تحلیل مانایی سری زمانی شاخص قیمت مصرف کننده در ایران و ارائه یک مدل ARIMA برای پیش بینی آن
محورهای موضوعی : مدیریت استراتژیکصمد کاظمی 1 * , پوریا سوری 2 , مهدي غضنفري 3 , میرسامان پیشوایی 4
1 - دانشگاه علم و صنعت
2 - دانشگاه علم و صنعت
3 - دانشگاه علم و صنعت
4 - دانشگاه علم و صنعت
کلید واژه: سری زمانی آزمون ریشه واحد مانایی مدل خودرگرسیو انباشته با میانگین متحرک پیش بینی,
چکیده مقاله :
در این پژوهش برای تعیین مانایی یا غیر مانایی داده های سری زمانی شاخص قیمت مصرف کننده در ایران از سال 1359 تا 1391 به قیمت های ثابت سال 1383، پس از کشف روند تغییرات داده های سری زمانی و تعیین مرتبه خود رگرسیو، میانگین متحرک و میانگین متحرک خودرگرسیو سری زمانی، آزمون ریشه واحد را روی داده های سری زمانی انجام می دهیم و عدم مانایی سری مشخص می شود. در ادامه با انجام آزمون دیکی فولر تکمیل شده، مشخص می شود که سری زمانی تنها یک ریشه واحد دارد. پس از حصول اطمینان از داشتن تنها یک ریشه واحد، مدل فرایند خودرگرسیون انباشته با میانگین متحرک را برای این سری زمانی برآورد می کنیم. سپس کفایت مدل را با آزمون پورتمن تیو بررسی کرده و تصادفی خالص بودن سری زمانی مشخص می شود. در نهایت با استفاده از مدل به دست آمده، مقادیر آینده داده های سری زمانی را در دو فاصله اطمینان 80 و 95 درصد از سال 1392 تا 1401 پیش بینی می کنیم.
This study aims to determine stationarity or non-stationarity of time series data of consumer price index (CPI) in 1980-2012 (i.e. 1359-1391 based on Iranian calendar) time period where 2004 (i.e. 1383 Iranian calendar) is the base year. applying the appropriate methods to detect the trend of time series and determine the autoregressive, moving average and autoregressive moving average of CPI time series autoregressive, moving average and autoregressive moving average of CPI time series, we perform unit root test that results in non-stationarity of CPI time series. using the Augmented Dicky-Fuller test, it is concluded that the time series has only one unit root. therefore, an ARIMA model is developed for the time series data. using Portman-Teau test, the adequacy of the model and its pure randomness is proved. Finally, we use the proposed ARIMA model to forecast future data from 2013 to 2022 (i.e. 1392 to 1401 Iranian calendar) in 80 and 95 percent levels of confidence.
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