Add to ORKGAnalysis of economic time series often involves correlograms and partial correlograms as graphical descriptions of temporal dependence. Two methods are available for computing these statistics: one based on autocorrelations and the other on scaled autocovariances. For stationary time series the resulting plots are nearly identical. When it comes to economic time series that usually exhibit non-stationary features these methods can lead to very different results. This has two consequences: (i) incorrect inferences can be drawn when confusing these concepts; (ii) a better discrimination between stationary and non-stationarity appears when using autocorrelations rather than autocovariances which are commonly used in econometric software
This text presents modern developments in time series analysis and focuses on their application to e...
The presented method called Significant Non-stationarities, represents an exploratory tool for ident...
This paper introduces the notion of common non-causal features and proposes tools to detect them in ...
In time-series analysis of business and economic data (e.g. stock index data; corporate dividend pay...
The second order properties of a process are usually characterized by the autocovariance function. I...
‘Classical ’ econometric theory assumes that observed data come from a stationary process, where mea...
Many macroeconomic time series exhibit non-stationary behaviour. When modelling such series an impor...
Some recent developments in the analysis of time series are applied to real economic data. It is ass...
'Classical' econometric theory assumes that observed data come from a stationary process, where mean...
In this paper the serial dependences between the observed time series and the lagged series, taken i...
Here we present a novel approach to the description of the lagged interdependence structure of stati...
Many economic variables are correlated over time. It is important to determine whether this observed...
In this article the serial dependences between the observed time series and the lagged series, taken...
This paper introduces the notion of common non-causal features and proposes tools to detect them in ...
A new test to detect changes in the covariance structure of a time series is developed. The test doe...
This text presents modern developments in time series analysis and focuses on their application to e...
The presented method called Significant Non-stationarities, represents an exploratory tool for ident...
This paper introduces the notion of common non-causal features and proposes tools to detect them in ...
In time-series analysis of business and economic data (e.g. stock index data; corporate dividend pay...
The second order properties of a process are usually characterized by the autocovariance function. I...
‘Classical ’ econometric theory assumes that observed data come from a stationary process, where mea...
Many macroeconomic time series exhibit non-stationary behaviour. When modelling such series an impor...
Some recent developments in the analysis of time series are applied to real economic data. It is ass...
'Classical' econometric theory assumes that observed data come from a stationary process, where mean...
In this paper the serial dependences between the observed time series and the lagged series, taken i...
Here we present a novel approach to the description of the lagged interdependence structure of stati...
Many economic variables are correlated over time. It is important to determine whether this observed...
In this article the serial dependences between the observed time series and the lagged series, taken...
This paper introduces the notion of common non-causal features and proposes tools to detect them in ...
A new test to detect changes in the covariance structure of a time series is developed. The test doe...
This text presents modern developments in time series analysis and focuses on their application to e...
The presented method called Significant Non-stationarities, represents an exploratory tool for ident...
This paper introduces the notion of common non-causal features and proposes tools to detect them in ...