ASSIGNMENT SAMPLE ON BUSINESS STATISTICS AND FORESCATING MODULE
1.0 Introduction
Data from the entire subset where a wide range of up-and-coming indicators are being considered, hypothetical techniques are becoming inexorably well-known equipment for studying predictive capacity and variable importance. Given a reaction variable of interest and a starting sum summed up as an additional substance (blended) model fit, we show how to create, fit, and think about a comprehensive model arrangement of possible natural or ecological indicators using the quantifiable programming language R. The primary advantages include not requiring a complete model to be fit as the starting point for developing new model sets (implying that a greater number of indicators could potentially be investigated than might be possible with capacities like dig); model sets that include factor communications and consistent nonlinear indicators; and scheduled evacuation of models with related indicators (in light of a client characterized model for rejection) (Rao et al. 2022). The taken data source is defined as mainly 6 types of variable in which 5 of the variables are mainly nominal variables which are mentioned as the years and the number of donors and the last one is named.
1.1 Aim and objective
The main aim of the project is to define the forecast for Argentina’s GDP by the linear regression method. The objective of this paper has been set using SMART framework. There are total five elements of the framework those are “Specific, Measurable, Achievable, Realistic and time bounded”
The objective of the study is,
- To evaluate the taken data set by using E-views software.
- To observed the impact over the last few years of the Brazilian market on Argentina economy.
- To evaluate crucial factors that increases Argentinean GDP in last year.
2.0 Methodology
The methodology of the research mainly describes the process of the research and also describes the action taken to complete the research. The methodology section mainly gives a broad overview of the data set and also the process taken to get the result from the dataset this section is also described as the model taken for completing action and implementation of the model in the analytic software to get a result from that (Gorji and Gorji, 2018). This section has also described the technique and software used to conduct the study. This section has also described the variables used in the study.
The used model is mainly the multiple time series regression models. This type of time series model has more than one time-dependent variable. In this type of model, the used variables are dependent on the past values of the variable not only that it also depends on the other variables used in the data set. This type of dependency can help to predict or forecast the future very well, and also help to recommend the important factors that need to be changed in the future to grow more. The time-series regression model is mainly helping to understand the dynamic systems and also predict the behavior from the observational data that used in the report.
In this data set, the used variables are mainly numeric or nominal variables. Those variables are mainly the divided data sources from some parts. There is mainly 68 observations are taken to conduct the data (Montgomery et al. 2021). The data sets are mainly taken from the given websites of data. In the dataset there is mainly two types of variable one is dependent and another is independent. Dependent variables are the estimated values of GDP in the countries and the independent variable is the year’s number.
Firstly have to upload or import the whole data set on the E-views analytics software then the uploaded page shows the number of observations taken for the variables. In that portion, Brazil’s GDP is used as an explanatory variable. Argentina and Brazil are the close trending partners so if Brazil’s economy grows more than they are mainly importing more goods from Argentina. For that, Argentina’s GDP is growing to increase as well. Then go to the quick option in the software and click on the equation estimation to write up the variables in the report (Shrestha, 2020). This report is mainly based on the lo log regression so the variables write like a format which is given below,
Log (Argentina) c log (Brazil)
The method is used to conduct the report is the least-squares and the sample length is 1950 to 2017. Then clicking on the OK button can give the estimated state of the whole dataset.
3.0 Finding and Discussion
The findings and discussion part is mainly the findings of the report and also meet them with the objective of the report and the discussion section discusses those findings. In this report, the findings of the report are mainly described in these 3 figures where the relation is being discussed in figure number one. [Refers to appendix 1]
Figure one mainly describes the summary of the tasks taken to conduct the report. The top of the statistical table describes the dependent variable as the log (Argentina), Method used in it is the least-squares method, the sampling frequency is 1950 to 2017 and also the number of observations is 68 (Valaskova et al. 2018). From the second step of the table, the other variables constant and also the log (Brazil) are being discussed. Where the value of the coefficient of the constant is 5.84 and also the real GDP of Brazil’s coefficient value is 0.49. So from that, we can discuss that if Brazil’s GDP increases 1% then the GDP of Argentina is increased 0.49%. Standard errors are very small in amount so that’s why it confirms that the analysis is so much accurate. The p-value is also less than .05 so it confirms that Brazil’s GDP is helping o grow the GDP of Argentina (Yuan et al. 2020). If the r square value is closer to one (in this case it is 0.93) then Brazil’s GDP is helping to explain the GDP of Argentina.
From the figure 2, it is confirmed that Argentina’s GDP is increasing since 2005 and in a good manner (Hünermund and Louw, 2020). But there is some drawback is happened in 1990 in that time the GDP of Argentina goes very much lower than the other years.
Figure three also describes the graph of Argentina’s residual in this graph the GDP of Argentina describe in a very simple manner and this confirms the point of growth, and also from this graph the software can predict or forecasting the GDP of Argentina.
4.0 Forecasting
The for forecasting or the prediction are the same thing from uploading and using the data set in that E-views analytics software can give the proper output and also give one of the accurate forecasting’s because all of the values need for the time regression model is accurate so the forecasting also is an accurate one (Ma et al. 2018). The graph was created in the forecasting section of the software.
From the graph and the given data, it can confirm that the taken observation number is 68. The root mean squared error is 58343.29, the mean absolute error is 40776.23, the mean absolute percentage error is 9.468077, the bias proportion is 0.008717 and also the variance proportion is 0.234632 and the last one covariance proportion is 0.756652. From the whole graph, it shows that the graph is mainly going in an upward direction, so it is forecasting that the GDP of Argentina will increase in the future (Hidayat and Sadewa, 2020). This graph is also said that the increase of Brazil’s GDP and also the increase of the import goods are parallel to each other so the increase of the Brazil GDP can help to increase the GDP of Argentina.
5.0 SWOT analysis and PESTLE analysis
| Strength
· Increasing international capital in the market. · The inflow of USD is increasing in the market |
Weakness
· CAPEX is increasing in the local market · Number of challenges is increasing for FDI’s |
| Threats
· The economy took a severe hit in the recent times · The unstable political situation. |
Opportunity
· New business law has been introduced. · Massive infrastructure will bring more investment in the market |
The PESTLE analysis will enable the stakeholder’s to indentify the involvement of the external element. Based on the analysis it will help the stakeholders to make more effective decision, there are six external element’s which will allows the stakeholder’s to understand the overall impact of the environment on the market or the organization.
Political:
The Argentinean market has heavily impacted due to the instability in political power. The country has mixed political system socialism and communism approach.
Economical:
The country has become poor to poorer according to the stat, in 2017 the 27.1% of the people were in the below poverty line in 2018 the 32% of the people were in poverty line. The growing inflation is another reason.
Social:
Unemployment, social inequality among the classes is one of the big reasons for current situation of the market.
Technological:
Slowly and steadily the country is making progress in the technological division and the country has an advanced medical setup and along with that, the country has a massive real-estate opportunity which is a lucrative offer for future investor.
Legal
The country has made various legal reforms recently to form more friendly for the businesses and additionally, the country has taken an initiative create more FDI friendly legislation in their constitution.
Environmental
Currently the country is focusing on the green technology; the country is committed to transform the energy consumption habit. Country is heavily invested in renewable energy.
6.0 Recommendations and conclusion
The recommendation is taken for the report is the increase of the import of the goods and also the other products which are needed to Brazil can increase the economy of Argentina. Using the time regression technique we can give a more accurate forecast for the country’s GDP. The introduction is mainly discussed the used model and also the section describes the variables used in this report. The introduction section also describes the aim and objectives of the report. The methodology section firstly describes the data set if it is time-series data or non-time series data and also gives some further information about the model used in this report. This section also describes the number of variables used in the data set and also describes the taken data frequency has which format. The third section of the methodology describes the number of observations and also how to observe the whole data source by using the E-views analytics software. This section is also described the pre-estimation diagnostics of the chosen dataset. Findings and also the discussion section mainly outline the findings of the report and also outline the result we get from the data set. This section also describes the whole findings and also focused on the important data. The recommendation section mainly creates a visual recommendation from the report. Taken data are very much accurate and also the taken data sources are very much reliable. So this report is very much acceptable for the data regression model technique.
Reference
Journal
Rao, P.S., Varma, G.P. and Prasad, C., 2022. PSO-WT-Based Regression Model for Time Series Forecasting. In Applied Information Processing Systems (pp. 227-233). Springer, Singapore.
Gorji, A. and Gorji, M., CAUSALITY ANALYSIS IN CLIMATE TIME SERIES USING WINDOWED REGRESSION.
Montgomery, D.C., Peck, E.A. and Vining, G.G., 2021. Introduction to linear regression analysis. John Wiley & Sons.
Shrestha, N., 2020. Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2), pp.39-42.
Valaskova, K., Kliestik, T. and Kovacova, M., 2018. Management of financial risks in Slovak enterprises using regression analysis. Oeconomia Copernicana, 9(1), pp.105-121.
Yuan, Y., Wang, M., Zhu, Y., Huang, X. and Xiong, X., 2020. Urbanization’s effects on the urban-rural income gap in China: A meta-regression analysis. Land Use Policy, 99, p.104995.
Hünermund, P. and Louw, B., 2020. On the nuisance of control variables in regression analysis. arXiv preprint arXiv:2005.10314.
Ma, L., Hu, C., Lin, R. and Han, Y., 2018, December. ARIMA model forecast based on EViews software. In IOP Conference Series: Earth and Environmental Science (Vol. 208, No. 1, p. 012017). IOP Publishing.
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