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The UK Demand for Money 1963-1989 Econometrics for Economists

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The UK Demand for Money 1963-1989 Econometrics for Economists (2030003) Exam: 57230 Index: Introduction Econometric analysis can be used to help explain the importance of certain factors on a dependent variable. In this project the dependent variable is the "demand for money in the UK between 1963-1989". Although it is very difficult, if not impossible to find perfect econometric models, this project will attempt to explain the relationship between the demand for money and other various explanatory factors. The Economic Theory Money is one of many forms of wealth. A simply economic explanation, using two of the more important forms of wealth, is that people have a choice between holding money and holding bonds. Money is used for everyday transactions and includes currency (coins and notes) and checkable deposits. Bonds will pay an interest rate but cannot be used for transactions, i.e. you cannot buy a cup of coffee with a bond. The interacting relationship between money and its substitutes (bonds being an example) can help explain the demand for money. The measure of money in this project is the liquid money, M1. The demand for M1 will basically depend on the opportunity cost of not holding M1 money, i.e. ...read more.


Dummy variables could be used to distinguish between other external variables such as the type of government in power at the time but this would be an unnecessary complication. Because this is a "log-linear" model, the coefficients will be a measure of elasticity. The regression will cover the time period, i.e. from 1963_1 to 1989_2 (Note: 1983_3/4 will not be included due to incomplete data) Hypotheses The variables will each be tested for individual significance and the overall strength of the model will also be measured. After the initial regression the model will be tested for problems such as autocorrelation, multicollinearity and misspecification bias. The model shall also be tested in the long run. As mentioned before, if there is sufficient time the ca variable will be looked at. Estimation The regression results from our original basic model are reproduced in appendix, (a). The signs of the two explanatory variables are as expected, i.e. the level of disposable income has a positive relationship with the demand for money while the interest rate has a negative one. The t-values obtained from the coefficients of RNET and IA reject the null hypothesis that they are individually insignificant at both the 5% and 1% level of significance. ...read more.


= 1539 [0.000]** log-likelihood 292.988 DW 2.01 no. of observations 103 no. of parameters 12 mean(ma-pa) 10.8974 var(ma-pa) 0.0370415 Error autocorrelation coefficients in auxiliary regression: Lag Coefficient Std.Error 1 0.34614 0.9412 2 0.48134 0.6174 3 -0.23942 0.2097 4 0.16274 0.1137 5 0.21758 0.1099 RSS = 0.0188337 sigma = 0.000218997 Testing for error autocorrelation from lags 1 to 5 Chi^2(5) = 7.9075 [0.1614] and F-form F(5,86) = 1.4303 [0.2216] Testing for heteroscedasticity using squares Chi^2(22)= 20.089 [0.5774] and F-form F(22,68) = 0.74893 [0.7728] APPENDIX (d) EQ( 3) Modelling ma-pa by OLS (using dataset3.in7) The estimation sample is: 1963 (4) to 1989 (2) Coefficient Std.Error t-value t-prob Part.R^2 ma-pa_1 0.909460 0.01242 73.2 0.000 0.9819 Constant 0.120873 0.1260 0.960 0.340 0.0092 RNET -0.743990 0.05791 -12.8 0.000 0.6251 ia 0.0864331 0.009326 9.27 0.000 0.4645 sigma 0.0149187 RSS 0.0220341891 R^2 0.994225 F(3,99) = 5681 [0.000]** log-likelihood 289.019 DW 2.46 no. of observations 103 no. of parameters 4 mean(ma-pa) 10.8974 var(ma-pa) 0.0370415 Error autocorrelation coefficients in auxiliary regression: Lag Coefficient Std.Error 1 -0.25767 0.1024 2 -0.053333 0.1055 3 -0.079404 0.1061 4 0.11792 0.1063 5 0.20319 0.1029 RSS = 0.0196544 sigma = 0.000209089 Testing for error autocorrelation from lags 1 to 5 Chi^2(5) = 11.125 [0.0490]* and F-form F(5,94) = 2.2764 [0.0531] Testing for heteroscedasticity using squares Chi^2(6) = 7.3739 [0.2876] and F-form F(6,92) = 1.1824 [0.3226] ?? ?? ?? ?? Exam Number: 57230 ...read more.

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