FINC20018 Managerial Finance Paper Editing Services

Introduction:

This project intends to conduct analysis for forecasting inflation and interest rate of two selected countries. For doing so, the report selects Germany and Japan. Inflation rate and nominal interest rate play significant role in bond market, where through inflation rate and discount rate on can calculate real interest rate (Stanley, Doucouliagos & Steel, 2018). For instance, if it is predicted that if inflation will remain at 6 percent per annum for the coming years then bondholders will require a real interest rate for obtaining a risk free situation (Hyndman & Athanasopoulos, 2018). Hence, this report is going describe about the methodology regarding research methodology with the help of which it intends to forecast the calculation.

Brief description about methodology:

The process of forecasting helps to predict future values related to data. Most of the model regarding forecasting assumes that past value act as proxy for determining the future. For forecasting data, various models can be used like regression, exponential smoothing, composite model and time series. Regression analysis helps to analyse quantitative data for estimating model (Bolin, 2014). In this statistical tool, parameters conduct forecast with suitable methodology. In this methodology, y-axis considers dependent variable while x-axis measures dependent variable.

This report takes real data of interest rate and inflation rate of both Germany and Japan since 2013 to 2018. Based on these data, the report has further forecasted predicted rate of inflation and interest rate of these countries for the next 10 years through using regression analysis (Kaytez, Taplamacioglu, Cam & Hardalac, 2015). In this analysis, year is taken as depended variable while inflation rate and interest rate are taken as independent variables.

Presentation regarding real data collection:

The collected real data are:

Data analysis:

With the help of above-mentioned data, the report has conducted regression analysis, with the help of data analysis tool of excel from where following tables of statistical analysis are obtained.

Regression analysis for interest rate of Germany:

In this table, intercept coefficient (a) = 187.2975 and coefficient of year (b) = - 0.09286

Regression analysis for interest rate of Japan:

In this table, intercept coefficient (a) = 48.332 and coefficient of year (b) = - 0.024

Regression analysis for inflation rate of Japan:

In this table, intercept coefficient (a) = 53.45133 and coefficient of year (b) = - 0.0.26

Regression analysis for inflation rate of Germany:

In this table, intercept coefficient (a) = -269.603 and coefficient of year (b) = 0.134286

From each analysis, coefficients of intercept term along with year are taken to plot the regression equation in the form of:

Where, a represents intercept coefficient and b represents coefficient of depended variables (Linoff, 2015). Plotting the data in this diagram, the report has calculated a trend line to obtain future values of these macroeconomic indicators.

The following table shows future forecasted values:

Interpretation of statistical analysis:

The above table has provided the forecasting value from where some conclusions can be drawn. In future, interest rate of Germany may decrease further, as the data represents a negative value. This value is going to reduce over the year indicating an inverse regression line that have negative slope (Tradingeconomics.com, 2018). This situation is also true for the future interest of Japan, where interest rate will decrease over the next 10 years representing a downward sloping curve (Gonçalves et al., 2018). Inflation rate of Japan also shows a decreasing trend though it will not experience negative inflation or deflation for the next ten years. On the contrary, inflation rate of Germany will increase further for the same period. However, this rate will remain below 3 percent (Ghinea et al., 2016). Hence, slow growth rate of Germany’s inflation can help the economic condition of this country further by developing its overall price level.

Conclusion:

In conclusion, it can be said that the report has intended to observe future value of inflation rate and interest rate of Germany and Japan with the help of regression analysis for the next f10 years. For doing so, the report has taken real value of these two indicators for the last five years since 2013 to 2018. BY conducting regression, the report has obtained the future trend of these two variables for these two countries. In this situation, statistical analysis is conducted by selecting regression method.

References:

1. Bolin, J. H. (2014). Hayes, Andrew F.(2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression?Based Approach. New York, NY: The Guilford Press. Journal of Educational Measurement, 51(3), 335-337.
2. Germany - Economic Indicators . (2018). Tradingeconomics.com. Retrieved 27 September 2018, from https://tradingeconomics.com/germany/indicators
3. Ghinea, C., Dr?goi, E.N., Com?ni??, E.D., Gavrilescu, M., Câmpean, T., Curteanu, S. and Gavrilescu, M., 2016. Forecasting municipal solid waste generation using prognostic tools and regression analysis. Journal ofenvironmental management, 182, pp.80-93.
4. Gonçalves, F., Pereira, R., Ferreira, J., Vasconcelos, J. B., Melo, F., & Velez, I. (2018). Emergency waiting times data analysis. Emergency waiting times data analysis, (3), 494-499.
5. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
6. Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems, 67, 431-438.
7. Linoff, G. S. (2015). Data analysis using SQL and Excel. John Wiley & Sons.
8. Stanley, T. D., Doucouliagos, H., & Steel, P. (2018). DOES ICT GENERATE ECONOMIC GROWTH? A META?REGRESSION ANALYSIS. Journal of Economic Surveys, 32(3), 705-726.