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- Gil-Alaña, Luis A. (2000): A generalized fractional time series model
We propose in this article a general time series model, whose components are modelled in terms of fractionally integrated processes. This specification allows us to consider the trend, the seasonal and the cyclical components as stochastic processes, including the unit root models as particular cases.
RePEc:zbw:sfb373:2000107 Save to MyIDEAS - Tsyplakov, Alexander (2015): Quasifiltering for time-series modeling
In the paper a method for constructing new varieties of time-series models is proposed. The idea is to start from an unobserved components model in a state-space form and use it as an inspiration for development of another time-series model, in which time-varying underlying variables are directly observed. The goal is to replace a state-space model with an intractable likelihood function by another model, for which the likelihood function can be written in a closed form. If state transition equation of the parent state-space model is linear Gaussian, then the resulting model would belong to the class of score driven model (aka GAS, DCS).
RePEc:pra:mprapa:66453 Save to MyIDEAS - Tommaso, Proietti & Alessandra, Luati (2012): Maximum likelihood estimation of time series models: the Kalman filter and beyond
The purpose of this chapter is to provide a comprehensive treatment of likelihood inference for state space models. These are a class of time series models relating an observable time series to quantities called states, which are characterized by a simple temporal dependence structure, typically a first order Markov process. ... Time-varying volatility, which is quintessential to finance, is an important feature also in macroeconomics. In the multivariate framework relevant features can be common to different series, meaning that the driving forces of a particular feature and/or the transmission mechanism are the same.
RePEc:pra:mprapa:39600 Save to MyIDEAS - Lindsay W. Turner & Stephen F. Witt (2001): Forecasting Tourism Using Univariate and Multivariate Structural Time Series Models
Structural time series models have shown significant potential as both univariate and explanatory forecasting tools. Inbound tourism to New Zealand from Australia, Japan, the UK and the USA disaggregated by purpose of visit is analysed, using both univariate and multivariate structural time series models, and their respective forecasting accuracy is compared. The naïve ‘no change’ model is used for benchmark comparison purposes. The structural time series model outperforms the naïve process, but the causal structural time series model does not generate more accurate forecasts than the univariate model.
RePEc:sae:toueco:v:7:y:2001:i:2:p:135-147 Save to MyIDEAS - Proietti, Tommaso (1999): Structural Time Series Modelling of Capacity Utilisation
In this paper we introduce a structural non-linear time series model for joint estimation of capacity and its utilisation, thereby providing the statistical underpinnings to a measurement problem that has received ad hoc solutions, often underlying arbitrary assumptions. The model we propose is a particular growth model subject to a saturation level which varies over time according to a stochastic process specified a priori.
RePEc:pra:mprapa:62621 Save to MyIDEAS - Broadstock, David C. & Collins, Alan (2010): Measuring unobserved prices using the structural time-series model: The case of cycling
By specifying demand as a function of generalised price and income and then applying a structural time-series model to elucidate the unobserved component of prices (while controlling for observed income levels), it is illustrated that the role of prices in influencing demand is non-trivial.
RePEc:eee:transa:v:44:y:2010:i:4:p:195-200 Save to MyIDEAS - Franses, Ph.H.B.F. & Paap, R. (1999): Forecasting with periodic autoregressive time series models
This paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. ... We illustrate the models for various quarterly UK consumption series.
RePEc:ems:eureir:1598 Save to MyIDEAS - A. J. Lawrance & P. A. W. Lewis (1982): A Mixed Exponential Time Series Model
It should be broadly applicable for stochastic modelling in operations analysis. In particular, it provides a model for simulating interarrival times in queuing systems when these random variables are overdispersed relative to an exponential random variable, and moreover are positively correlated. The model also has capability to model a variable which may be zero, but which otherwise is exponentially distributed. Such variables are found as waiting times in queuing models. Because of the (random) linearity of the process, it is easily extended to the modelling of cross-coupled sequences of interarrival and service times.
RePEc:inm:ormnsc:v:28:y:1982:i:9:p:1045-1053 Save to MyIDEAS - Heewon Park & Fumitake Sakaori (2013): Lag weighted lasso for time series model
The adaptive lasso can consistently identify the true model in regression model. However, the adaptive lasso cannot account for lag effects, which are essential for a time series model. Consequently, the adaptive lasso can not reflect certain properties of a time series model. To improve the forecast accuracy of a time series model, we propose a lag weighted lasso.
RePEc:spr:compst:v:28:y:2013:i:2:p:493-504 Save to MyIDEAS - NGAI SZE HAN & SHIQING LING (2017): Goodness-Of-Fit Test For Nonlinear Time Series Models
Many time series models have been used extensively in modeling economic and financial data. However, it is difficult to determine the functional forms of the conditional mean and conditional variance in these models. ... The test statistic is applicable not only to the family of GARCH models but also to other nonlinear time series models.
RePEc:wsi:afexxx:v:12:y:2017:i:02:n:s2010495217500063 Save to MyIDEAS