Over the past decade, long-term memory processes have been an important part of time series analysis. Long-term memory has important applications in evaluating market performance and macroeconomic, financial, and accounting variables, while the researcher may in some cases have to convert data or in other words manipulate data, which is expected to provide some data features that are important in economic forecasting. Therefore, in this study, the effect of data conversion on long-term memory and structural dependency and finally the results of the study were investigated. For this purpose, the existence of long-term memory in raw and converted data is first tested. To understand the effect of long-term memory on structural dependence, the tail dependence coefficient between raw and manipulated data (seasonal data 1991 to 2018) is estimated; the results show that raw data have long-term memory, more tail dependence on the manipulated data. It changes the nature of the data and not only reduces their memory but also reduces animal dependency.
Hasanshahi, M. (2019). The Impact of data conversion on the long-term memory of time series and its consequences
Case Study: Oil Price Data. Journal of Econometric Modelling, 4(4), 85-105. doi: 10.22075/jem.2019.18463.1357
MLA
Morteza Hasanshahi. "The Impact of data conversion on the long-term memory of time series and its consequences
Case Study: Oil Price Data", Journal of Econometric Modelling, 4, 4, 2019, 85-105. doi: 10.22075/jem.2019.18463.1357
HARVARD
Hasanshahi, M. (2019). 'The Impact of data conversion on the long-term memory of time series and its consequences
Case Study: Oil Price Data', Journal of Econometric Modelling, 4(4), pp. 85-105. doi: 10.22075/jem.2019.18463.1357
VANCOUVER
Hasanshahi, M. The Impact of data conversion on the long-term memory of time series and its consequences
Case Study: Oil Price Data. Journal of Econometric Modelling, 2019; 4(4): 85-105. doi: 10.22075/jem.2019.18463.1357
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