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Present research is carried out to understand extent to which monetary policy as a tool of government and central banks is effective in bringing changes ion the economic life cycle to the nation. It is has been observed most of the times that when economic turmoil comes in existence central banks make changes in their monetary policy in order to bring economy on track and to control inflation rate (Starr, 2014). But physically any big change in economic cycle is not observed. Even most of times changes are bring in the policy but in the market prices of goods does 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 decisive role in bringing changes in the economic cycle of the nation (Armendáriz and Morduch, 2010). In this regard econometric are used in the present study and technique of Vector auto regression model is applied to the collected data.
In this research, data of Spain GDP and interest rate is taken. Change in GDP is the symbol of economic cycle of the nation and due to this reason GDP figures are taken in to this research. Apart from this central bank monetary policy indicate change in interest rate by the central bank. Thus, in research relevant figures are taken and by using both type of data Vector auto regression model is prepared (Habib, 2013). Hence, this research certainly produce good results that will be used for deriving useful conclusions.
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 are not available on any book, journal, magazine and website. Whereas, secondary data refers to the data that is already available and collected from sources of information like book, journal, magazine and website. It is very important to collect secondary data because by using this data researcher develop broad understanding about the at scenario. By reviewing information related to past mangers can estimate likely changes that currently may happen in the specific variable. On the basis of good understanding of past conditions researcher get a direction in which he needs to conduct a research. Thus, it is very important for one to collect secondary data before conducting a research.
In the present research data is collected from various websites and these sites are highly reliable source 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 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 help in evaluating single variable but by using same we can identify the extent to which one variable is depending on other for performance. Hence, it can be said that this research is very helpful in identifying the extent to which economic cycle is affected by the monetary policy of the government.
In order to conduct research quantitative approach is followed and under this econometric is prepared by the researcher. For preparing this metrics data of Spain GDP and interest rate is taken and various calculation are done as can be seen in the tables that are attached in the answer file. Lag GDP and interest rate is computed in the table and difference between values of lag column is calculated (Chaudhuri and Banerjee, 2010). This process is further carried out in case of variables GDP and interest rate of central bank. After doing all calculation in the table regression model is applied on raw data and calculated values. Under regression model values of X axis and Y axis are taken as inputs and on this basis values of various components of regression analysis table is 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 literature review to large extent help in understanding past scenarios and impact of monetary policy on the economic cycle of the nation.
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 literature review various sources of information are reviewed. Hence, it can be said that data is collected in appropriate way to conduct research.
Data analysis
In order to analyze data in proper way and to get useful results econometric 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.
Data related to GDP and interest rate is collected. Both type of data are arranged in different columns of the excel sheet. On these individual values various formulas applied and by doing this different sub calculations are done in the report.
This is second column in excel sheet for both variables namely GDP and interest rate. In order to carry out further calculations and to develop econometric model difference of GDP on year on year basis is computed in the excel sheet (Silverman, 2013). Similarly for interest rate also difference between interest rate of different years is computed in order to identify the changes in interest rate and GDP each and every year. This calculation is indicating that GDP and interest rate increase or decrease and to what extent. In this way calculation is giving overview of movement in values of variable.
In the third stage of calculation of lag GDP and interest rate is done. Lag GDP refers to the transfer of past year figure in current year in order to do better analysis of past figures (Clawson and Knetsch, 2013). In case of interest rate also lag interest rate is computed and in this case also past year rate of interest is transferred to current year for doing further calculation.
In this table difference between lag GDP values are computed. In this way most recent changes in the value of GDP are taken in to account for doping further calculation (Corbin and Strauss, 2014). This reflects that this matrix will certainly provide reliable results for the research. For interest rate also diff 1 interest column is prepared in the table and this helps researcher in tracking past few year changes in the interest rate of the firm.
This table is prepared after column of diff1 GDP and interest. In case of diff 2 GDP and interest rate in order to track most recent changes in the values of the variable calculation is done. In this regard past year changes in the variable are taken in to consideration for current year (Campbell and Stanley, 2015). In case of interest also past year changes that were computed in previous column is taken in to account and relevant years change in interest rate value is transferred to current year.
Finally, trend column is prepared in the excel sheet and under this year sequence 1,2,3,4 and 5 is given to all rows. This thing is done in case of both variables namely GDP and interest rate.
These are the all components of econometric table that are used for performing regression analysis in the report.
Every research study there some limitation remain in existence. It is not possible to conduct research studies that does not have any limitation. But existence of limitation in the research study does not mean that results produced by the research are not reliable. The main limitation of study may that in present calculation only four to five year data is taken in to account (Ritchie and er.al., 2013). This may affects research results. But every efforts is made to make sure that research will produce valid and reliable results. In this regard difference of different year values of specific variables are 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
Regression is a technique that is used to establish 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 results produced by the research it is necessary to understand these five components of table. Broad understanding will help in interpreting results in systematic way. These five components of regression calculation are explained below.
This is very component of the regression table and it indicate the relationship between two variables (Barnett and Morse, 2013). Multiple R can also treated as correlation and like latter statistical tool values of multiple R remain in range of -1,0 and +1 indicate that there is no correlation between two variable and both variables are moving in inverse direction. Zero indicate that there is no relationship between two variables means that if one variable change by 10% then this change will not put any sort of impact on other variable value (Yin, 2011). There is +1 value which reveal that there is perfect relationship between two variables and with change in one variable other variable is also changing. For example is one variable value change by 20% then certainly value of other value of variable will also change by 20%. If value of correlation is between 0 to +1 then it means that more and more variables are associated positively with each other. Similarly, if value of multiple R remain in range of -1 to 0 then it means that value of variable are low or highly negatively correlated with each other (Daly and Farley, 2011). Hence, it can be said that it is very important statistical tool that is used by the researcher in order to analyze relationship between two variables.
This component of regression analysis indicate the extent to which variation in dependent variable is happening due to change in independent variable. The values of R square are not shown in percentage terms instead they are shown in points (Fowler 2013). But we can change these values in to percentage and can identify the extent to which specific independent variable is affecting value of dependent variable. If value of R square is 0.87 then it means that 87% variation in values of variables of dependent variable are affecting by the independent variable. Hence, this is very important statistical tool that is used by the managers or researchers in order to broadly understand relationship between two variables.
This is least important statistical tool that is used by the business managers or researchers in their day to day practice. It is only measure of explanatory power of two variables (Nagurney, 2013). The value of variables never remain in percentage form and not in form of F value. Hence, it is very difficult task to use this tool to make interpretation. This value only remain in table and not used by data analysts in order to understand relationship between two variables.
It is an estimation of the variation in the values of the dependent variable. This is also very important statistical tool that is used by the business managers in their practice. If value of this variable is very high then it means that values of variable are change in at a rapid pace in the data set (Denzin. and Lincoln, 2011). Similarly, if value of standard error's is very low then it means that variation is happening in the values of specific variable at a low pace.
It indicate the number of values of sample units that we take for application of specific statistical tool. If there will be 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 depend on the variable and some times it is not possible to collect huge data of specific variable. Hence, it can be said that according to data set researchers must determine number of observations for the research.
This model is also known as VAR model and it is an econometric model that is used to present values of the specific variable. This model is used to capture interdependence of multiple variables on each other. This model can be prepared by using various software like excel and Matlab etc. In present case entire calculation is done in the excel. In this regard firs of all econometric model for computing Vector auto regression is prepared and regression of two values of variable is done by the analyst. Ala variables in VAR model are treated in symmetrically. Under this model in order to do better analysis of values of variable equation is prepared which explain progress in the variable by calculating lags of the variable. Same thing is done in case of other variables also. In this way entire model structure is prepared and regression of values of variable is done (Becker, 2010). In this way entire model help in identification of relationship between two variables. Vector auto regression model is one of the flexible and easy to use model for analyzing multivariate series of data. It can be said that it is a model that is used to compute relationship between multiple variables in the data set.
This model is often used by the economists in order to test success or failure of steps taken by the government to control inflation and to boost GDP (Camerer, C.F., Loewenstein, G. and Rabin, M., 2011). Thus, this model have a great importance for the analysts. It has been observed that in most of the nations this model is used by the analysts to identify the impact of recent changes in the monetary policy on the inflation rate and GDP of the nation (Etzioni, 2010). Hence,regression model which is also known as Vector auto regression model have a great importance for the analysts in their day to day practice and research that they carried out on the economics related research topics.
Suppose interest rate changed by 1% then GDP of Spain will also get changed. This is proved from the value of regression in case of GDP and interest rate. It is also concluded that interest rate is only to small extent affected by the changes in the GDP of the Spain. When any committee of central bank seat for determining monetary policy of the nation they take in to consideration changes that are taken place in all nations of the Euro-zone. If condition of Greece is worse then central bank will give due importance to the Greece economy and they will not consider the extent to which there is inflation rate and GDP in the Greece. This is the main reason due to which changes in the GDP of the Spain does not put too much impact on the interest rate.
It is identified that interest rate is putting large impact on the GDP of the Spain. This happened because interest rate directly affects money supply in the Spain economy. There may be two cases either interest rate may increase or it may decline. If interest rate increases then banks of Spain will also hike their debt interest rate. Due to this reason loan become dearer and firm will like to take less amount of loan from banks. Due to increase in interest rate finance cost for debt that are taken at floating interest rate will also increase. Hence, two things will happen in the Spain economy one is that less amount of loan will be available to business firms and second thing is that finance cost of the firms will increase. This may put negative impact on the economy and due to less availability of loan 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 be concluded that GDP of the Spain is greatly dependent on the interest rate that is determined by the central bank. It is responsibility of central bank to address concerns and condition of all European nations and due to this reason small positive and negative change in the Spain GDP does not put any significant impact on the interest rate. This theoretical concept is verified from the low regression value of interest rate and GDP.
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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