Consumption function of macroeconomic model of the BRICS countries: Brazil, Russia, India, China, South Africa. From 1981 to 2013

BRAZIL, RUSSIA, INDIA, CHINA, SOUTH AFRICA, CONSUMPTION, GDP, PREVIOUS PERIOD CONSUMPTION.

This economic system studies the consumption function of Macroeconomic model of the BRICS countries: Brazil, Russia, India, China, and South Africa. In this study we have two variables: GDP and consumption for the previous period, which are measured in US dollars.

The aim of this study is to test the adequacy of the empirical data of our consumption function, for this were used Statistical Hypothesis Testing.

It has been decided to build a model on a basis of “third equation of Lawrence Klein which is called consumption model”. Only those factors which will be able to explain the variations in the dependent variables will be left in the final output of the model and afterwards tested.

However, this study is important from the point of view of the analysis of latest data about chosen countries, such as: real retail sales in the BRICS, with the exception of Russia, have held up much better than in the advanced economies over the past two years, and this trend is expected to continue. Our consumption growth forecasts imply that, the BRICS will contribute almost half of global consumption growth.

In this paper we study the following countries: Brazil, Russia, India, China, and South Africa. The main methods of research are methods of analysis of econometric models and simulation methods.[1]

As a result of research we find out about, as far as are adequate econometric models, and suggests the possibility of money market function.

The necessity of this study caused the need for a detailed analysis of the economies, which are held in the same economy group as Russia.

An important step in this research is to collect relevant data on the two variables. The World Bank has long been leading a detailed and reliable statistics, it is this resource is the data source. [2]

Based on the results of research we have found out following : F-test

The F-distribution or Fisher F distribution is named after the famous statistician R. A. Fisher. The Fisher F distribution is the ratio of two chi-square distributions, and hence is right skewed. It has a minimum of 0, but no maximum value (all values are positive). The peak of the distribution is not far from 0, as can be seen in the following figure. We will use the F-distribution through the F test.[3]

• •     We used the probability of mistake equal to 0,05.

• •      If F is higher than F-crit than the R^2 is non-random and the quality

of the specification of the model is high

T-test

• •      If |T-stat| is bigger than T-crit it means that the coefficient is

significant

• •      As we can see, for China all coefficients are significant

• •      But for others situation is not so strict

GQ-test

The GQ test checks for homoscedasticity in regression analyses in order not to allow overestimation of the model’s results. The test is also sometimes called two-group test as it is based on the division of the dataset into two independent groups.

1/GQ

• •     As we can see, for all countries only 1 condition is true, therefore

residuals are heteroscedastic Heteroscedasticity is a major concern in the application of regression analysis, including the analysis of variance, because the presence of heteroscedasticity can invalidate statistical tests of significance that assume that the modelling errors are uncorrelated and normally distributed and that their variances do not vary with the effects being modelled.

DW-test

Durbin-Watson statistic is a test statistic used to detect the presence of autocorrelation in the residuals from a regression analysis.

• •    The DWstat for Brazil, India and Russia lies between DU and 4-DU –

it means that the residuals aren’t autocorrelated and 3rd Gauss-Markov condition is not broken

• •    DWstat for South Africa lies between 0 and DL, which means that

residuals are positively autocorrelated

DWstat for China lies between dl and du which means that residuals are also positively autocorrelated

Our final economic model shows high relevance of the independent variables for the explanation of the dependent one due to the high R2 value and a number of tests, which have been successfully run during the model check. [4]

BRICS countries were chosen as locomotive countries, which has been flourishing for the last years or even decades. Brazil is the flagman of Latin America, Russia, despite some current problems on the international area, is the biggest country in the World with a huge amount of resources, India and China are the biggest countries by population, where efficiency of labour force is very high, and South Africa, which is similar with Brazil, but on another, African, continent.

The 2007-2009 financial crisis has been a major challenge for all of the world economy. As we have shown in this paper, the BRICS economies collectively appear to have withstood the crisis better than many of their developed-country counterparts. Indeed, their contribution to world economic activity has increased even more through the crisis, and since. This is likely to continue in the near, medium and long term. We now think it is more likely, rather than less, that China will become as big as the US by 2027 and the BRICS will become as big as the G7 by 2032. China, Brazil and India have all performed particularly well, and although Russia has not done so recently[5], it deserves its position as a BRICS.