Forecasting GDP Of British Columbia Using ARIMA Model

Research Objectives

The forecasting is used to provide the great ways for current, quantitative, economic and statistical strategies for evaluating and producing the forecasts. Forecasts are settled on to direct decisions in a variety of fields. Gross Domestic Product (GDP) of a nation is the cash estimation of all goods and administrations created by every one of the enterprises inside the bounds of a nation in a year. The execution of economy can be forecast with the assistance of GDP. This paper aims to displaying and determining forecasting GDP of British Columbia utilizing ARIMA model. Here analyzed by time series method. Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) are will calculated. Proper Box-Jenkins Auto Regressive Integrated Moving Average (ARIMA) demonstrates the forecasting the future GDP of British Columbia. Legitimacy of the model was tested using standard statistical techniques. And, ARIMA model is used to show were utilized to forecasting zone and creation of British Columbia for future years.

1. To test the stationary in the data of GDP using the Augmented Dickey – Fullers unit root test over the period.
2. To study Auto correlation in the observed series of GDP ACF and PACF values and correlogram will be used to measure the AR and MA to predict which past series is a fitter model for future value prediction.
3. To test the model validity statically a portmanteau test of Independence i.e. the BDS test for time-based dependence in a series will be applied.
4. Finally Forecast the GDP for the next ten years using ARIMA Model along with the upper control level (UCL) and the lower control level (LCL).

Literature Review

According to this paper (Dritsaki, 2015), the ARIMA model has be used extensively by many researchers. This method is used to highlight the future rates of GDP. It easily examines the forecasting of GDP growth rate for India using the ARIMA model. This model is used to predicted the values follow an increasing the trend for a years. It establishes the stationary of time series. Result of this paper is used to provide the policy makers to formulate the effective policies for attracting the foreign direct investment. It also helpful the managerial business executive for implementing or taking decision concerned with the expansion of the existing business.

The GDP data was collected over the time period from 1997 to 2017 were used for forecasting the future values using Auto Regressive Integrated Moving Average (ARIMA) models. The ARIMA procedure is likewise called as Box-Jenkins approach. The Box-Jenkins method is worried about fitting an ARIMA model to a given data of information. The objective in fitting ARIMA model is to distinguish the stochastic procedure of the time series and predicted the future values correctly. These strategies have additionally been helpful in numerous sorts of circumstances which include the working of models for discrete time series and dynamic frameworks. Anyway this technique is not useful for seasonal series with a large random component.

The GDP data was collected over the time period from 1997 to 2017. The provided data was contains the information about the forecast the GDP growth for the province of British Columbia based on the overall annual expenditure. It is illustrated as below (Camacho & Martinez-Martin, 2013).

The testing of Stationarity is represent the GDP rate series and it conclude that coefficients of autocorrelation (ACF) starts with a high value and declines slowly, indicating that the series is stationary.  Thus, the series must be configured in first differences (Chun-Chu, 2011).ACF for Reference period 

ACF for Gross domestic product at market prices in Dollars  

PACF for Reference period

Subsequent to making the GDP series stationary the autocorrelation and partial autocorrelation function were utilized. By examining the PACF values and correlogram term AR and MA was observed to be fit for forecasts. The ARMA parameters were distinguished utilizing Autocorrelation and Partial Autocorrelation Functions. It is illustrated as below (Fildes & Allen, 2011).

Model validity is illustrated as below.

Final GDP Forecast values for Forecasting the future GDP of British Columbia is illustrated as below (?en Do?an & Midiliç, 2018).

Conclusions

This project was successfully predicted, displayed the GDP forecast of British Columbia future utilizing by using the ARIMA model. The predicted value analyzed by time series method. The Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) are effectively calculated. Used the Proper Box-Jenkins Auto Regressive Integrated Moving Average (ARIMA) successfully demonstrated the forecasting the future GDP of British Columbia. Legitimacy of the model was effectively tested by using standard statistical techniques. And, ARIMA model is used to display the forecasting zone and creation of British Columbia for future years.

References

Camacho, M., & Martinez-Martin, J. (2013). Real-time forecasting US GDP from small-scale factor models. Empirical Economics, 47(1), 347-364. doi: 10.1007/s00181-013-0731-4

Chun-Chu. (2011). Forecasting the Spanish Stock Market Returns with Fractional and Non-Fractional Models. American Journal Of Economics And Business Administration, 3(4), 586-588. doi: 10.3844/ajebasp.2011.586.588

Dritsaki, D. (2015). Forecasting Real GDP Rate through Econometric Models: An Empirical Study from Greece. Journal Of International Business And Economics, 3(1). doi: 10.15640/jibe.v3n1a2

Fildes, R., & Allen, P. (2011). Forecasting. Los Angeles, Calif.: SAGE.

?en Do?an, B., & Midiliç, M. (2018). Forecasting Turkish real GDP growth in a data-rich environment. Empirical Economics. doi: 10.1007/s00181-017-1357-8

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