Forecasting Unemployment Rates in the Ilocos Region, Philippines, Using Time Series Analysis

Authors

DOI:

https://doi.org/10.11594/

Keywords:

Time Series Analysis, Ilocos Region, Unemployment Rate

Abstract

Unemployment rates in the Ilocos Region are continuously progressing alongside the data-driven policymaking and strategies for the economy. This study presents a forecasting model that aims to forecast unemployment trends and propose measures to create more job opportunities, enhance workforce skills, and recommend strategies to reduce unemployment in the Ilocos Region. Utilizing time series analysis across the five different models, the ARIMA (1,1,1) model was identified as the most suitable in forecasting unemployment rates over time. Results also indicate that this approach can informed can be made effectively on the unemployment issues. This research helps Filipino economists, encouraging them to come up with new implementing strategies and interventions to enhance economic well-being in the Ilocos Region and the Philippines.

Downloads

Download data is not yet available.

References

Bevans, R. (2023) Akai Information Criteri-on/When and How to Use It. https://www.scribbr.com/statistics/akaike-information-criterion/

Brooks, R. (2002) Why is unemployment high in the Philippines? International Mone-tary Fund. https://www.imf.org/en/Publications/WP/Issues/2016/12/30/Why-is-Unemployment-High-in-the-Philippines-15591

Boateg, N. (2018) Building Arima Models for Forecasting, Time Series Analysis Meth-ods. https://rpubs.com/mr148/303786.

Dabbla-Norris E. (2015) Poverty rates by Re-gions Causes and Consequences of In-come Inequality: A Global Perspective. https://www.imf.org/external/pubs/ft/sdn/2015/, (2015), pp. 15-16

International Labor Organization (2020) Initial labor markets impacts of COVID-19, https://www.ilo.org/wcmsp5/groups/public/---asia/---ro-bangkok/

Dabbla-Norris E. (2015) Poverty rates by Re-gions Causes and Consequences of In-come Inequality: A Global Perspective. https://www.imf.org/external/pubs/ft/sdn/2015/, (2015), pp. 15-16

National Economic Development Authority (2023). Neda Reiterates Gov’t Focus on High-Quality Job Creation as Unemploy-ment Rate Further Declines. https://neda.gov.ph/neda-reiterates-govt-focus-on-high-quality-job-creation-as-unemployment-rate

Nau, C. (2024) ARIMA models for time series. https://people.duke.edu/~rnau/411arim.htm. Accessed 18 April 2024.

Nau, R. (2020) Statistical Forecasting: Identify the numbers of AR or MA terms in an ARIMA model. https://people.duke.edu/~rnau//411arim3.htm.

Narvasa, D. (2024) An Application of the First –Order Linear Ordinary Differential Equation to Regression Modeling of Un-employment Rates, Journal of Interdisci-plinary Perspectives. https://www.jippublication.com/an-application-of-the-first-order-linear-ordinary-differential-equation-to-regression-modeling-of-unemployment-rates

Pecardo, E. (2024) Employment vs Unem-ployment How The Unemployment Rate Affect Everybody? https://www.investopedia.com/articles/economics/10/unemployment-rate-get-real

Philippine News Agency (2022) Employment rate up 94% in Ilocos. https://www.pna.gov.ph/articles/1178217

Philippine Statistics Authority (2024) Labor force survey. https://psa.gov.ph/statistics/

Sharma, V. (2021) Application of geographic information system and remote sensing in heavy metal assessment. Science Di-rect. https://www.sciencedirect.com/topics/earth-and-planetary-sciences/autocorrelation

Swamidass, P. (2024) Mean Absolute Percent-age Error (MAPE) https://link.springer.com/referenceworkentry/10.1007/1-4020-0612-8_580%2

Tyagi, S. (2024) “Introduction to time series forecasting” https://towardsdatascience.com/introduction-to-time-series

Vandekerckhove, J (2015) Model comparison and the principle of parsimony. https://psycnet.apa.org/record/2015-10090-014

World Bank in the Philippines (2024) Over-view domestic growth is strong in the Philippines, while global challenges are affecting prospects https://www.worldbank.org/

Downloads

Published

2026-01-26

How to Cite

Narvasa, D. Q. (2026). Forecasting Unemployment Rates in the Ilocos Region, Philippines, Using Time Series Analysis . International Journal of Multidisciplinary: Applied Business and Education Research, 7(1), 455-487. https://doi.org/10.11594/