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Economic growth and GDP estimates: Introduction: economic growth determined by the level of GDP, economic growth is therefore determined by changes in the level of GDP, Keynesian model to measure economic growth, the income of a country is determined by adding up consumption, investment, government spending and net exports. However, there are different approaches in measuring the Olympics GDP, and this includes the income approach, cost approach and the product approach. In this paper we determine the level of GDP as a function of inflation, net exports, FDI and domestic investment. The calculation of this model was made using data for the UK economy for the period 1970-2002, however, an estimated model which is clay to determine the statistical significance OIF model and also on all the assumptions of OLS estimation model are met such as absence of autocorrelation and Heteroscedasticity. In this case we will use time series data, and this type of data is a risk of autocorrelation, the autocorrelation is defined as cases where one of the assumptions OLS is violated, which states that the error expression in two different periods of observations have zero covariance. Therefore the existence of autocorrelation means that our estimates are not BLU. This paper is therefore necessary that efforts to eliminate the problem of autocorrelation, and therefore involves assessing three different models. Results: first estimated a model that LGDPt = b1 + b2LXt b3LFDIt + + + b4LDIt b5INF, which means that GDP level is a function of exports, foregn direct investment, domestic investment and the level of inflation, the results of our estimated model is as follows: LGDPt = 11th 15,785 + 0th 366,704 LXt -0. 006,544 LFDIt + 0th 265,253 LDIt-0. 001313 INF This means that if we increase exports LX with one unit so the level of LGDP will increase by 0 366,704 assuming all factors held constant, if we increase the volume of FDI LFDI with one unit so the level of LGDP will decrease by 0 006,544 assuming all factors held constant, if we increase the size of LDI with one unit so the level of LGDP will increase by 0 265,253 assuming all factors held constant, and finally, if we raise the inflation rate INF with one unit so the level of LGDP will decrease by 0 001313th Statistical significance: We carry one two tail test at the estimated coefficient of 98% level, the following table summarizes the results of the test: 98% level of test (two tail test) variable coefficients ; T statistics T critical null hypothesis alternative hypothesis null hypothesis

C b1 14th 3179 1st 281 552 b1 = 0 b1 ≠ 0 reject LX

b2 13th 04894 1st 281 552 b2 = 0 b2 ≠ 0 reject LFDI

b3 -1. 01064 1st 281 552 b3 = 0 b3 ≠ 0 accept LDI

b4 5th 183 639 1st 281 552 b4 = 0 b4 ≠ 0 reject INF

b5 -1. 45926 1st 281 552 b5 = 0 b5 ≠ 0 reject All the estimated coefficients are statistically significant at 98% two tail test except LFDI coefficients are not statistically significant. Coefficient of determination R squared for this model is the 99th 2292%, which means there is a very strong correlation between the explanatory variables and dependent variable, this value means that the 99th 229% of the variation of LGDP is explained by the explanatory variable. Other tests: 98% level of test (two tail test) null hypothesis alternative hypothesis t-statistic: T critical null hypothesis

b2 b2 = 0 b2> 0 13th 04894 1st 281 552 reject b3 b3 = 0 b3> 0

-1. 010 641 1st 281 552 accept b4 b4 = 1 b4> 1

-14. 35866018 1st 281 552 reject b5 b5 = 0 b5 <0 ;

-1. 459259 1st 281 552 reject From the above tests, it is clear that b2 is greater than 1, b3 is zero, b4 is greater than 1 and b5 is less than zero, a further test for autocorrelation with regard to the Durbin Watson test if the coefficient 0th 646833 shows that there is autocorrelation. Estimate two involved assessing model LGDPt = b1 + + b20LXt b21LXt-1 + + b30LFDIt b31LFDIt-1 + + b40LDIt b41LDIt-1 + + b50INFt b51INFt-1 + b5LGDPt-1 + ut, this model results in time lags of the variables in the model, it does increase other variables describing the previous values after estimation results are as follows: LGDPt = 3rd 251,747 + 0th 068,721 LXt + 0th 011,649 LXt-1 + 0th 001,123 LFDIt – 0 003,232 LFDIt-1 + 0th 250,335 LDIt – 0 156,964 LDIt-1 – 0 001,963 INFt + 0th 000,493 INFt-1 + 0th 719,464 LGDPt-1 Test statistics for statistical significance are summarized in the table below: 98% level of test (two tail test) < , p> variable coefficients T statistics T critical null hypothesis ;

alternative hypothesis null hypothesis C b1 2nd 447 224 1st 281 552 b1 = 0 b1 ≠ 0 reject LXT

B20 0th 959 683 1st 281 552 B20 = 0 B20 ≠ 0 accept LXT_1

B21 0th 154 077 1st 281 552 B21 = 0 B21 ≠ 0 accept LFDIT

B30 0th 293 811 1st 281 552 B30 = 0 B30 ≠ 0 accept LFDIT_1

B31 -0. 79871 1st 281 552 B31 = 0 B31 ≠ 0 accept LDIT

B40 4th 820 457 1st 281 552 B40 = 0 B40 ≠ 0 reject LDIT_1

B41 -2. 78611 1st 281 552 B41 = 0 B41 ≠ 0 reject INFT

B50 -2. 94661 1st 281 552 B50 = 0 B50 ≠ 0 reject INFT_1

b51 0th 738 533 1st 281 552 b51 = 0 b51 ≠ 0 accept LGDPT_1

b5 6th 532 978 1st 281 552 b5 = 0 b5 ≠ 0 reject coefficients of LXt, LXt- 1 LFDIt, LFDIt-1 and INFt-1 is not statistically significant at the 98% test level, but all the other coefficients are statistically significant. correlation of determination value for this model squared R is equal to 0 997,893 which means that there is still a very strong correlation between the explanatory variables and the dependent variable, the value means that the 99th 7893% of the variation of LGDP explained by the independent variables.

account autocorrelation by Durbin Watson test value equals the second 026,241 is the value two indicates that there is zero autocorrelation, and therefore we can conclude that the addition of lagged variables in our first model has removed the autocorrelation although there is some degree of autocorrelation in the model, and therefore estimates are not BLU. Our third estimation involves assessing model LGDPt = b1 + + b20LXt b21LXt-1 + + b30LFDIt b31LFDIt-1 + + b40LDIt b41LDIt-1 + b5LGDPt-1 + ut, this model includes the removal of inflation variables in the model after model estimation were the following results: LGDPt = 2 322,694 + 0th 137,682 LXt – 0 054,333 LXt-1 – 0 002289LFDIt – 0 004,492 LFDIt-1 + 0th 278,083 LDIt – 0 185,637 LDIt-1 + 0th 750794LGDPt-1 statistical significance of the estimated coefficients are summarized in the table below: 98% level of test (two tail test) < p> variable coefficients T statistics T critical null hypothesis

alternative hypothesis null hypothesis C b1 1st 734 605 1st 281 552 b1 = 0 b1 ≠ 0 reject LXT

B20 1st 799 921 1st 281 552 B20 = 0 B20 ≠ 0 reject LXT_1 B21

-0. 66139 1st 281 552 B21 = 0 B21 ≠ 0 accept LFDIT

B30 -0. 55771 1st 281 552 B30 = 0 B30 ≠ 0 accept LFDIT_1

B31 -1. 03266 1st 281 552 B31 = 0 B31 ≠ 0 accept LDIT

B40 5th 274 759 1st 281 552 B40 = 0 B40 ≠ 0 reject LDIT_1

B41 -2. 99087 1st 281 552 B41 = 0 B41 ≠ 0 reject LGDPT_1

b5 6th 101 389 1st 281 552 b5 = 0 b5 ≠ 0 reject From the above summary of statistical tests it is clear that the coefficients of LXt-1, LFDIt and LFDIt-1 is not statistically significant, but all the other coefficients are statistically significant at the 98% test level. Correlation determination in this model R squared is equal to 0 997,034 which means that the 99th 7034% of the variation in the dependent vatrriable can be explained by the explanatory variables, there is s strong relationships between the independent and dependent variables. Tests for autocorrelation shows that the Durbin Watson test is first 959,273, this shows that the autocorrelation has been reduced from previous estimates, but the value is not equal to 2, and therefore no estimates BLU Conclusion: From the above analysis shows that the level of GDP, which means economic growth can be approximated by the value of exports, FDI, domestic investment and inflation, the first estimated model violates OLS assumptions about autocorrelation, and therefore there is a need to contain the model again, the second model estimated includes time lagged variables for all the independent variables involved and the time lagged GDP level. This helps to reduce the level of autocorrelation in the first model, although it still exhibits autocorrelation, our third model involves mentioning the model again but this time in the absence of inflation and the time lagged inflation variable, which also reduces the autocorrelation, but the model still exhibits autocorrelation. From the above study is the second and third model can be used to estimate the size of GDP, because they exhibit low autocorrelation, the R squared from the model shows that there is a very close relationship between the variables , this discussion above shows how we can reduce autocorrelation and remove the problem so we come up with a good linear neutral model, therefore, models can be used to assess GDP and also for forecasting. References: David Cox (2001) Applied Statistics: Principles and Examples, McGraw Hill Press, New York Leonard Henry (1991) Statistics, Oxford University Press, Oxford Appendix: dependent variable: LGDP Method: least squares Date: 04/17/2008 Time: 14:20 Sample: 1970 2002 < / p> Included observations: 33 Variable Coefficient Std. Error t-Statistic Prob. C 11th 15785 0th 779 293 14th 31790 0th 0000 LX 0th 366 704 0th 028 102 13th 04894 0th 0000 LFDI -0. 006 544 0th 006 475 -1. 010 641 0th 3208 LDI 0th 265 253 0th 051 171 5th 183 639 0th 0000 INF -0. 001 313 0th 000 899 -1. 459 259 0th 1556 R-squared 0th 992 292 Mean dependent was 27th 56269 Adjusted R-squared 0th 991 191 SD-dependent were 0th 216 879 SE of regression 0th 020 355 Akaike info criterion -4. 812 252 Sum squared Resid 0th 011 601 Schwarz criterion -4. 585 509 Log likelihood 84th 40216 F-statistic nine hundred and first 1990 Durbin-Watson State 0th 646 833 Prob (F-statistic) 0th 000 000 dependent variable: LGDPT Method: least squares Date: 17/04/2008 Time: 14:33 < / p> Sample: 1971 2002 Included observations: 32 Variable Coefficient < , p> Std. Error t-Statistic Prob. C 3rd 251 747 1st 328 749 2nd 447 224 0th 0228 LXT 0th 068 721 0th 071 608 0th 959 683 0th 3476 LXT_1 0th 011 649 0th 075 604 0th 154 077 0th 8790 LFDIT 0th 001 123 0th 003 823 0th 293 811 0th 7717 LFDIT_1 -0. 003 232 0th 004 046 -0. 798 710 0th 4330 LDIT 0th 250 335 0th 051 932 4th 820 457 0th 0001 LDIT_1 -0. 156 964 0th 056 338 -2. 786 108 0th 0108 INFT -0. 001 963 0th 000 666 -2. 946 612 0th 0075 INFT_1 0th 000 493 0th 000 667 0th 738 533 0th 4680 LGDPT_1 0th 719 464 0th 110 128 6th 532 978 0th 0000 R-squared 0th 997 893 Mean dependent was 27th 57356 Adjusted R-squared 0th 997 031 SD-dependent were 0th 211 029 SE of regression 0th 011 498 Akaike info criterion -5. 842 928 Sum squared Resid 0th 002 909 Schwarz criterion -5. 384 885 Log likelihood 103rd 4868 F-statistic 1157th 767 Durbin-Watson State 2nd 026 241 Prob (F-statistic) 0th 000 000 dependent variable: LGDPT Method: least squares Date: 17/04/2008 Time: 14:40 < / p> Sample: 1971 2002 sightings Included: 32 Variable Coefficient < , p> Std. Error t-Statistic Prob. C 2nd 322 694 1st 339033 1st 734 605 0th 0956 LXT 0th 137 682 0th 076 493 1st 799 921 0th 0845 LXT_1 -0. 054 333 0th 082 150 -0. 661 392 0th 5147 LFDIT -0. 002 289 0th 004 104 -0. 557 712 0th 5822 LFDIT_1 -0. 004 492 0th 004 350 -1. 032 660 0th 3121 LDIT 0th 278 083 0th 052 720 5th 274 759 0th 0000 LDIT_1 -0. 185 637 0th 062 068 -2. 990 870 0th 0063 LGDPT_1 0th 750 794 0th 123 053 6th 101 389 0th 0000 R-squared 0th 997 034 Mean dependent was 27th 57356 Adjusted R-squared 0th 996 169 SD-dependent were 0th 211 029 SE of regression 0th 013 062 Akaike info criterion -5. 625 897 Sum squared Resid 0th 004 095 Schwarz criterion -5. 259 463 Log likelihood 98th 01435 F-statistic 1152nd 491 Durbin-Watson State 1st 959 273 Prob (F-statistic) 0th 000 000