Document Type : Research Paper

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Abstract

Abstract
The study of the effect of memory in different economic indices, especially inflation and money market, has high research attractiveness. In this paper, by using the data of consumer price index for Iran during 1990/04 – 2011/11, we investigate the characteristics of CPI’s long–run memory and regress its ARFIMA model. In addition, the amount of error terms in ARFIMA model are examined by FIGARCH model in order to determine what model the heteroscedasticity in inflation is following. The results indicate that monthly time series of inflation may have non-integer root. In other words, the degree of integration for inflation can be a non-integer number rather than an integer. To determine this, an Augmented Dikey-Fuller test, Philips–Prone test and KPSS are used and the results show that the degree of integration for inflation series should lie between zero and one. Thus, the hypothesis of inflation series with memory is proposed. By estimating the parameter of long run memory in the model it becomes evident that the inflation series has the degree of integration of 0.46 and one time differentiating leads to over-differentiation. Hence, inflation series has a long run memory in Iran and the effects of each shock on this variable exists for long periods.

Keywords

برایان، اسنودن (1980)، راهنمای نوین اقتصاد کلان، ترجمة منصور خلیلی عراقی و علی سوری، تهران، انتشارات برادران، 1383.
عباسی‌نژاد، حسین و احمد تشکینی (1389)، اقتصادسنجی کاربردی پیشرفته، تهران، انتشارات دانشکده علوم اقتصادی و نور علم.
عرفانی، علیرضا (1388)، پیش‌بینی شاخص کل بورس اوراق بهادار تهران با مدل ARFIMA، فصلنامه تحقیقات اقتصادی،  شماره 86.
کشاورز حداد، غلامرضا (1385)، ﺗﺤﻠﻴﻞ اﺛﺮات ﺗﻘﻮﻳﻤﻲ در ﻧﻮﺳﺎﻧﺎت ﻗﻴﻤﺖ ﺑﺮﺧﻲ از ﻛﺎﻻﻫﺎی اﺳﺎﺳﻲ (ﻣﻄﺎﻟﻌﻪ ﻣﻮردی: دادهﻫﺎی ﻣﺎﻫﻴﺎﻧﻪ ﻗﻴﻤﺖ ﮔﻮﺷﺖ ﻣﺮغ، ﮔﻮﺷﺖ ﻗﺮﻣﺰ و ﺗﺨﻢ ﻣﺮغ)، ﻣﺠله ﺗﺤﻘﻴﻘﺎت اﻗﺘﺼﺎدی، شماره 73.
کشاورز حداد، غلامرضا و باقر صمدی (1388)، برآورد و دقت پیش‌بینی تلاطم بازدهی در بازار سهام تهران و مقایسه دقت روش‌ها در تخمین ارزش در معرض خطر: کاربردی از مدل‌های خانواده FIGARCH، مجله تحقیقات اقتصادی، شماره 86.
کشاورز حداد، غلامرضا، (در دست چاپ)، مباحثی در  روش‌های اﻗﺘﺼﺎدﺳﻨﺠﻲ، تهران، انتشارات نی.
محمدی، تیمور و رضا طالبلو (1389)، پویایی‌های تورم و رابطه تورم و عدم اطمینان اسمی با استفاده از الگوی ARFIMA- GARCH، پژوهشنامه اقتصادی، سال دهم، شماره اول.
محمودی، وحید، شاپور محمدی و هستی چیت‌سازان (1389)، بررسی روند حافظه­ بلندمدت در بازارهای جهانی نفت، فصلنامه تحقیقات مدل‌سازی اقصادی، سال اول، شماره اول.
مشیری، سعید و حبیب مروت (زمستان 1385)، پیش‌بینی شاخص کل بازدهی سهام تهران با استفاده از مدل‌های خطی و غیرخطی، فصلنامه پژوهشنامه بازرگانی،  شماره 41.
Baillie, R.T and Chung, F.C (1996), Analysing inflation by the fractionally integrated ARFIMA–GARCH model, Journal of Applied Econometrics, Vol 11.
Baillie, R. T. and Bollerslev (1992), Prediction in Dynamic Models with TimeDependent Conditional Variance, Journal of Econometric, Vol 52.
Ball, L (1992), Why Does High Inflation Raise Inflation Uncertainty?, Journal of Monetary Economics, Vol 29.
 Bollerslev, T (1986), Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometric, Vol 31.
Box, G. E. P. and G. M. Jenkins (1976), Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco.
Brunner, A.D. and G.D. Hess (1993), Are Higher Levels of Inflation Less Predictable? A State-dependent Conditional Heteroskedasticity Approach, Journal of Business and Economic Statistics, Vol 11.
Bos.S. Charles, Koopman.S. Jan and Ooms.Marius (2007), Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks, Tinbergen Institute Discussion Paper, TI 2007-099/4.
Cheung, Y.-W and F. X. Diebold (1994), On Maximum Likelihood Estimation of the Differencing Parameter of Fractionally Integrated Noise with Unknown Mean, Journal of Econometrics, Vol 62.
Dickey, D. A. and W. A. Fuller (1979), Distribution of the Estimators for Autoregressive Time Series with a Unit Root, Journal of the American Statistical Association, Vol 74.
Engle, R. F (1982), Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of U. K. Inflation, Econometrica, No. 50.
Geweke, J. and Porter-Hudak, S (1983), The estimation and Application of Long Memory Time Series Models, Journal of Time Series Analysis, No. 4.
Granger, C. W. J. (1980), Long Memory Relationships and the Aggregation of Dynamic Models, Journal of Econometrics, Vol 14.
Granger, C. W. J. and R. Joyeux (1980), An Introduction to Long Memory Time Series Models and Fractional Differencing, Journal of Time Series Analysis, Vol 1.
Hosking, J. R. M (1981), Fractional Differencing, Biometrika, Vol 68.
Kwiatkowski, D., P. C. B. Phillips, P. Schmidt and Y. Shin (1992), Testing the Null hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure are we that Economic Time Series are Non Stationary? Journal of Econometrics, Vol 54.
Phillips, P. C. B. and Perron, P (1988), Testing for a Unit Root in Time Series Regression. Biometrika, No. 75.
R. T. Baillie (1996). Long Memory Processes and Fractional Integration in Econometrics, Journal of Econometrics, Vol 73.
Robinson, F. Peter (2003), Time Series with Long Memory, Oxford University Press.
Sowell, F. B. (1992), Maximum Likelihood Estimation of Stationary Univariate Fractionally-integrated Time-Series Models, Journal of Econometrics, Vol 53.
Tsay, Wen- Jen (2008), Analysing Inflation by the ARFIMA Model with Markov-Switching Fractional Dierencing Parameter, The Institute of Economics, Academia Sinica, Taiwan.