A vector autoregressive approach on investigation the relationship of industrial employment, domestic product growth in the Philippines / by Jessica Joyce R. Caspillan and Katrina B. Sequiña.

Thesis (Bachelor of Science in Applied Mathematics) Cavite State University.

Includes bibliographical references.

College of Arts and Sciences (CAS), Department of Physical Science

CASPILLAN, JESSICA JOYCE R. and SEQUINA, KATRINA B. A Vector Autoregressive Approach on Investigating the Relationship of Industrial Employment, Salaries and Wages, and Real Gross Domestic Product Growth in the Philippines. Undergraduate Thesis. Bachelor of Science in Applied Mathematics with specialization in Statistics. Cavite State University, Indang, Cavite. May 2019. Adviser: Prof. Jennifer R. Mojica.
The industry, as a part of the three main sectors of our country, plays an important role in the Gross Domestic Product (GDP) growth of the Philippines. There are three top growing industries in our country according to Philippine Statistics Authority (PSA), namely: construction, manufacturing, and tourism. Countries that have strong industrial sector showed improvement in national income and promoted a high living standard. This study determined the relationship between industrial sector and GDP growth. This study specifically aimed to model and estimate the relationships between industrial employment, salaries and wages, and real GDP using Vector Autoregressive Approach (VAR); investigate the cause and effect relationship between industrial employment, salaries and wages, and real gross domestic product growth; determine how each variable will respond over time to a shock in its own value and every other variable; and compare the forecasted values to the actual values performance over five years.
The quantitative data used in this study are secondary data gathered from PSA.
The data from 2005 to 2015 include industrial total employment, industry wages and real GDP were on quarter basis. This paper describes the Vector Autoregressive Model that is used extensively in econometrics in analyzing multivariate time series. It has a superior forecast ability compared to those univariate time series models. The VAR model has been proven to be useful especially for describing the dynamic behavior of economic and financial time series.
The results show the unidirectional causality between employment rate and GDP growth while the rest have no causality at all. The findings imply that employment does granger cause GDP growth. For the variance decomposition analysis, first, it showed that GDP slightly decreased, and its variance is mostly explained by employment. Second, the
88.89 percent variance of employment is explained by the 6.69 percent in GDP. Lastly, the variance in the salary and wages is explained mostly by the employment rates. In comparing the forecasted values from the actual values, stochastic was used in order to measure uncertainty by adding error bounds confidence intervals to the projections. The results then exhibited a model that is acceptable since the actual values are within the confidence bounds.
Having learned the relationship, the government and economists could determine the future status of the GDP of the country since based on the results, employment does affect GDP. The government should pave a way in giving opportunities and jobs to the Filipinos. In this way, it can improve the growth of the GDP and it can help improve the way of living of the people.

Submitted to the University Library 09/23/2019 T-8637