The Superiority of Panel SPSM-KSS Fourier Univariate Unit Root Test towards Problematic PFI Time Series Data

AUTHOR(S):

Saadi Bin Ahmad Kamaruddin, Nor Azura Md Ghani, Norazan Mohamed Ramli

TITLE

The Superiority of Panel SPSM-KSS Fourier Univariate Unit Root Test towards Problematic PFI Time Series Data

ABSTRACT

This paper aims at investigating the properties of problematic time series data with outliers and missing values problems by applying the Sequential Panel Selection Method (SPSM) using Panel KSS unit root test with a Fourier Function. The problematic time series data refer to the real-industrial-data which comprise of the Malaysian construction materials price indices monthly data from January 1980 to December 2013, with base 100 in year 1980 covering four states of Malaysian Peninsular central region; Wilayah Persekutuan Kuala Lumpur, Selangor, Melaka and Negeri Sembilan. The method used in this study is powerful to classify the whole panel using structural breaks as well as nonlinearity control, and determines which series in the panel are stationary processes. The empirical results found that the series of Aggregates, Sand and Roof Materials price indices are all stationary even though there exist different severity of outliers problem and interpolated missing values in the data. The missing values interpolation techniques with respect to this study are nearest neighbor, linear, piecewise cubic spline, shape-preserving piecewise cubic, and their significance based on bootstrap p-values are also shown in this paper. This initial test is important to be considered before any further attempts of time series or forecast modeling can be implemented on the data. The findings are important for the policy makers, contractors as well as subcontractors to further forecast the future prices of construction materials and soon assist them in tender bidding before any agreements on construction projects are made.

KEYWORDS

Outliers, missing values, time series, SPSM, KSS Fourier univariate unit root

REFERENCES

[1] C. Nelson, and C. Plosser, Trends and random walks in macroeconomic time series, Journal of Monetary Economics, 10, 1982, pp. 139–162.

[2] T. Chang, H. –P. Chu, and O. Ranjbar, Are GDP fluctuations transitory or permanent in African countries? Sequential Panel Selection Method, International Review of Economics and Finance, 29, 2014, pp. 380-399.

[3] A. Akintoye, M. Beck, and C. Hardcastle, Public-private partnerships, managing risks and opportunities, Blackwell Science Ltd. Garsington Road, Oxford: United Kingdom, 2003, pp. 123-165.

[4] H. M. Foad, and A. Mulup, Harga siling simen dimansuh 5 Jun, Utusan, Putrajaya, 2nd June, 2008.

[5] S. B. A. Kamaruddin, N. A. M. Ghani and N. M. Ramli, Estimating Construction Materials Price Indices of Private Financial Initiative in Malaysian East Coast Region, Proceedings of the 15th WSEAS International Conference on Mathematical and Computational Methods in Science and Engineering (MACMESE 2013), 2013, pp. 90-97.

[6] S. B. A. Kamaruddin, N. A. M. Ghani, and N. M. Ramli, Best Forecasting Models for Private Financial Initiative Unitary Charges Data of East Coast and Southern Regions in Peninsular Malaysia, International Journal of Economics and Statistics, Vol.2, 2014, pp.119-127.

[7] J. A. Giachino, Current construction market conditions present price challenges to owners and design-builders, Florida Chapter Design-Build Institute of America, 2006.

[8] J. Gallagher and F. Riggs, Material price escalation: Allocating the risks, Construction Briefings, No. 2006-12, December, 2006.

[9] M. Taylor and L. Sarno, The behavior of real exchanges during the post-Bretton Woods period, Journal of International Economics, 46, 1998, pp. 281-312.

[10] J. B. Breuer, R. McNown and M. S. Wallace, Misleading inferences from panel unit-root tests with an illustration from purchasing power parity, Review of International Economics, 9, 2001, pp. 482–493.

[11] A. Levin, C. F. Lin and C. S. Chu, Unit root in panel data: Asymptotic and finite-sample properties, Journal of Econometrics, 108, 2002, pp. 1–24.

[12] M. P. Taylor, Purchasing power parity, Review of International Economics, 11, 2003, pp. 436–452.

[13] A. M. Taylor and M. P. Taylor, The purchasing power parity debate, Journal of Economic Perspectives, 18, 2004, pp. 135– 158.

[14] P. Perron, The great crash, the oil price shock and the unit root hypothesis, Econometrica, 57, 1989, pp.1361–1401.

[15] G.Maddala and I. -M. Kim, Unit roots, cointegration and structural change, UK: Cambridge University Press, 1998.

[16] L. C. Nunes, P. Newbold and C. M. Kuan, Testing for unit roots with breaks: Evidence on the great crash and the unit root hypothesis reconsidered, Oxford Bulletin of Economics and Statistics, 59, 1997, pp. 435– 448.

[17] J. Lee and M. C. Strazicich, Minimum Lagrange multiplier unit root test with two structural breaks, The Review of Economics and Statistics, 85, 2003, pp. 1082–1089.

[18] D. Kim and P. Perron, Unit root tests allowing for a break in the trend function at an unknown time under both the null and alternative hypotheses, Journal of Econometrics, 148, 2009, pp. 1–13.

[19] G. Kapetanios, Y. Shin and A. Snell, Testing for a unit root in the nonlinear STAR framework, Journal of Econometrics, 112, 2003, pp. 359–379.

[20] N. Ucar and T. Omay, Testing for unit root in nonlinear heterogeneous panels. Economics Letters, 104, 2009, pp. 5–8.

[21] K. S. Im, M. H. Pesaran and Y. Shin, Testing for unit roots in heterogeneous panels, Journal of Econometrics, 115, 2003, pp. 53– 74.

[22] R. Becker, W. Enders and J. Lee, A general test for time dependence in parameters, Journal of Applied Econometrics, 19, 2004, pp. 899–906.

[23] W. Enders and J. Lee, A unit root test using a Fourier series to approximate smooth breaks, Oxford Bulletin of Economics and Statistics, 74(4), 2012, pp. 574–599.

[24] R. Pascalau, Unit root tests with smooth breaks: An application to the Nelson–Plosser data set, Applied Economics Letters, 17, 2010, pp. 565–570.

[25] E. F. Putra, R. Kosala and I. Indonesia, Application of artificial neural networks to predict intraday trading signals, Recent Researches in E-Activities, 2011, pp. 174- 179.

[26] G. Chortareas and G. Kapetanios, Getting PPP right: Identifying mean-reverting real exchange rates in panels, Journal of Banking and Finance, 33, 2009, pp. 390–404.

[27] Mathworks, Missing Data – MATLAB and Simulink, available:http://www.mathworks.com/help/m atlab/data_analysis/missing-data.html

[28] P. Hajek and V. Olej, Municipal Revenue Prediction by Ensembles of Neural Networks and Support Vector Machines, WSEAS Transactions on Computers, Issue 11, Vol.9, 2010, pp.1255–1264.

[29] H. Lin and K. Chen, Soft Computing Algorithms in Price of Taiwan Real Estates, WSEAS Transactions on Systems, Issue 10, Vol.10, 2011, pp.342–351.

[30] P. Hajek, Forecasting Stock Market Trend using Prototype Generation Classifiers, WSEAS Transaction on Systems, Issue 12, Volume 11, 2012, pp. 671-680.

[31] P. Hajek and F. Neri, An Introduction to the special Issue on Computational Techniques for Trading Systems, Time Series Forecasting, Stock Market Modeling, Financial Assets Modeling, WSEAS Transactions on Business and Economics, Issue 4, Volume 10, 2013, pp. 291-292.

Cite this paper

Saadi Bin Ahmad Kamaruddin, Nor Azura Md Ghani, Norazan Mohamed Ramli. (2016) The Superiority of Panel SPSM-KSS Fourier Univariate Unit Root Test towards Problematic PFI Time Series Data. International Journal of Economics and Management Systems, 1, 31-38