IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/72648.html
   My bibliography  Save this paper

Simultaneity of Crime Incidence in Mindanao

Author

Listed:
  • Madanlo, Lalaine
  • Murcia, John Vianne
  • Tamayo, Adrian

Abstract

The study simulated the predictive relationships of regional monthly crime rates for a period covering January 2009 to July 2013. A six-equation model representing the six regions in Mindanao was estimated using the seemingly unrelated regression (SUR). The SUR estimation shows that the increase of incidences of crimes in Southern Mindanao Region and SOCCSKSARGEN tended a 1.73% rise and 0.85% reduction in crime incidences in Zamboanga Peninsula. Monthly crime rates in Northern Mindanao increases crime rates in Southern Mindanao (1.1%), SOCCSKSARGEN (1.29%), CARAGA (0.22%) and ARMM (0.96%). Southern Mindanao yielded simultaneous increase in crimes with Zamboanga Peninsula (0.21%) and Northern Mindanao (0.36%); yet a drop in crimes in CARAGA (0.08%) and ARMM (0.29%). SOCCSKSARGEN's crime rates rise simultaneously by 0.39% in every percentage increase of crime rates in Northern Mindanao yet plunged by about 0.09% and 0.50% when crimes rise by a notch higher in Zamboanga Peninsula and ARMM. CARAGA posted 0.97% increase and 1.07% decrease of crime rates upon the rise of crime rates Northern Mindanao and Southern Mindanao. Lastly, crime rates in ARMM, on the other hand, tend to increase by 0.63% upon the rise of the same in Northern Mindanao and dipped by 0.58% and 1.09% in simultaneity with Southern Mindanao and SOCCSKSARGEN.

Suggested Citation

  • Madanlo, Lalaine & Murcia, John Vianne & Tamayo, Adrian, 2016. "Simultaneity of Crime Incidence in Mindanao," MPRA Paper 72648, University Library of Munich, Germany, revised 20 Jul 2016.
    • Handle: RePEc:pra:mprapa:72648
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/72648/1/MPRA_paper_72648.PDF
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tamayo, Adrian, 2016. "Determining Statistical Pattern on the Drug-Related Killing in Philippines Using ARIMA and Poisson Techniques," MPRA Paper 72518, University Library of Munich, Germany.
    2. Dickey, David A & Pantula, Sastry G, 1987. "Determining the Ordering of Differencing in Autoregressive Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 455-461, October.
    3. Baumöhl, Eduard & Lyócsa, Štefan, 2009. "Stationarity of time series and the problem of spurious regression," MPRA Paper 27926, University Library of Munich, Germany.
    4. Spanos, Aris, 1990. "The simultaneous-equations model revisited : Statistical adequacy and identification," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 87-105.
    5. Lee Adkins, 2014. "Using gretl for Principles of Econometrics, 4th Edition," Economics Working Paper Series 1412, Oklahoma State University, Department of Economics and Legal Studies in Business.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    2. Keblowski, Piotr & Welfe, Aleksander, 2010. "Estimation of the equilibrium exchange rate: The CHEER approach," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1385-1397, November.
    3. Moritz Cruz & Armando Sánchez‐Vargas, 2022. "Government spending and the exchange rate: Exploring this relationship in Mexico using a cointegrated system of equations," Review of Development Economics, Wiley Blackwell, vol. 26(1), pages 587-605, February.
    4. Urbain, Jean-Pierre, 1995. "Partial versus full system modelling of cointegrated systems an empirical illustration," Journal of Econometrics, Elsevier, vol. 69(1), pages 177-210, September.
    5. Giot, Pierre & Petitjean, Mikael, 2007. "The information content of the Bond-Equity Yield Ratio: Better than a random walk?," International Journal of Forecasting, Elsevier, vol. 23(2), pages 289-305.
    6. Kate Phylaktis & David Blake, 1993. "The fisher hypothesis: Evidence from three high inflation economies," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 129(3), pages 591-599, September.
    7. Arne Kildegaard, 2006. "Fundamentals of real exchange rate determination: What role in the peso crisis?," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 21(1), pages 3-22.
    8. Julien Malizard, 2013. "Opportunity Cost Of Defense: An Evaluation In The Case Of France," Defence and Peace Economics, Taylor & Francis Journals, vol. 24(3), pages 247-259, June.
    9. Chou, W. L. & Shih, Y. C., 1998. "The Equilibrium Exchange Rate of the Chinese Renminbi," Journal of Comparative Economics, Elsevier, vol. 26(1), pages 165-174, March.
    10. Fabio Busetti & Silvia Fabiani & Andrew Harvey, 2006. "Convergence of Prices and Rates of Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(s1), pages 863-877, December.
    11. José Manuel Belbute & Leonardo Dia Massala & Júlio António Delgado, 2016. "Measuring Persistence in Inflation: Evidence For angola," South African Journal of Economics, Economic Society of South Africa, vol. 84(4), pages 594-606, December.
    12. repec:aly:journl:201820 is not listed on IDEAS
    13. Bensalma, Ahmed, 2015. "New Fractional Dickey and Fuller Test," MPRA Paper 65282, University Library of Munich, Germany.
    14. Claude DIEBOLT & Karine PELLIER, 2018. "Patents in the Long Run: Theory, History and Statistics," Working Papers of BETA 2018-20, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    15. Masih, Rumi & Masih, Abul M. M., 2000. "A Reassessment of Long-Run Elasticities of Japanese Import Demand," Journal of Policy Modeling, Elsevier, vol. 22(5), pages 625-639, September.
    16. Spanos, Aris, 2010. "Statistical adequacy and the trustworthiness of empirical evidence: Statistical vs. substantive information," Economic Modelling, Elsevier, vol. 27(6), pages 1436-1452, November.
    17. Nastac, Iulian & Dobrescu, Emilian & Pelinescu, Elena, 2007. "Neuro-Adaptive Model for Financial Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 4(3), pages 19-41, September.
    18. Joanna Olbrys, 2011. "ARCH Effect in Classical Market-Timing Models with Lagged Market Variable: the Case of Polish Market," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 185-202.
    19. Pilar Fernández‐Ferrín & Belén Bande & Aitor Calvo‐Turrientes & M. Mercedes Galán‐Ladero, 2017. "The Choice of Local Food Products by Young Consumers: The Importance of Public and Private Attributes," Agribusiness, John Wiley & Sons, Ltd., vol. 33(1), pages 70-84, January.
    20. Cheng, Fuzhi & Orden, David, 2005. "Exchange rate misalignment and its effects on agricultural producer support estimates," MTID discussion papers 81, International Food Policy Research Institute (IFPRI).
    21. Dolado, Juan J. & Marmol, Francesc, 1997. "On the properties of the Dickey-Pantula test against fractional alternatives," Economics Letters, Elsevier, vol. 57(1), pages 11-16, November.

    More about this item

    Keywords

    panel data; regional crime rates; Mindanao; seemingly unrelated regression; simultaneous effects;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:72648. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.