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How European Monetary Policy Affected their Recovery

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Introduction

The present research is carried out to understand the extent to which monetary policy as a tool of government and central banks is effective in bringing changes in the economic life cycle of the nation. It has been observed most of the time that when economic turmoil comes into existence central banks make changes in their monetary policy in order to bring the economy on track and to control inflation rate (Starr, 2014). But physically any big change in the economic cycle is not observed. Even though most of the time changes are brought in the policy in the market prices of goods do not significantly change. Hence, for one it is very difficult to estimate the extent to which monetary policy is bringing change in the economic cycle of the nation. In this research, every attempt is made to identify and prove that monetary policy is playing a decisive role in bringing changes in the economic cycle of the nation (Armendáriz and Morduch, 2010). In this regard, econometrics is used in the present study and the technique of the Vector auto-regression model is applied to the collected data.

Research

In this research, data on Spain's GDP and interest rate is taken. Change in GDP is the symbol of the economic cycle of the nation and due to this reason, GDP figures are taken into this research. Apart from this central bank monetary policy indicates changes in interest rates by the central bank. Thus, in research relevant figures are taken, and by using both types of data, a Vector auto-regression model is prepared (Habib, 2013). Hence, this research certainly produces good results that will be used to derive useful conclusions.

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Research Type

There are two types of research namely primary and secondary research. Primary research refers to the data that is collected for the first time and is not available in any book, journal, magazine, or website. Whereas, secondary data refers to the data that is already available and collected from sources of information like books, journals, magazines, and websites. It is very important to collect secondary data because by using this data researcher develops a broad understanding of the scenario. By reviewing information related to the past managers can estimate likely changes that currently may happen in the specific variable. On the basis of a good understanding of past conditions, the researcher gets a direction in which he needs to conduct research. Thus, it is very important for one to collect secondary data before conducting research.

In the present research data is collected from various websites and these sites are highly reliable sources of information (Jalles, 2010). Data from 2012 to 2016 is collected from these websites and used for preparing a model for computing Vector auto-regression analysis. This model is very helpful for the researchers because by using this tool managers and researchers identify the relationship between two variables and to what extent changes are observed in the single variable (Descriptive statistics using Excel and Stata, 2016.). Thus, it can be said that this statistical tool not only helps in evaluating a single variable but by using the same we can identify the extent to which one variable depends on another for performance. Hence, it can be said that this research is very helpful in identifying the extent to which the economic cycle is affected by the monetary policy of the government.

Research Approach

In order to conduct research quantitative approach is followed and under this econometric is prepared by the researcher. For preparing these metrics data on Spain's GDP and interest rate is taken and various calculations are done as can be seen in the tables that are attached in the answer file. Lag GDP and interest rate are computed in the table and the difference between the values of the lag column is calculated (Chaudhuri and Banerjee, 2010). This process is further carried out in the case of variables GDP and interest rate of the central bank. After doing all the calculations in the table regression model is applied to raw data and calculated values. Under the regression model values of the X-axis and Y-axis are taken as inputs and on this basis values of various components of the regression analysis table are computed by the researcher. In the present study secondary research is also done and for this literature from various sources of information is reviewed by the researcher. These sources of information were books, journals, magazines, and websites as well as newspapers. On the basis of evaluation of information available in these sources of information literature review is prepared (Yin, 2013). Preparation of a literature review to a large extent helps in understanding past scenarios and the impact of monetary policy on the economic cycle of the nation.

Data Collection Method

In order to collect data various websites are visited and data available on them are reviewed (Krueger and Casey, 2014). It is ensured that data is collected from the perfect website and there is very high reliability of the data that is collected from the specific website. In order to prepare a literature review various sources of information are reviewed. Hence, it can be said that data is collected in an appropriate way to conduct research.

Data Analysis

In order to analyze data in proper way and to get useful results econometrics is used and under this Vector active regression model is used in the report. In order to analyze data many steps were followed and some of these steps are as follows.

Collection of Data and Preliminary Calculations

Data related to GDP and interest rates is collected. Both types of data are arranged in different columns of the Excel sheet. On these individual values, various formulas are applied, and by doing this different sub-calculations are done in the report.

Diff GDP and Interest Rate

This is the second column in the Excel sheet for both variables namely GDP and interest rate. In order to carry out further calculations and to develop an econometric model difference in GDP on a year-on-year basis is computed in the Excel sheet (Silverman, 2013). Similarly for interest rates also difference between interest rates of different years is computed in order to identify the changes in interest rate and GDP each and every year. This calculation indicates that GDP and interest rates increase or decrease and to what extent. In this way, calculation gives giving overview of movement in values of variables.

Lag GDP and Interest Rate

In the third stage of calculation of lag GDP and interest rate is done. Lag GDP refers to the transfer of past year figures into the current year in order to do a better analysis of past figures (Clawson and Knetsch, 2013). In the case of interest rate also lag interest rate is computed and in this case also past year's rate of interest is transferred to the current year for further calculation.

Diff 1 GDP and Interest Rate

In this table differences between lag GDP values are computed. In this way, the most recent changes in the value of GDP are taken into account for further calculation (Corbin and Strauss, 2014). This reflects that this matrix will certainly provide reliable results for the research. For interest also diff 1 interest column is prepared in the table and this helps the researcher in tracking the past few year's changes in the interest rate of the firm.

Diff 2 GDP and Interest Rate

This table is prepared after the column of diff1 GDP and interest. In the case of diff 2 GDP and interest rate in order to track the most recent changes in the values of the variable calculation is done. In this regard past year changes in the variable are taken into consideration for the current year (Campbell and Stanley, 2015). In the case of interest also past year changes that were computed in the previous column are taken into account and relevant years' change in interest rate value is transferred to the current year.

Read the Role of Fiscal & Monterey Policy of the Uk

Trend

Finally, the trend column is prepared in the Excel sheet and under this year sequences 1,2,3,4 and 5 are given to all rows. This thing is done in the case of both variables namely GDP and interest rate.

These are all components of the econometric table that are used for performing regression analysis in the report.

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Limitation of the Study

Every research study there some limitations that remain in existence. It is not possible to conduct research studies that do not have any limitations. However, the existence of limitations in the research study does not mean that the results produced by the research are not reliable. The main limitation of the study may be that in the present calculation, only four to five years of data is taken into account (Ritchie et.al., 2013). This may affect research results. However, every effort is made to make sure that research will produce valid and reliable results. In this regard difference of different year values of specific variables is taken and by doing so econometric is prepared. Hence, by doing so it is ensured that there will be reliability of the results produced by the research. Thus, it can be assumed that calculations are producing accurate results. I

Components of Regression

Regression is a technique that is used to establish a relationship between two variables. There are five components of regression analysis Multiple R, R square, Adjusted R square, Standard error, and number of observations (Tietenberg and Lewis, 2010). In order to understand the results produced by the research it is necessary to understand these five components of the table. Broad understanding will help in interpreting results in systematic way. These five components of regression calculation are explained below.

Multiple R

This is very component of the regression table and it indicates the relationship between two variables (Barnett and Morse, 2013). Multiple R can also treated as a correlation and like the latter statistical tool values of multiple R remain in the range of -1,0 and +1 indicating that there is no correlation between two variables and both variables are moving in the inverse direction. Zero indicates that there is no relationship between two variables means that if one variable changes by 10% then this change will not have any sort of impact on other variable values (Yin, 2011). There is a +1 value which reveals that there is a perfect relationship between two variables and with a change in one variable other variable is also changing. For example, if one variable value changes by 20% then certainly the value of the other value of the variable will also change by 20%. If the value of correlation is between 0 to +1 then it means that more and more variables are associated positively with each other. Similarly, if the value of multiple R remains in the range of -1 to 0 then it means that the values of variables are low or highly negatively correlated with each other (Daly and Farley, 2011). Hence, it can be said that it is a very important statistical tool that is used by the researcher in order to analyze the relationship between two variables.

R Square

This component of regression analysis indicates the extent to which variation in the dependent variable is happening due to a change in the independent variable. The values of R square are not shown in percentage terms instead they are shown in points (Fowler 2013). However, we can change these values into percentages and identify the extent to which a specific independent variable is affecting the value of a dependent variable. If the value of R square is 0.87 then it means that 87% variation in values of variables of the dependent variable is affected by the independent variable. Hence, this is a very important statistical tool that is used by managers or researchers in order to broadly understand the relationship between two variables.

Adjusted R Square

This is the least important statistical tool that is used by business managers or researchers in their day-to-day practice. It is only a measure of the explanatory power of two variables (Nagurney, 2013). The value of variables never remains in percentage form and not in the form of F value. Hence, it is a very difficult task to use this tool to make interpretation. This value only remains in the table and is not used by data analysts in order to understand the relationship between two variables.

 

Standard Error

It is an estimation of the variation in the values of the dependent variable. This is also a very important statistical tool that is used by business managers in their practice. If the value of this variable is very high then it means that the values of the variable are changing at a rapid pace in the data set (Denzin. and Lincoln, 2011). Similarly, if the value of standard errors is very low then it means that variation is happening in the values of specific variables at a low pace.

Observations

It indicates the number of values of sample units that we take for the application of specific statistical tools. If there is a higher number of observations in the data set then there will be high reliability of the results produced by the statistical tool (Harborne, 2013). However, it depends on the variable and sometimes it is not possible to collect huge data on specific variables. Hence, it can be said that according to the data set, researchers must determine the number of observations for the research.

Vector auto Regressive Model

This model is also known as the VAR model and it is an econometric model that is used to present values of the specific variable. This model is used to capture the interdependence of multiple variables on each other. This model can be prepared by using various software like Excel and Matlab etc. In the present case, the entire calculation is done in Excel. In this regard, first of all, econometric models for computing Vector autoregression are prepared and regression of two values of the variable is done by the analyst. All variables in the VAR model are treated symmetrically. Under this model in order to do a better analysis of the values of the variable equation is prepared that explains progress in the variable by calculating lags of the variable. The same thing is done in the case of other variables. In this way entire model structure is prepared and regression of values of variables is done (Becker, 2010). In this way entire model helps in the identification of the relationship between two variables. The vector auto-regression model is one of the flexible and easy-to-use models for analyzing multivariate series of data. It can be said that it is a model that is used to compute the relationship between multiple variables in the data set.

This model is often used by economists in order to test the success or failure of steps taken by the government to control inflation and boost GDP (Camerer, C.F., Loewenstein, G. and Rabin, M., 2011). Thus, this model has great importance for analysts. It has been observed that in most of nations, this model is used by analysts to identify the impact of recent changes in the monetary policy on the inflation rate and GDP of the nation (Etzioni, 2010). Hence, the regression model which is also known as the Vector autoregression model has great importance for analysts in their day-to-day practice and research that they carry out on economics-related research topics.

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Conclusion

Suppose the interest rate changes by 1% then the GDP of Spain will also change. This is proved from the value of regression in the case of GDP and interest rate. It is also concluded that interest rates are only to a small extent affected by the changes in the GDP of Spain. When any committee of the central bank seat for determining the monetary policy of the nation they take into consideration changes that are taking place in all nations of the Euro-zone. If the condition of Greece is worse then the central bank will give due importance to the Greece economy and they will not consider the extent to which there is an inflation rate and GDP in Greece. This is the main reason why changes in the GDP of Spain do not have too much impact on the interest rate.

It is identified that interest rates are having a large impact on the GDP of Spain. This happened because interest rate directly affects the money supply in the Spain economy. There may be two cases either interest rate may increase or it may decline. If the interest rate increases then the banks of Spain will also hike their debt interest rate. Due to this reason, loans become dearer and firms will like to take less amount of loans from banks. Due to the increase in interest rate finance costs for debt that are taken at a floating interest rate will also increase. Hence, two things will happen in the Spain economy one is that less amount of loans will be available to business firms and the second thing is that the financial cost of the firms will increase. This may have a negative impact on the economy and due to less availability of loans productivity will be reduced in the economy. Due to this reason GDP of the Spain will decline. This theoretical concept is proved by the high regression and multiple regression value between GDP and interest rate. Hence, it can be easily concluded that the GDP of Spain is greatly dependent on the interest rate that is determined by the central bank. It is the responsibility of the central bank to address the concerns and conditions of all European nations and due to this reason small positive and negative changes in Spain's GDP do not have any significant impact on the interest rate. This theoretical concept is verified by the low regression value of interest rate and GDP.

References

  • Armendáriz, B. and Morduch, J., 2010. The economics of microfinance. MIT Press.
  • Atkinson, A.B. and Stiglitz, J.E., 2015. Lectures on public economics. Princeton University Press.
  • Barnett, H.J. and Morse, C., 2013. Scarcity and growth: the economics of natural resource availability (Vol. 3). Routledge.
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