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FORECASTING AND BUSINESS ANALYSIS Essay Example
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Forecasting and business analysis 6
FORECASTING AND BUSINESS ANALYSIS
Abstract
The research shows findings after carrying out an econometric analysis of foreign exchange rates and arrivals forecast of tourism arrivals for the remaining months in 2016 assuming that the exchange rates remain the same as that of July 2016 as discussed in the research below. The research also contains findings after the research and relevant limitations of the analysis. It also contains analysis of foreign exchange of the international tourism arrivals to Australia
Keywords: unit, series, data.
Introduction
Econometric analysis helps analysis help come up data that can be used to forecast and analysis data of foreign exchange rates on international tourism arrivals. This data can be used to make a prediction of the exchange rate in the near future. Studying the exchange rates of tourism arrivals may be difficult since sophisticated criteria’s may be difficult to follow and come up with correct results hence analyzing wrong findings which might not be effective to the company hence producing wrong figures in the forecast (Baltagi, 2008)
The advantage of analyzing it helps predict the future thus employing more effectiveness for the company since the analysis done can help the company plan for the future. The company is also able to avoid any losses since their analysis is more effective to make sure that the company receives profits hence working towards maximizing their profits. The findings are used to enhance smooth running of the business since their able to use data to the advantage of the company hence planning is effective hence an advantage to the company. Therefore the findings can help the company come up with policies to run the business effectively (Wooldridge, 2010).
Data analysis
After analyzing the following data was obtained.
Descriptive Statistics 

Std. Deviation 

foreign exchange 
.870985821 
.1187944773 

Arrivals 
510428.3582 
65953.73591 
Correlations 

foreign exchange 
Arrivals 

Pearson Correlation 
foreign exchange 

Arrivals 

Sig. (1tailed) 
foreign exchange 

Arrivals 

foreign exchange 

Arrivals 
Variables Entered/Removed^{a} 

Variables Entered 
Variables Removed 

Arrivals^{b} 

a. Dependent Variable: foreign exchange 

b. All requested variables entered. 
Model Summary^{b} 

Adjusted R Square 
Std. Error of the Estimate 
Change Statistics 

R Square Change 
Sig. F Change 

.1176806321 

a. Predictors: (Constant), Arrivals 

b. Dependent Variable: foreign exchange 
Sum of Squares 
Mean Square 

Regression 

Residual 

a. Dependent Variable: foreign exchange 

b. Predictors: (Constant), Arrivals 
Coefficients^{a} 

Unstandardized Coefficients 
Standardized Coefficients 
Correlations 

Std. Error 
Zeroorder 

(Constant) 

Arrivals 
2.907E007 

a. Dependent Variable: foreign exchange 
Residuals Statistics^{a} 

Std. Deviation 

Predicted Value 
.817713976 
.892736316 
.870985821 
.0191708757 

Residual 
.2467562556 
.2177786231 
.1172373889 

Std. Predicted Value 

Std. Residual 

a. Dependent Variable: foreign exchange 
Foreign exchange data from the airport the general exchange rate for July 2005 to July 2016 the average exchange rate is 0.7637. But it seems that there is more exchange between August and December this is because of it towards the end of the year and traveling seems to be more. This is because of the holidays in between, and many people prefer to celebrate outside hence the increase in exchange rates. The increase in the exchange rate is also due to the winter breaks, and people prefer to go to countries with summer thus the exchange rate is experienced during this time (Paolella & Taschini, 2008).
Arrivals seem to be more in the Airport from September to December this is because tourist from other countries visits the country towards the end of the year hence the high number of arrivals during this period. But in 2016 arrivals seem to have increased over the previous years from March 2015 arrivals have greatly increased (Hsiao, 2014).
Modeling and analysis
The model can be identified from the analysis. This is because some months have consistency on the arrivals over several years. Also, the airport experiences a season that arrivals are high or generally standard this depends on the season example: in winter traveling seems to be minimal since people prefer to stay indoors due to the cold weather hence the model of this months during winter seem to be consistent (Baltagi, 2008).
Foreign exchange seems to have increased in 2012 this may be caused by several reasons example: the exchange rates may have been in favor of in 2012 hence the high exchange rates during this year. The exchange rates have greatly dropped in 2015 despite been high in 2012. The low exchange rates could have caused this during the year affecting the exchange rates in 2015. However, their expected to rise in 2016 and the near future since they have recorded an increase in the exchange rate in 2016 since march hence their expected to behave normally during this years. The analysis also show that arrivals have been increasing since 2014 and are expected to continue increasing all over 2016 and in the future. This is because the exchange rates seem to be fair and cheaper compared to the previous years hence expecting that the arrivals will continue increasing thus the airline will create more profits (Barndorff‐Nielsen, 2002).
Findings
The findings are expected to help the airline increase sales since their able to know what is expected of them hence planning for the future. It is expected that arrivals will continue increasing this is due to the low exchange rates hence more profit to the airline. The airline is expected to maintain its relation thus continuing increasing their sales. Furthermore, the airline is also expected to continue with the same managerial skills that they have been using since the airline has made more profits lately due to the huge number of arrivals. The airline should not change their marketing skills since they have yielded more profits to the airline (Baltagi, 2008).
The foreign exchange rates are greatly favoring the airlines since their able to get more arrivals compared to the previous years this is because the exchange rates are favoring the performance of the airline hence more profits. The exchange rates seem to be fair thus people will prefer to travel via air hence more profits to the airline.
Conclusion
The findings are to be used to enhance the smooth running of the company hence will help the airline to avoid making losses and know what is expected of them. The limitations of the findings would be if the airline management relaxes when they find out that they are to expect more arrivals in the near future they can relax due to the expectation derived from the findings thus making losses. The findings should also not be relayed on since they can be incorrect an exaggerated thus the airline should only use the findings as a guideline to how they are expected to run the airlines to expect more profits. The analysis may also misguide the airline since they may prove that the airline needs to work on their exchange rates other than arrivals hence misguiding them and they may end up receiving losses in the future other than the profits they have been expecting (Wooldridge, 2010).
References
Baltagi, B., 2008. Econometric analysis of panel data. John Wiley & Sons.
Barndorff‐Nielsen, O.E., 2002. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Roya l Statistical Society: Series B (Statistical Methodology), 64(2), pp.253280.
Hsiao, C., 2014. Analysis of panel data (No. 54). Cambridge university press.
Paolella, M.S. and Taschini, L., 2008. An econometric analysis of emission allowance prices. Journal of Banking & Finance, 32(10), pp.20222032.
Wooldridge, J.M., 2010. Econometric analysis of cross section and panel data. MIT press.