Add to ORKGThe task of assessing posterior distributions from noisy empirical data imposes difficult requiremen...
Macroeconomic indicators are typically appraised in seasonally adjusted form, and forecasts are ofte...
The seasonal adjustment method proposed by Schlicht (1981) can be viewed as a method that minimizes ...
An analytical Bayesian approach to seasonal analysis is proposed, using robust priors to control for...
This article proposes an alternative methodology for modeling and forecasting seasonal series. The a...
Dynamic Linear Models are a state space model framework based on the Kalman filter. We use this fram...
This chapter reviews the principal methods used by researchers when forecasting seasonal time series...
SIGLEAvailable from British Library Document Supply Centre- DSC:3597.442(STICERD-DP-EM--93-266) / BL...
Multiple seasonalities play a key role in time series forecasting, especially for business time seri...
We describe observation driven time series models for Student-t and EGB2 conditional distributions i...
In this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observed time serie...
The aim of this paper is to develop a model-based seasonal adjustment method which will yield the sa...
textabstractIn this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observe...
Climate models are generally calibrated manually by comparing selected climate statistics, such as t...
The intention of this paper is to define and estimate several classes of models of seasonal behavior...
The task of assessing posterior distributions from noisy empirical data imposes difficult requiremen...
Macroeconomic indicators are typically appraised in seasonally adjusted form, and forecasts are ofte...
The seasonal adjustment method proposed by Schlicht (1981) can be viewed as a method that minimizes ...
An analytical Bayesian approach to seasonal analysis is proposed, using robust priors to control for...
This article proposes an alternative methodology for modeling and forecasting seasonal series. The a...
Dynamic Linear Models are a state space model framework based on the Kalman filter. We use this fram...
This chapter reviews the principal methods used by researchers when forecasting seasonal time series...
SIGLEAvailable from British Library Document Supply Centre- DSC:3597.442(STICERD-DP-EM--93-266) / BL...
Multiple seasonalities play a key role in time series forecasting, especially for business time seri...
We describe observation driven time series models for Student-t and EGB2 conditional distributions i...
In this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observed time serie...
The aim of this paper is to develop a model-based seasonal adjustment method which will yield the sa...
textabstractIn this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observe...
Climate models are generally calibrated manually by comparing selected climate statistics, such as t...
The intention of this paper is to define and estimate several classes of models of seasonal behavior...
The task of assessing posterior distributions from noisy empirical data imposes difficult requiremen...
Macroeconomic indicators are typically appraised in seasonally adjusted form, and forecasts are ofte...
The seasonal adjustment method proposed by Schlicht (1981) can be viewed as a method that minimizes ...