Numerous economic time series do not have a constant mean and in practical situations, we often see that the variance of observational error is subject to a substantial variability over time. This phenomenon is known ...Numerous economic time series do not have a constant mean and in practical situations, we often see that the variance of observational error is subject to a substantial variability over time. This phenomenon is known as volatility. To take into account the presence of volatility in an economic series, it is necessary to resort to models known as conditional heteroscedastic models. In these models, the variance of a series at a given time point depends on past information and other data available up to that time point, so that a conditional variance must be defined, which is not constant and does not coincides with the overall variance of the observed series. There is a very large variety of nonlinear models in the literature, which are useful for the analysis of any economic time series with volatility, but we will focus in analyzing our series of interest using ARCH type models introduced by Engle (1982) and their extensions. These models are non-linear in terms of variance. Our objective will be the study of the monthly inflation data of Argentina for the period from January 1943 to December 2013. The data is officially published by the National Institute of Statistics and Censuses (or INDEC as it is known in Argentina). Although it is a very long period in which various changes and interventions took place, it can be seen that certain general patterns of behavior have persisted over time, which allows us to admit that the study can be appropriately based on available information.展开更多
文摘Numerous economic time series do not have a constant mean and in practical situations, we often see that the variance of observational error is subject to a substantial variability over time. This phenomenon is known as volatility. To take into account the presence of volatility in an economic series, it is necessary to resort to models known as conditional heteroscedastic models. In these models, the variance of a series at a given time point depends on past information and other data available up to that time point, so that a conditional variance must be defined, which is not constant and does not coincides with the overall variance of the observed series. There is a very large variety of nonlinear models in the literature, which are useful for the analysis of any economic time series with volatility, but we will focus in analyzing our series of interest using ARCH type models introduced by Engle (1982) and their extensions. These models are non-linear in terms of variance. Our objective will be the study of the monthly inflation data of Argentina for the period from January 1943 to December 2013. The data is officially published by the National Institute of Statistics and Censuses (or INDEC as it is known in Argentina). Although it is a very long period in which various changes and interventions took place, it can be seen that certain general patterns of behavior have persisted over time, which allows us to admit that the study can be appropriately based on available information.