussion of the analytical tools for modeling vector time series; an expanded chapter on special topics like unit root testing, time-varying volatility models like ARCH (autoregressive conditional heteroscedasticity) and GARCH (generalized autoregressive conditional heteroscedasticity), nonlinear time series models, and long-memory models; enhanced and updated numerical illustrations through the use of R; added exercises; and edited tables and added graphs.
Forecasts can be obtained by various methods such to purely judgmental approval, Structural econometric models, invariant time series models or in combination (Bessler Chamberlain, 1989); (Olorunnipa 1989) and (Rosa, 1990).
In another recent work, Chandra & Kumari (2018) compared various time series models to forecast FTAs in India and found Seasonal ARIMA to be better in producing the desired forecast.