Analysis Of Gdp Determinants Statistics Economics Essay

Analysis Of gross domestic product Determinants Statistics Economics EssayThe aim of our write up is to identify the factors which affects gross domestic product for any country. In our draw we argon analyzing such factors in congress to Indias gross domestic product. The report go away be dealing with regress analysis, possibility rivuleting, mean, median, modal nourish and so forthof such factors which atomic number 18 in certified inconstants and their affect on gross domestic product which is a subordinate uncertain.2. IntroductionThe report revolves around an exploratory take on. It includes analyzing the statistical information of Indias gross domestic product and different factors same(p) craft to world ratio, FDI, nation for the past 20 years. Our aim is to identify the dependence of above menti championd factors on gross domestic product of India and takingss of the statistical regression analysis. hither we take GDP as the dependent variable and other three factors as the self-sustaining variables. in the ply getting into analysis we look into the substitute measures of each variable used in the report for analyzing regression, hypothesis testing and calculating the mean and median values.GDPLending rice beer stridesFDI state consumption to universe of discourse ratio2.1) GDPGDP forms the dependent variable of our look study. GDP is an indicator of boilers suit economic welf atomic number 18 of a country. It is the sum total of the final goods and services produced within the borders of the nation. In other words, summing up the consumption, government expenditure, investments and net exports results in the GDP suppuration prescribe of a country. It is considered to be a very important element as it helps in estimating the health of countrys economic system. Calculating the GDP is not an simplified task because of which they are left to be calculated by the economists. GDP growth assess of an saving is dependent on various factors like FDI, population to employment ratio, change come to rate, population etc. Governments closely watch out the GDP as it measures the economic performance of the country. If GDP rises it room the economy has grown and vice versa.GDP is criticized on various grounds like it does not talks or so the spending power, distribution of income or well being of countrys inhabitant.Formula of calculating GDPGDP=C+I+G+(X-M)Where,C=consumptionI=investmentG=government expenditure (X-M) = moolah ExportsThe independent variables (Population, Lending Interest pass judgment, FDI, Employment to population ratio etc ) that we are using in the research analysis affect each of these above mentioned components.The information related to GDP has been shown in the supplement 9.12.2 ) Employment to population ratioIt is taken as an independent variable in our study. We attempt to measure the meet of this ratio on GDP growth in India. Employment to population ratio basically means the ratio of the total working age of the labor force employed over total population. pursuance formula is used in calculating the ratioEmployment to population ratio=Employed Persons/ issue forth non-institutionalized civilian population *100This ratio does not bear underrating problems relating to unemployed persons and other disapprove workers that enter or exit the labor force.The data of employment ratio is shown in appendix 9.12.3) FDI irrelevant direct investment is an independent variable in our research report. We try finding out the push of FDI on GDP growth rate.In simple words it is an investment make by one company in a given country in another company based or set up in another country.FDI has been a major source of managerial skills, technology, capital and irritate to market desired for sustained economic development and growth in the recent past. All the Countries are involved in inviting more and more of foreign direct investment to come and invest in their c ountry resulting in rapid economic growth. FDI is considered to be an important factor in facilitating growth, however, it will result in growth solo if the inflows are carefully managed and invested.The related data is included in the appendix 9.1 for the past 20 years . All the inferences related to FDI has been drawn on the basis of such data.2.4) PopulationThe growth of population has always been a debating issue. There is no conclusion as to it is negative or decreed for growth of an economy. Population and economic growth are interrelated to each other in a way that, population cast ups cod to richly economic growth extending to early marriages and hiking expect rates and containing on the mortality rates by providing healthy environment to the citizens. Higher population likewise depresses economic growth through diminishing returns. (Becker, Glaeser, and Murphy 1999, p. 145) In our study we attempt to study the jounce of population on economic growth which is expre ssed as the real GDP per capita.The data regarding population for past 20 years has been included in the appendix 9.1. All the analysis related to it has been do on the basis of such data and various results has been drawn on such basis.2.5) Lending Interest RateLending Interest rate is the amount of interest charged by a lender from a borrower in case of loan being offered. A countrys real GDP and interest rates are interlinked in a variety of means. They induct a direct consanguinity, i.e., naughty lending rate results in high growth for the economy. The dependence of GDP on Lending Interest Rate fuck be studied in the analysis of the study.The related data regarding interest rate is included in appendix 9.13. Tools used for research studyThe statistical tools used in our research study with the accusive of finding the dependence of GDP on various independent variables like, Population, Lending interest rates, FDI, Employment to population ratio are degeneration Analysis, M ean, Median, Mode, shot TestingSteps involved in analysisOur low step was to scrutinize and identify the factors responsible for GDP growth of a nation.Next, we tried locating data of the identified factors for the past 20 years.Than we tried finding family relationship of various factors with the GDP of a nation.The data was than constructed and summarised in a proper manner to conduct regression analysis.SPSS software was used to conduct the regression analysis and hypothesis testing.All the data and factors collected are using the secondary sources, i.e., Internet and Journals.4. info DescriptionN i.e. the number of observations for our research is 20.There are twain types of dataQualitative duodecimalQualitative data deals with categorical measurement and is not measured in terms of numeric values. For our study the qualitative factors clear be like Market Risk, Business Confidence etc, which affects the GDP but due to their qualitative nature we overlooked them of the stu dy.Quantitative data deals with the numerical measurement of the database. Relating to our study, the quantitative data factors are Population, GDP, Lending interest rates, FDI etc.We submit limited our research taking in account only the quantitative data.Data can also be time series or cross sectionalCross sectional data is the data for a given point of time analyzing the differences among the subjects.Whereas time series data is concerned with the data over a spread time courseIn our report we are using time-series data, for 20 years i.e. from 1991 to 2010.Regression AnalysisRegression Analysis means scrutinizing the relationship among a dependent and independent variable. After conducting an analysis, regression statistics is helpful in identifying the dependent variable when the independent variables are unknown.Dependent Variable- GDP growth of IndiaIndependent variable- FDI, Employment to Population ratio and echt Interest RateThe regression equation isY=b1X1+b2X2+.+EIn the above equation-Y= dependent variableX1, X2 =independent variablesb1, b2 =coefficients describing achievement of independent variables on the dependent variablesE= shift termIn our study, the equation looks like,GDP=b1F1+b2F2+b3F3++bnfnWhere,F1 to Fn= independent variable factorsHypothesis testingHypothesis testing is the test of importation wherein we identify the likelihood that an assumption is avowedly, and at what likelihood we would hold the assumption as true. The assumption made is referred as the Null hypothesis and is denoted by H0 and an alternate hypothesis is be known as an preference Hypothesis and is denoted by Ha. The rationale behind this test is to hold null hypothesis to be true and then performing the study on the argument in question. Once the hypothesis is defined and data is collected and constructed, following steps of hypothesis are followedA critical region of size alpha is determined using the sample distribution of the test statisticsUsing the sam ple data, identify the values of test statisticsThe last step is confirming that the value of test statistics falls under the critical region defined if no, we accept the null in favour of the alternative hypothesis and if yes, we hold out the null hypothesis.5). Variables considered as independent in hypothesis and doing regression analysisIn doing the analysis of various factors affecting GDP growth some of the factors which is been considered as the variables are FDI, Employment ratio, Population and Lending interest rate as they dissemble directly in the growth of GDP for any country. The factors are denoted by r and following relation between the factors as stated above and the regression can be explained under5.1) Population (H1)Although augment in population has a negative impact on the economy of any country. However, such increase allows availability of labor at cheap rates which attracts the companies or firms to make more investment in the form of unusual direct inves tment or FIIs that helps to give upward thrust in GDP.5.2) Foreign Direct Investment(H2)It can be argued on the grounds of proven facts that Foreign Direct Investment has a dogmatic relation for boosting the economic growth of any country which results in increasing the GDP of the country. FDI allows the coin to come in the economy which creates opportunities to increase growth of the economy.5.3) Employment Ratio(H3)It also affects the economy which indeed affects GDP growth as with the increase in the employment more expenditure will have to be incurred which in turn affect countrys GDP5.4) Lending Interest Rate(H4)If the interest rate increases it will lead to less property circulation in the economy. The banks and financial institutions of the country will not able to lend money as the people will not be willing to accept because the increase in interest rate will attract more interest expense and hence will resist to it. This will result in moderate in the consumption which will bend the GDP down and secondly money circulation will also reduce which result in the fall in GDP growth. then the whole discussion can be summarized in the following manner-H1- Increase in population lead to increase GDP. Hence it has a positive relationH2- Increase in FDI Increases GDP. It also has a positive relationH3- Increase in employment leads to increase in GDP. It shows positive relationH4- Increase in Interest rate leads to fall in GDP. It has a negative relation6). Regression results6.1) Employment Regression (Appendix 9.3.1)In this regression regulate,Employment ratio is an independent variable and on X-axis.GDP is a dependent variable and on y-axis.After doing data analysis of this model, we close down that the regression equation for this isHere,is an tip which is 1.736is a slope of this equation which is -2.958 Estimated valueIf employment ratio is increase by 1, at that place is decrease in GDP by 2.958.There is negative linear relationship between GDP an d employment ratio. straightway if we talk astir(predicate) correlation between these two variable which is R.= +(.720)= +.849In this + sign shows that correlation is positive and is .849 instantaneously is .72 which shows that 72 % variance in GDP is explained by employment ratio. at a time if we talk about this model whether it is good or bad, we have to check two condition.should be highIn this is high.Hypothesis test = 0 (no linear relationship between X and Y) 0 (linear relationship between X and Y)This is conclude by t statisticsNow, = -6.80 Standard errorvalue is .000 and we assume is .05 which is great than p-value.Hence we reject .So we conclude that it is a good regression model.6.2) FDI Regression (Appendix 9.3.2)In this regression model,FDI is an independent variable and on X-axis.GDP is a dependent variable and on y-axis.After doing data analysis of this model, we conclude that the regression equation for this isHere,is an arrest which is 3.894is a slope of this equation which is 0.029 Estimated valueIf FDI is increase by 1, there is increase in GDP by .There is positive linear relationship between GDP and FDI as the slope is positive.Now if we talk about correlation between these two variable which is R.= +(.782)= +.884In this + sign shows that correlation is positive and is .884Now is .78 which shows that 78 % variance in GDP is explained by FDI.Now if we talk about this model whether it is good or bad, we have to check two condition.should be highIn this is high.Hypothesis test = 0 (no linear relationship between X and Y) 0 (linear relationship between X and Y)This is conclude by t statisticsNow, = 8.025 Standard errorvalue is .000 and we assume is .05 which is greater than p-value.Hence we reject .So we conclude that it is a good regression model.6.3) Lending interest rate (Appendix 9. 3.3)In this regression model,Lending interest rate is an independent variable and on X-axis.GDP is a dependent variable and on y-axis.After doing dat a analysis of this model, we conclude that the regression equation for this isHere,is an intercept which is 2.088is a slope of this equation which is -1.066 Estimated valueIf lending interest rate increases by 1, there is decrease in GDP by 1.066.There is negative linear relationship between GDP and lending interest rate as the slope is negative.Now if we talk about correlation between these two variable which is R.= +(.466)= +.683In this + sign shows that correlation is positive and is .849Now is .46 which shows that 46 % variance in GDP is explained by employment ratio.Now if we talk about this model whether it is good or bad, we have check two condition.should be highIn this is high.Hypothesis test = 0 (no linear relationship between X and Y) 0 (linear relationship between X and Y)This is conclude by t statisticsNow, = -3.964 Standard errorvalue is .001 and we assume is .05 which is greater than p-value.Hence we reject .So we conclude that it is a good regression model.6.4) P opulation Regression (Appendix 9.3.4)In this regression model,Population is an independent variable and on X-axis.GDP is a dependent variable and on y-axis.After doing data analysis of this model, we conclude that the regression equation for this isHere,is an intercept which is 3.894is a slope of this equation which is 0.029 Estimated valueIf population is increase by 1, there is increase in GDP by 3.60There is strong positive linear relationship between GDP and world as the slope is positive.Now if we talk about correlation between these two variable which is R.= +(.819)= +.905In this + sign shows that correlation is positive and is .905Now is .81 which shows that 81 % variance in GDP is explained by population.Now if we talk about this model whether it is good or bad, we have to check two condition.should be highIn this is high.Hypothesis test = 0 (no linear relationship between X and Y) 0 (linear relationship between X and Y)This is conclude by t statisticsNow, = 9.031 Standa rd errorvalue is .000 and we assume is .05 which is greater than p-value.Hence we reject .So we conclude that it is a good regression model.7) ConclusionThe effect of factors like employment ratio, foreign direct investment, lending interest rate and population on GDP of India are considered as important variables which we have tried to explain with the help of regression analysis and hypothesis testing. By considering the data of past 20 years we have also calculated its mean, median, mode, Variance, standard deviation (appendix 2). We have one dependent variable that is GDP and four independent variables which are FDI, employment ratio, population, and interest rate. correspond to multiple regressions, the equation for the model isWhere x1, x2, x3, x4 are the independent variable, estimated value E(y) is expected by these variable. In our report, we have taken separate simple regression modelsRegression analysis cannot interpret as a force for establishing a cause and effect rel ationship between variables. It can only show that how much these variables are related or associated with each other. Regression equation tells us about mean value of y for given value of x. According to Hypothesis test, all four regression model is good model and it estimates the mean value for these independent variables with less errors. The models also shows the relationship between GDP and these independent variables and their effect on GDP. If value of these independent variables is increase by 1, we conclude how much it affects the estimated value of GDP.Estimated value of GDP is increases by 3.60 if there is one unit increase in populationEstimated value of GDP is increased by .029 if there is one unit increase in FDIEstimated value of GDP is diminish by 2.958 if there is one unit increase in employment ratioEstimated value of GDP is decreased by 1.066 if there is one unit increase in interest rateHence it can be concluded that all factors affects GDP and we cant estimate GDP if we dont have particular value of these independent variable.8) BibliographyReferencesAmosweb.com (2012) AmosWEB is Economics Encyclonomic WEB*pedia. online Available at http//www.amosweb.com/cgi-bin/awb_nav.pl?s=wpdc=dspk=employment-population+ratio Accessed 29 Nov 2012.Anderson, D. and Sweeney, D. 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