Markov chains and Mixture Transition Distribution are the most traditional models for binary time series. More recently, Startz (2008) has introduced a novel model for binary data, the so-called Binomial AutoRegressive Moving Average model basically based on the comparison between theoretical and empirical autopersistence functions. However, some economic phenomena show a long memory structure not captured by any of these formulations. For quarterly U.S. binary data on recession, we show that a long-memory model for binary data can substantially improve the fit
We discuss models that impart a form of long memory in raw time series xt or instantaneous functions...
In forecasting problems it is important to know whether or not recent events rep-resent a regime cha...
textabstractThere is substantial evidence that several economic time series variables experience occ...
A.1. Background on long memory models. As mentioned in the introduction, long-memory estimation is t...
The time series of German and Italian recessions are analyzed using a novel approach for binary time...
This article revises semiparametric methods of inference on different aspects of long mem-ory time s...
Recent research has focused on the links between long memory and structural change, stress-ing the l...
Some recent developments in the analysis of time series are applied to real economic data. It is ass...
We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally inte...
International audienceTwo recent contributions have found conditions for large dimensional networks ...
The time series of German and Italian recessions are analyzed using a novel approach for binary time...
We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally inte...
[[abstract]]We consider an autoregressive regime-switching model for the dynamic mean structure of a...
In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other ...
Recently De Luca and Carfora (Statistica e Applicazioni 8:123–134, 2010) have proposed a novel model...
We discuss models that impart a form of long memory in raw time series xt or instantaneous functions...
In forecasting problems it is important to know whether or not recent events rep-resent a regime cha...
textabstractThere is substantial evidence that several economic time series variables experience occ...
A.1. Background on long memory models. As mentioned in the introduction, long-memory estimation is t...
The time series of German and Italian recessions are analyzed using a novel approach for binary time...
This article revises semiparametric methods of inference on different aspects of long mem-ory time s...
Recent research has focused on the links between long memory and structural change, stress-ing the l...
Some recent developments in the analysis of time series are applied to real economic data. It is ass...
We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally inte...
International audienceTwo recent contributions have found conditions for large dimensional networks ...
The time series of German and Italian recessions are analyzed using a novel approach for binary time...
We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally inte...
[[abstract]]We consider an autoregressive regime-switching model for the dynamic mean structure of a...
In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other ...
Recently De Luca and Carfora (Statistica e Applicazioni 8:123–134, 2010) have proposed a novel model...
We discuss models that impart a form of long memory in raw time series xt or instantaneous functions...
In forecasting problems it is important to know whether or not recent events rep-resent a regime cha...
textabstractThere is substantial evidence that several economic time series variables experience occ...