Starma Modeling and Estimation of Province-Based Inflation in Turkey

Yıl 2010, Cilt: 28 Sayı: 1, 1 - 36, 30.06.2010

K. Batu Tunay

Öz

In this study, Turkey's province-based
inflation is estimated by space-time autoregressive moving average (STARMA)
models. Study also aims to introduce STARMA models as efficient econometrical
estimation tools for the analysis of geographical based economic variables. Findings
obtained shows us that statistically significance level and explanatory power
of model are both expressively high. Consequently, this model can be used for
forecasting of province-based inflation. Thus, political authorities can easily
forecast inflation and thereby take necessary measures to cope with both
province-based and country-wide inflation. As a result of these, success of
executed policies will undoubtedly increase.

 

Anahtar Kelimeler

Inflation, spatial dependence, STARMA models

TÜRKİYE’DE İLLER TEMELİNDE ENFLASYONUN UZABHO MODELLEMESİ VE TAHMİNİ

Öz

Bu çalışmada Türkiye’de farklı iller
temelinde enflasyonun uzay-zaman ardışık bağlanım hareketli ortalama (UZABHO)
modelleriyle tahmini yapılmaktadır. Coğrafi temelli ekonomik değişkenlerin
analiz edilmesinde etkin bir ekonometrik tahmin aracı olarak UZABHO
modellerinin tanıtılması da amaçlanmaktadır. Elde edilen sonuçlar gerek
istatistik anlamlılık gerekse açıklayıcı güçleri açısından son derece
başarılıdır. Sonuçların başarısına bakılarak, söz konusu modelin bölgesel
enflasyonun öngörüsünün yapılmasında başarıyla kullanılabileceği rahatlıkla
söylenebilir. Böylelikle, politika yapanlar ülke genelinde olduğu gibi bölgesel
düzeyde de enflasyonu öngörebilecek, bölgeye özel tedbirler alınabilecek ve
uygulanacak politikaların başarı şansı da kuşkusuz artacaktır.

Anahtar Kelimeler

Enflasyon, uzaysal bağlılık, UZABHO modelleri

Kaynakça

• Abdus-Salam, M., S. Salam and M. Feridun (2006) “Forecasting Inflation in Developing Nations: The Case of Pakistan”, International Research Journal of Finance and Economics, 3(May), 138-159.
• Abdus-Salam, M. and M.K. Pervaiz (2005) “Modeling and Forecasting Pakistnan’s Inflation by Using Time Series ARIMA Models”, European Journal of Scientific Research, 9(1), 65-99.
• Abuaf, N. and P. Jorion (1990) “Purchaising Power Parity in the Long Run”, Journal of Finance, 45(1), 157-174.
• Aksu, C. and J.Y. Narayan (1991) “Forecasting with Vector ARMA and State Space Methods”, International Journal of Forecasting, 7(1), 17-30.
• Alberola, E. and J.M. Marques (1999) “On the Relevance and Nature of Regional Inflation Differentials: The Case of Spain”, Banco de Espana, Working Papers, No: 9913.
• Anselin, L. (1999) “Spatial Econometrics”, Center for Spatially Integrated Social Sciences, Working Papers, No: 2, http://www.csiss.org.
• Anselin, L. (1988) Spatial Econometrics: Methods and Models, Dortech: Kluwer Academic Publishers.
• Arbia, G., J.P. Elhorst and G. Piras (2005) “Serial and Spatial Dependence in the Growth Process of EU Regions”, Workshop on Spatial Econometrics, Kiel Institute for World Economics, Kiel, 8-9 April 2005, http://www.uni-kiel.com/ifw/konfer/spatial/ arbia_elhorst_piras.pdf.
• Beck, G., K. Hubrich and M. Marcellino (2006) “Regional Inflation Dynamics Within and Across Euro Area Countries and A Comparison with the US”, European Central Bank, Working Paper Series, No: 681.
• Beck, G.W. and A.A. Weber (2005) “Inflation Rate Dispersion and Convergence in Monetary and Economic Unions: Lessons for the ECB”, Center for Financial Studies Working Papers, No: 2005/31, Frankfurt: Goethe University, http://www.ifk-cfs.de/papers/05_31.pdf.
• Bokhari, S.M.H. and M. Feridun (2006) “Forecasting Inflation Through Econometric Models: An Empirical Study on Pakistan Data”, Doğuş Üniversitesi Dergisi, 7(1), 39-47.
• Box, G.E.P., G.M. Jenkins and G.C. Reinsel (1994) Time Series Analysis: Forecasting and Control, (third ed.), New Jersey: Prentice Hall.
• Breitung, J. and S. Das (2003) “Panel Unit Root Tests under Cross Sectional Dependence”, Mimeo, University of Bonn, http://ideas.repec.org/p/ecm/nasm04/55.html.
• Brincker, R. and P. Andersen (1999) “ARMA Models in Modal Space”, Proceedings of the 17th International Modal Analysis Conference, Proc. SPIE, 3727, 330-334, ftp://ftp.svibs.com/Download/Literature-/Papers/1999/1999_3.pdf.
• Busetti, F., L. Forni, A. Harvey and F. Venditti (2006) “Inflation Convergence and Divergence with in European Monetary Union”, ECB Working Papers, No: 574.
• Cecchetti S.G., N.C. Mark and R.J. Sonora (2002) “Price Index Convergence among United States Cities”, International Economic Review, 43(4), 1081-1099.
• Cecchetti, S. and G. Debelle (2005), “Has the Inflation Process Changed?”, BIS Working Papers, No: 185, November, http://www.bis.org/publ/work185.pdf.
• Ceglowski, J. (2003) “The Law of one Price: International Evidence for Canada”, Canadian Journal of Economics, 36(2), 373-400.
• Cliff, A.D. and J.K. Ord (1975) “Space-Time Modeling with an Application to Regional Forecasting”, Transactions of the Institute of British Geographers, 64, 119-128.
• Cliff, A.D. and J.K. Ord (1981) Spatial Processes: Models and Applications. London: Pion Limited (Militino v.d. 2004: 197 içinden aktarma).
• Dai, Y. and L. Billard (1998) “A Space-Time Bilinear Model Its Identification”, Journal of Time Series Analysis, 19(6), 657-679.
• Dalezios, N.R. and K. Adamowski (1995) “Spatio-Temporal Precipitation Modelling in Rural Watersheds”, Hydrological Sciences, 40(5), 553-568.
• Das, S. and K. Bhattacharya (2005) “Price Convergence Across Regions in India”, Bonn Econ Discussion Papers, No: 2005/1, University of Bonn, Bonn Graduate School of Economics, Department of Economics http://www.ect.uni-bonn.de/forschung /discussion/cpik.pdf
• De Grauwe, P. (1996) “Inflation Targeting to Achieve Inflation Convergence in the Transition Towards EMU”. Centre for Economic Policy Research Discussion Paper Series, No: 1457, (September). http://www.cepr.org/pubs/new-dps/dplist.asp?dpno=1457
• De Jong, P. and J. Penzer (2004) “The ARMA Model in the State Space Form”, Statistics and Probability Letters, 70(1), 119-125.
• den Reijer, A.H.J. and P.J.G. Vloar (2003) “Forecasting Inflation: An Art as well as a Science”, DNB Staff Reports, No: 107/2003, (http://www.dnb.nl/dnb/pagina.jsp?pid=tcm:13-39088-64&activepage=).
• Dheerassinghe, K.G.K. (1997) “Disparity in Regional Inflation in Sri Lanka”, Central Bank of Sri Lanka, Staff Studies, 27-28, 46-79. http://www.centralbanklanka.org /staff_studies_vol_27-28c.PDF
• Di Giacinto, V. (2006) “A Generalized Space-Time ARMA Model with an Application to Regional Unemployment Analysis in Italy”, International Regional Science Review, 29(2), 159-198.
• Duarte, M. and A.L. Wolman (2005) Fiscal Policy and Regional Inflation in A Currency Union. Federal Reserve Bank of Richmond, Working Paper, No: 03-11’in yenilenmiş versiyonu. http://home.comcast.net-/~margarida.duarte/research/DW_paper.pdf
• Dubin, R.A. (1998) “Spatial Autocorrelation: A Primer”, Journal of Housing Economics, (December), 7(4), 304-327.
• Dufour, J.M. and D. Pelletier (2002) “Linear Methods for Estimating VARMA Models with a Macroeconomic Application”, 2002 Proceedings of the Business and Economic Statistics Section of the American Statistical Association, Washington, DC, 2659-2664.
• Dufour, J.M. and D. Pelletier (2008) “Practical Methods for Modelling Weak VARMA Processes: Identification, Estimation and Specification with a Macroeconomic Application”, McGill University (Department of Economics), CIREQ and CIRANO, Discussion Paper.
• Dufour, J.M. and J. Tarek (2005) “Asymptotic Distribution of a Simple Linear Estimator for VARMA Models in Echelon Form”, Center for Interuniversity Research in Quantitative Economics (CIREQ), Working Papers, No: 10-2005. Durlauf, S. and D. Quah (1999) “The New Empirics of Economic Growth”, in J.B. Taylor and M. Woodford (ed.), Handbook of Macroeconomics, Vol. I, Amsterdam: Elsevier Science, 235-308.
• Elhorst, J.P. (2001) “Dynamic Models in Space and Time”, Geographical Analysis, 33(1), 119-140.
• Elhorst, J.P. (2003) “Specification and Estimation of Spatial Panel Data Models”, International Regional Science Review, 26(1) 244-268.
• Elhorst, J.P. (2005) “Models for Dynamic Panels in Space and Time; An application to Regional Unemployment in the EU”, Workshop on Spatial Econometrics, Kiel Institute for World Economics, Kiel, 8-9April 2005. http://ideas.repec.org/p/wiw/wiwrsa/ ersa05p81.html
• Engel, C. and J. H. Rogers (1996) “How Wide Is the Border?”, American Economic Review, 86(December), 1112-1125.
• Epperson, B.K. (1993) “Spatial and Space-Time Correlations in Systems of Subpopulations With Genetic Drift and Migration”, Genetics, 133(March), 711-727.
• Epperson, B.K. (2000) “Spatial and Space-Time Correlations in Ecological Models”, Ecological Modelling, 132(1), 63-76.
• Fan, C. S. and X. Wei (2003) “The Law of One Price: Evidence from the Transitional Economy of China”, Mimeo, Department of Economics, Lingnan University, China. http://www.hiebs.hku.hk/events_updates/pdf/ weixiangdong.pdf
• Fassois, S.D. and J.E. Lee (1990) “A Linear Multi-Stage Method for ARMAX Process Identification – Part II: Effective Structure/Parameter Estimation and Performance Evaluation”, UM-MEAM Report, No: 90-05.
• Fritzer, F., G. Moser and J. Schanler (2002) “Forecasting Austrian HICP and Its Components Using VAR and ARIMA Models”, ONB Working Papers, No: 73, (August), http://www.oenb.at/en/img/wp73_tcm16-6163.pdf
• Galbraith, J.W., A. Ullah and V. Zinde-Walsh (2002) “Estimation of The Vector Moving Average Model by Vector Autoregression”, Econometric Reviews, 21(2), 205-219.
• Giacomini, R. and C.W.J. Granger (2004) “Aggregation of Space-Time Process”, Journal of Econometrics, 118(1), 7-26.
• Gluschenko, K. (1999) “Inter-regional Variability of Inflation Rates”, Economics Education and Research Consortium Working Papers, No: 99/17. http://ideas.repec.org /p/eer/wpalle/99-17e.html
• Gruben, W.C. and D. (McLeod 2004) “Currency Competition and Inflation Convergence”, Centre for Latin American Economics Working Papers, No: 0204 http://www.dallasfed.org/latin/papers/2004/lawp0402.pdf
• Gujarati, D.N. (1999) Temel Ekonometri, Ü. Şenesen ve G.G. Şenesen (Çev.), İstanbul: Literatür Yayıncılık.
• Gudmundson, G. (1998) “A Model of Inflation with Variable Time Lags”, Central Banks of Iceland Working Papers, No: 2, (June).
• Hamaker, E.L. (2006) “Kalman Filter and State-Space Representations”, University of Virginia, Center for Developmental and Health Research Methodology Papers. http://www.cdhrm.org/showimg.php?iid=41
• Harvey, A.C. and D. Bates (2003) “Multivariate Unit Root Tests and Testing for Convergence”, University of Cambridge D.A.E. Working Papers, No: 0301. http://ideas.repec.org/p/cam/camdae/0301.html
• Hobijn, B. and D. Lakagos (2003) “Inflation Inequality in the United States”, Federal Reserve Bank of New York, Staff Reports, No: 173, (October). http://www.newyorkfed.org/research/staff_reports/sr173.pdf
• Jeanneney, S.G. and P. Hua (2001) “Does the Balassa-Samuelson Effect Apply to the Chinese Provinces?”, Centre d'Etudes et de Recherches sur le Développement International (CERDI), Working Papers, No: 2001-6.
• Junttila, J. (2001) “Structurals Breaks, ARIMA Model and Finnish Inflation Forecasts”, International Journal of Forecasting, 17(2), 203-230.
• Im, K.S. -, M.H. Pesaran and S. Shin (2003) “Testing for Unit Roots in Heterogeneous Panels”, Journal of Econometrics, 115(1), 53-74.
• Ippoliti, L. (2001) “On-Line Spatio-Temporal Prediction by A State-Space Representation of the Generalised Space Time Autoregressive Model”, Metron – International Journal of Statistics, 59(1-2), 157-168.
• Kamarianakis, Y. (2003) “Spatial-Time Series Modeling: A Review of The Proposed Methodologies”, Regional Economics Applications Laboratory (REAL) Discussion Papers, No: REAL 03-T-19.
• Kamarianakis, I. and P. Practacos (2001) “Multivariate Hierarchical Bayesian Space-Time Models in Economics”, ETK-NTTS 2001 Proceedings New Techniques and Technologies for Statistics, Eurostat, 503-514. www.iacm.forth.gr /regional/people/kamarianakis.html
• Kamarianakis, I. and P. Practacos (2002) “Space-Time Modeling of Traffic Flow”, Methods of spatial analysis – spatial time series analysis, ERSA Proceedings. http://www.iacm.forth.gr /regional/people/kamarianakis.html
• Kamarianakis, I. and P. Practacos (2003) “Forecasting Traffic Flow Conditions in an Urban Network: A Comparison of Univariate and Multivariate Procedures”, Journal of the Transportation Research Board. No: 1857, 74-84. www.iacm.forth.gr/regional/people/ kamarianakis.html
• Kamarianakis, Y. and P. Practacos (2005) “Space-Time Modelling of Traffic Flow”, Computers & Geosciences, 31(1), 119-133.
• Kapetanios, G. (2002) “A Note on an Iterative Least Squares Estimation Method for ARMA and VARMA Models”, Economic Letters, 79(3), 305-312.
• Koreisha, S.G. and T. Pukkila (1989) “Fast Linear Estimation Methods for Vector Autoregressive Moving-Average Models”, Journal of Time Series Analysis, 10(4), 325-339.
• Koreisha, S.G. and T. Pukkila (1990a) “A GLS Approach for Estimation of ARMA Models”, Journal of Time Series Analysis, 11(2), 139-151.
• Koreisha, S.G. and T. Pukkila (1990b), “Linear Methods for Estimating ARMA and Regression Models with Serial Correlation”, Comunications in Statistics – Simulation, 19(1), 71-102.
• Koreisha, S.G. and G. Yoshimoto (1991) “A Comparison Between Identification Procedures for ARMA Models”, International Statistical Review, 59(1), 37-57.
• Koreisha, S.G. and T. Pukkila (2004) “The Specification of Vector Autoregressive Moving Average Models”, Journal of Statistical Computation and Simulation, 74(8), 547-565.
• Lee, C.Y. (2004) An Integrated Environment for Analyzing STARMA Models, Department of Foresty, Michigan: Michigan State University, (December). http://fried.for.msu.edu/ieast-manual/iemanual.pdf
• Lee, C.Y. (2005) Space-Time Modeling and Application to Emerging Infectious Diseases, Doctorate Dissertation, Supervisor: B.K. Epperson, Michigan State University, Department of Foresty. LeSage, J.P. and A. Krivelyova (1999) “A Spatial Prior for Bayesian Vector Autoregressive Models”, Journal of Regional Science, 39(2), 297-317.
• Levin, A., C. Lin and C.J. Chu (2002), “Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties” Journal of Econometrics, 108(1), 1-24.
• Lütkepohl, H. (1987) Forecasting Aggregated Vector ARMA Processes, Berlin: Springer-Verlag.
• Lütkepohl, H. (2002) “Forecasting Cointegrated VARMA Processes”, in M.P. Clements &D.F. Hendry (ed.), A Companion to Economic Forecasting, Oxford: Blackwell, 179-205.
• Lütkepohl, H. (2004) “Forecasting with VARMA Models”, ECO Working Papers, No:13, Department of Economics, European University Institute, Florence. http://www.iue.it/PUB/ECO2004-25.pdf
• Lütkepohl, H. (2005) New Introduction to Multiple Time Series Analysis, Berlin: Springer.
• Lütkepohl, H. (2006) “Forecasting with VARMA Models”, Handbook of Economic Forecasting, Eds. G. Elliott, C. Granger ve A. Timmermann, 1(1), Elsevier, 287-325.
• Lütkepohl, H. and H. Claessen (1997) “Analysis of Cointegrated VARMA Processes”, Journal of Econometrics, 80(2), 223-239.
• Lütkepohl, H. and D.S. Poskitt (1996) “Specification of Echelon Form VARMA Models”, Journal of Business & Economic Statistics, 14(1), 69-79.
• Mauricio, J.A. (2005) “Exact Maximum Likelihood Estimation of Partially Nonstationary Vector ARMA Models”, Computational Statistics and Data Analysis, 50(12), 3644-3662.
• Mehrotra, A., T. Peltonen and A.S. Rivera (2007) “Modeling Inflation in China: A Regional Perspective”, Bank of Finland Institute for Economies in Transition (BOFIT), Discussion Papers, No: 19/2007.
• Mentz, M. and S.P. Sebastian (2003) “Inflation Convergence After the Introduction of the Euro”, Centre for Financial Studies Working Papers, No: 2003/30, Frankfurt: Goethe University. http://ideas.repec.org-/p/cfs/cfswop/wp200330.html
• Meyler, A., G. Kenny and T. Quinn (1998) “Forecasting Irish Inflation Using ARIMA Models”, Central Bank of Ireland Technical Paper, No:3/RT/98, (December). http://www.centralbank.ie/data/TechPaper Files/3RT98.pdf Militano, A.F., M.D. Ugarte and L. Garcia-Reinaldos (2004) “Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices”, Journal of Real Estate Finance and Economics, 29(2), 193-209.
• Nenna, M. (2001) “Price Level Convergence among Italian Cities: Any Role for the Harrod-Balassa-Samuelson Hypothesis?” CIDEI Working Papers, No: 64, University of Rome. http://www.eco.uniroma1.it /cidei/wp/abswp64.pdf
• Pallis, D. (2006). “The Trade-Off Between Inflation and Unemployment in the New European Union Member-States”, International Research Journal of Finance and Economics, 1, (January), 80-97.
• Parsley, D. and S. Wei (1996) “Convergence to the Law of One Price without Trade Barriers or Currency Fluctuations”, Quarterly Journal of Economics, 111(4), 1211-1236.
• Pfeifer, P.E. and S.E. Bodily (1990) “A Test of Space-Time ARMA Modeling and Forecasting with An Application to Real Estate Prices”, International Journal of Forecasting, 16, 255-272.
• Pfeifer, P.E. and S.J. Deutsch (1980a) A Three-Stage Iterative Procedure for Space-Time Modeling, Technometrics, 22(1), 35-47.
• Pfeifer, P.E. and S.J. Deutsch (1980b) “Identification and Interpretation of First-Order Space-Time ARMA Models”, Technometrics, 22 (3), 397-403.
• Pfeifer, P.E. and S.J. Deutsch (1981a) “Variance of the Sample-Time Autocorrelation Function of Contemporaneously Correlated Variables”, SIAM Journal of Applied Mathematics, Series A, 40(1), 133-136.
• Pfeifer, P.E. and, S.J. Deutsch (1981b) “Seasonal Space-Time ARIMA Modeling”, Geographical Analysis, 13(2), 117-133.
• Pfeifer, P.E. and, S.J. Deutsch (1981c) “Space-Time ARMA Modeling with Contemporaneously Correlated Innovations”, Technometrics, 23(4), 410-409.
• Pretorius, C.J. and T.N. Janse van Rensburg (1996) “The Forecast Performance of Alternative Models of Inflation”, South African Reserve Bank, Occasional Paper, No: 10.
• Razzak, W.A. (1997) “Testing the Rationality of the National Bank of New Zealand’s Survey Data”, Bank of New Zealand Working Paper, No: G97/5. http://www.rbnz.govt.nz/research/discusspapers/g97_5.pdf
• Rogers, J.H., G.C. Hufbouer and E. Wada (2001) “Price Level Convergence and Inflation in Europe”, Institute for International Economics Working Papers, No: 01-1, Washington. http://www.iie.com-/publications/wp/01-1.pdf
• Sevüktekin, M. and M. Nargeleçekenler (2005) Zaman Serileri Analizi, Ankara: Nobel.
• Siklos, P.L. and M.E. Wohar (1997) “Convergence in Interest Rates and Inflation Rates Across Countries and Over Time”, Review of International Economics, 5(1), 129-141.
• Stoffer, D.S. (1986) “Estimation and Interpretation of Space-Time ARMAX Models in the Presence of Missing Data”, Journal of the American Statistical Association, 81(395), 762-772.
• Stovicek, K. (2007) “Forecasting with ARMA Models: The Case of Slovenian Inflation”, Banka Slovenije Prikazi in Analize, XIV(1), May, 23-55.
• Tunay, K.B. (2008) “Türk Bankacılık Sektöründe Mevduatların ve Kredilerin Dinamik Uzay-Zaman Panel Veri Yöntemiyle Modellenmesi ve Tahmini”, Bankacılar Dergisi, Sayı 64, 3-26.
• Tunay, K.B. and A.M. Silpagar (2007) “Dinamik Mekan-Zaman Panel Veri Modelleriyle Türkiye’de Bölgesel Enflasyon Yakınsamasının Analizi”, Gazi Üniversitesi İ.İ.B.F. Dergisi, 9(1), 1-25.
• Tunay, K.B. (2008) “Türk Bankacılık Sektöründe Mevduatların ve Kredilerin Dinamik Uzay-Zaman Panel Veri Yöntemiyle Modellenmesi ve Tahmini”, Bankacılar Dergisi, Sayı: 64, 3-26.
• Vaona, A. and G. Acardi (2007) “Regional Inflation Persistence: Evidence from Italy”, University of Pavia, Departments of Economics Working Papers, No: 192(02-07).
• Valle, H.A. (2002) “Inflation Forecasts with ARIMA and Vector Autoregressive Models in Guatemala”, Economic Research Department Banco de Guatemala, Working Papers, May-2002.
• Weber, A.A. (2004) “European Inflation Dynamics and Inflation Convergence”, Open Macro Models and Policy in the Development of European Economy, Conference at the European University Institute, 15 October, Florence. http://www.bundesbank.de/download/presse/reden /20041015weber.pdf
• Zhou, M. and J. Buongiorno (2006) “Space-Time Modeling of Timber Prices”, Journal of Agricultural and Resource Economics, 31(1), 40-56.