Add to ORKGA time-varying quantile can be \u85tted to a sequence of observa-tions by formulating a state space model and iteratively applying a suitably modi\u85ed signal extraction algorithm. Quantiles estimated in this way provide information on various aspects of a time series, in-cluding dispersion, asymmetry and, for \u85nancial applications, value at risk. Estimates of the quantiles at the end of the series are the basis for forecasting. As such they o¤er an alternative to conditional quantile autoregressions and, at the same time, give some insight into their structure and potential drawbacks
Scaling phenomena can be found in a variety of physical situations, ranging from applications in hyd...
This paper proposes a Bayesian approach to quantile autoregressive (QAR) time series model estimatio...
There is an increasing interest in studying time-varying quantiles, particularly for environmental p...
A time-varying quantile can be fitted to a sequence of observations by formulating a time series mod...
A time-varying quantile can be fitted to a sequence of observations by formulating a time series mod...
A time-varying quantile can be fitted by formulating a time series model for the corresponding popul...
This thesis examines the use of quantile methods to better estimate the time-varying conditional ass...
We consider jointly modeling a finite collection of quantiles over time. Formal Bayesian inference o...
We consider the problem of estimating the conditional quantile of a time series at time \(t\) given ...
We consider the problem of estimating the conditional quantile of a time series at time t given obse...
Motivated by a broad range of potential applications, we address the quantile prediction problem of ...
Self-exciting threshold autoregressive time series models have been used extensively and the conditi...
Value at Risk models are concerned with the estimation of conditional quantiles of a time series. Fo...
Recently, Bayesian solutions to the quantile regression problem, via the likeli-hood of a Skewed-Lap...
This book integrates the fundamentals of asymptotic theory of statistical inference for time series ...
Scaling phenomena can be found in a variety of physical situations, ranging from applications in hyd...
This paper proposes a Bayesian approach to quantile autoregressive (QAR) time series model estimatio...
There is an increasing interest in studying time-varying quantiles, particularly for environmental p...
A time-varying quantile can be fitted to a sequence of observations by formulating a time series mod...
A time-varying quantile can be fitted to a sequence of observations by formulating a time series mod...
A time-varying quantile can be fitted by formulating a time series model for the corresponding popul...
This thesis examines the use of quantile methods to better estimate the time-varying conditional ass...
We consider jointly modeling a finite collection of quantiles over time. Formal Bayesian inference o...
We consider the problem of estimating the conditional quantile of a time series at time \(t\) given ...
We consider the problem of estimating the conditional quantile of a time series at time t given obse...
Motivated by a broad range of potential applications, we address the quantile prediction problem of ...
Self-exciting threshold autoregressive time series models have been used extensively and the conditi...
Value at Risk models are concerned with the estimation of conditional quantiles of a time series. Fo...
Recently, Bayesian solutions to the quantile regression problem, via the likeli-hood of a Skewed-Lap...
This book integrates the fundamentals of asymptotic theory of statistical inference for time series ...
Scaling phenomena can be found in a variety of physical situations, ranging from applications in hyd...
This paper proposes a Bayesian approach to quantile autoregressive (QAR) time series model estimatio...
There is an increasing interest in studying time-varying quantiles, particularly for environmental p...