کاربرد روش‌های نیمه پارامتریک و موجک‌ها در بررسی وجود پایداری نرخ تورم ایران
محورهای موضوعی : اقتصاد کار و جمعیت
احمد جعفری صمیمی
1
(استاد اقتصاد دانشگا مازندران)
روزبه بالونژاد
2
(دانشجوی دکتری اقتصاد مازندران)
کلید واژه: پایداری تورم, ARFIMA, روش‌های نیمه پارامتریک, موجک‌ها, نرخ تورم,
چکیده مقاله :
در این مقاله وجود پایداری در نرخ تورم ایران آزمون می‌شود. برای این منظور، درجه انباشتگی کسری، با استفاده از روش‌های GPH، تعدیل رابینسون، ریزن، وایتل و موجک‌ها و با استفاده از داده‌های بانک مرکزی در مورد شاخص قیمت مصرف کننده سال‌های 1351-1390، تخمین زده شد. نتایج حاصل از تحقیق، بیانگر وجود پایداری در نرخ تورم ایران است. وجود ایستایی و پایداری نرخ تورم در اقتصاد، بیانگر این است که در صورت بروز یک تکانه بر نرخ تورم، اثر آن تا مدتی طولانی باقی می‌ماند. این نتیجه می‌تواند در اتخاذ سیاست‌های مرتبط، مورد توجه تصمیم گیرندگان اقتصادی قرار گیرد.
In this study, the existence of inflation persistent rate is examined in Iran. For this purpose, the degree of fractional integration is estimated by using GPH, Robinson adjustment, Reisen, Whittle, wavelets methods and consumer price index data of Central Bank during 1972-2010. The results indicate a persistent rate of inflation in Iran. The stationary and persistence of inflation rate indicates that, by a shock in inflation rate, its effects remains for a long time. This may be considered by economic decision-makers to select appropriate policies.
منابع
- Balcilar, M. (2004). Persistence in inflation: Does aggregation cause long memory? Emerging markets finance and trade, 40(2): 25-56.
- Calvo, G. (1983). Staggered prices in a utility maximizing framework. Journal of Monetary Economics, 12(3): 383-3.
- Chauvet, M., & Kim, I. (2010). Microfoundations of inflation persistence in the New Keynesian Phillips Curve. MPRA paper 2310, University library of Munich, Germany.
- Cheung, Y.W., & Lai, K. (1993). A fractional cointegration analysis of purchasing power parity. Journal of Business & Economic Statistics, 11(1):103-112.
- Delgado, M.A., & Robinson, P.M. (1994). New methods for the analysis of long-memory time series: Application to Spanish inflation. Journal of Forecasting, 13(2): 97-107.
- Diebold, F. X., & Rudebusch, G. D. (1989). Long memory and persistence in aggregate output. Journal of Monetary Economics, 24(2): 189-209.
- Driscoll, J.C., & Holden, S. (2004). Fairness and inflation persistence. Journal of the European Economic Association, 2(2): 240-251.
- Fay, G., & Moulines, E., & Roueff, F., & Taqqu, M.S. (2009). Estimators of long-memory: Fourier versus wavelets. Journal of Econometrics, 151(2): 95 114.
- Gadea, M., & Mayoral, L. (2005). The persistence of inflation in OECD Countries: A fractionally integrated approach. International Journal of Central Banking, 2(1): 51-104.
- Geweke, J.S., & Porter, S.H. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4(4): 221-238.
- Gordon, R.J. (1982). Why stopping inflation may be costly: Evidence from fourteen historical episodes, in: Hall, R.E. (Ed.). Inflation: Causes and consequences (Chicago: university of Chicago press).
- Granger, C., & Joyeux, R. (1980). An introduction to long memory time series and fractional differencing. Journal of Time Series Analysis, 1(1):15–29.
- Graps, A. (1995). An introduction to wavelets. IEEE computational science and engineering, 2(2): 50-61.
- Hall, R. (1999). Comment on rethinking the role of the NAIRU in monetary policy: Implications of model formulation and uncertainty. Working paper.
- Hassler, U., & Wolters, J. (1995). Long memory in inflation rates: International evidence. Journal of Business & Economic Statistics, 13(1): 37-45.
- Hassler, U., & Scheithauer, J. (2011). Detecting changes from short to long memory, statistical papers. Springer, 52(4): 847-870.
- Jensen, M.J. (2000). An alternative maximum likelihood estimator of long-memory processes using compactly supported wavelets. Journal of Economic Dynamics and Control, 24(3): 361-387.
- Juillard, M., & Kamenik, O., & Kumhof, M., & Laxton, D. (2008). Optimal price setting and inflation inertia in a rational expectations model. Journal of Economic Dynamics and Control, 32(8): 2584-2621.
- Kim, C.J., & Nelson, C.R., & Piger, J. (2004). The less-volatile U.S. economy: A bayesian investigation of timing, breadth, and potential explanations. Journal of Business and Economic Statistics, 22(1): 80-93.
- Nielsen, M. O., & Frederiksen, P. (2008). Finite sample accuracy and choice of sampling frequency in integrated volatility estimation. Journal of Empirical Finance,15(2): 265-286.
- Perciva, D., & Walden, A. (2000). Wavelet methods for time series analysis. Cambridge University Press.
- Pivetta, F., & Reis, R. (2004). The persistence of inflation in the United States. Mimeo, Harvard University.
- Reisen, V. A. (1994). Estimation of the fractional difference parameter in the ARFIMA(p,d,q) model using the smoothed Period gram. Journal Time Series Analysis, 15(1): 335–350.
- Robinson, P.M. (1995a). Gaussian semi parametric estimation of long range dependence. Annals of Statistics, 23: 1630-1661.
- Schleicher, C. (2002). An introduction to wavelets for economists. Working papers 02-3, Bank of Canada.
- Sowell, F. (1992). Maximum likelihood estimation of stationary univariate fractionally integrated time series models. Journal of Econometrics, 53(1-3): 165-188.
- Taqqu, M.S., & Teverovsky, V. (1998). Long-range dependence in finite and infinite variance time series, Ed: By R. Adler, R. Feldman, and M. S. Taqqu, 12(1): 177-217.
- Taylor, J.B. (1979). Staggered wage setting in a Macro Model. American Economic Review, 69(2): 108-113.
- Tillmann, P. (2012). Has inflation persistence changed under EMU? German Economic Review, 13(1): 86–102.
- Ysusi, C. (2009). Analysis of the dynamics of Mexican inflation using wavelets. Working papers 2009-09.
- Walsh, C.E. (2010). Monetary theory and policy. MIT Press.
- Whitcher, B., & Jensen, M. (2000). Wavelet estimation of a local long memory parapeter. Journal of Exploration Geophysics, 31(2): 94-103.