Testing Money Demand Equation, Econometrics Assignment, SAS - Speech or Presentation Example

Testing Money Demand Equation, Econometrics Assignment, SAS Table of Contents Testing Money Demand Equation, Econometrics Assignment, SAS Table of Contents 2Question 1 3Question 2 7References 10Appendix 11Question 1IntroductionThe money market equation is a link between a few essential macroeconomic factors which indicates the aggregate demand of money in an economy over a considerable period of time. However, the form of the equation is purely a general one, which need not hold in the context of each and every economy.

The present case study involves the situation in USA, and is aimed at examining the factors which affect the economy-wide demand for money. The ModelThe model that has to be estimated in the present context is depicted underneath.mt – pt = γyt – αit + umtWhere, mt = natural logarithm of the US demand for money, pt = natural logarithm of US CPI, yt = natural logarithm of US output andit = US nominal rate of interest on 90-days Treasury Bills. All the variables have been retrieved from the archives of New York University, in the form defined above.

The data being collected are quarterly in nature beginning from the 3rd quarter of 1981 and ending at the 4th quarter of 2009. Estimation ResultsEstimation of the aforementioned model on the basis of the empirical data is, mt – pt = 1.3253 yt - 0.0962 it(12.27) (3.61)The figures in parentheses are the estimated Student’s t-statistic values respectively. To interpret whether variations in dependent variable, (mt – pt) is explained or not, explanatory powers of each of the two independent variables have to be considered.

If the Student’s-statistics of the respective estimated coefficients are found to be greater than the tabulated value at the given degrees of freedom, the corresponding variable is considered to be significantly explaining variations in the model and vice-versa. At 107 degrees of freedom, tabulated t-statistic is 1.99, which is lower than the estimated values in either case. Hence, each one of the two variables is found to be significantly explaining variations in the dependent variable so that variation in the model is perfectly explained.

Plotting the time-series graph of nominal rate of interest on 90-days US Treasury Bills reveals a gradually decreasing trend. ConclusionMoney demand in excess of the general price level is found to be highly dependent on income and rate of interest in context of the US economy. The dependence is found to be in line with that of theory which says that demand for money is directly related to income but inversely related to the rate of interest. Moreover, the rate of interest in the nation is also gradually falling over time, revealing that the money demand in the nation is rising actually.

A rise in money demand is actually a positive sign for economies which had been engulfed in a recession, since that implies a rise in aggregate demand and thus rise in national income. Hence it could be said that the US economy is actually at the verge of experiencing boom. In fact, a rising income will attract investors from all over the world thus ensure the nation a consistent period of boom.Question 2IntroductionThe model to be estimated here is, ln Pt = β0 + β1 t + etWhere, Pt = US price level or CPI andt = Time.

EstimationUsing the data from the above question, the model being estimated is as follows, ln Pt = 1.53799 – 0.00703 t + et (26.21) (7.79)Since the number of observations is the same as that in the previous case, the degrees of freedom are equal to 107. So, using the rule mentioned above it can be said that both the intercept and time factor can explain variations in the dependent variable significantly. Plotting the rate of inflation (ln Pt - ln Pt-1) against time, depicts the following graph which is symbolic of a fluctuating rate of inflation in the economy.

ConclusionThe model being estimated shows a significant relationship of price with time. According to the model being estimated, time draws only a negligible effect on the rate of inflation in the economy. It has been found that inflation rate falls by less than 1% per unit of time. This is a rather positive sign given that the inflation is considered as one of the most depressing of all economic malice. However, the problem is that, time cannot be considered as a considerable determinant of inflation, as is suggestive from the coefficient of the estimated model.

In fact, this is the case with most of the cases as is evident from the estimated level of significance or p-value. ReferencesHeboyan, V. (n.d.) ‘Beginner’s Guide to SAS and STATA Software’ USA: The University of GeorgiaAppendixDatasetTimeypmi1981 Q37.3211892.235376-1.7429714.441981 Q47.3193072.261763-1.6982711.031982 Q17.3172751.791759-1.6144512.221982 Q27.3076431.223775-1.5606511.751982 Q37.3010172.014903-1.532489.761982 Q47.2986091.386294-1.505088.031983 Q17.3156011.064711-1.474037.701983 Q27.3237741.386294-1.435487.941983 Q37.333421.504077-1.414699.031983 Q47.3369781.609438-1.362588.661984 Q17.3867581.435085-1.328038.851984 Q27.3946471.481605-1.347079.871984 Q37.4088471.308333-1.4312910.351984 Q47.4024081.386294-1.255279.221985 Q17.4019871.20896-1.248278.331985 Q27.4322871.386294-1.262317.931985 Q37.4269611.252763-1.26947.471985 Q47.425411.163151-1.241337.151986 Q18.1787591.163151-1.203976.971986 Q28.

205885#NUM!-1.158366.151986 Q38.2106331.064711-1.136315.501986 Q48.2100341.115142-1.11785.171987 Q18.233331.193922-1.084715.431987 Q28.2347381.446919-1.070025.891987 Q38.2449911.386294-1.055556.011987 Q48.2464341.435085-1.018885.851988 Q18.2704351.308333-1.016115.701988 Q28.2795631.423108-1.005126.331988 Q38.2930161.568616-1.002397.211988 Q48.2928881.609438-0.98357.671989 Q18.3184981.526056-0.972868.691989 Q28.3223941.774952-0.957118.361989 Q38.3272471.470176-0.946757.781989 Q48.3290471.386294-0.92137.431990 Q18.3307951.589235-0.884317.671990 Q28.3490091.386294-0.860387.731990 Q38.3358541.740466-0.843977.311990 Q48.333292.04122-0.798516.881991 Q18.3299571.360977-0.750785.761991 Q28.3286811.029619-0.715395.581991 Q38.3302231.163151-0.699175.541991 Q48.3276571.163151-0.689164.701992 Q18.5018781.098612-0.671393.931992 Q28.5062971.193922-0.644363.871992 Q38.4989131.098612-0.61993.201992 Q48.4989791.131402-0.586993.071993 Q18.5319621.098612-0.563873.091993 Q28.5308351.163151-0.539573.051993 Q38.5258080.97456-0.517513.121993 Q48.5418761.098612-0.482893.141994 Q18.5771291.064711-0.458873.391994 Q28.5803081.131402-0.427714.181994 Q38.5795141.193922-0.375424.631994 Q48.5815681.163151-0.356675.661995 Q18.6161551.147402-0.289026.251995 Q28.6145471.193922-0.262665.831995 Q38.6122251.064711-0.225655.471995 Q48.6152970.955511-0.142725.231996 Q18.8355761.029619-0.08234.791996 Q28.8374881.223775-0.025325.061996 Q38.8393080.9932520.0449735.231996 Q48.8394371.0986120.1177835.131997 Q18.8685261.0986120.1222185.101997 Q28.8754390.9162910.1186725.501997 Q38.8771270.7884570.1204465.291997 Q48.8802620.9162910.1518625.201998 Q18.9084190.6043160.1889665.091998 Q28.9121960.4700040.2239435.131998 Q38.9248830.6931470.2600545.041998 Q48.9266840.6418540.3015854.191999 Q18.9612540.6418540.3492474.381999 Q28.9679670.9932520.3974334.521999 Q38.9671440.7884570.43894.871999 Q49.0870820.9360930.4995625.062000 Q19.1264370.9162910.5306285.712000 Q29.1363441.1631510.5573276.192000 Q39.1414481.0647110.5894526.042000 Q49.1417721.0986120.6227256.062001 Q19.1609210.9932520.6302074.822001 Q29.1590851.1151420.6408013.652001 Q39.1460210.8754690.657523.312001 Q49.1403970.4700040.6168061.862002 Q19.1537910.3364720.6227251.802002 Q29.1677871.0986120.6339282.072002 Q39.1511030.6931470.6564831.742002 Q49.1516240.8329090.6805681.372003 Q19.1760660.9162910.7065571.282003 Q29.174350.6931470.7342891.262003 Q39.1758920.4382550.7654681.052003 Q49.1816580.7884570.7902741.062004 Q19.2940810.5306280.8206611.132004 Q29.2942751.0647110.8535641.372004 Q39.2902321.0986120.8858321.832004 Q49.2907130.9360930.9301942.292005 Q19.3263580.7654680.9639372.922005 Q29.3247781.1939220.9891693.402005 Q39.3191861.033540.9969493.812005 Q49.3184111.3609770.9928814.252006 Q19.3488740.6931470.9951024.612006 Q29.3513581.2237751.0013674.992006 Q39.343691.2809341.0206515.202006 Q49.

342293#NUM!1.0567485.002007 Q19.3703530.8828711.092934.982007 Q29.3637161.4350851.10364.942007 Q39.3539140.9472351.1474024.852007 Q49.3552061.0986121.1768073.912008 Q19.3737431.2484681.2053722.252008 Q29.3703161.2527631.221421.672008 Q39.3685221.6677071.257041.882008 Q49.36546#NUM!1.288130.712009 Q19.345254#NUM!1.3198870.292009 Q29.33569-0.356671.3252170.292009 Q39.4693580.8754691.311840.342009 Q49.4714840.7196951.3010090.22How to use SAS to run a regression?Import the dataset from Excel as a first step through, File → Import Data.

Select the mode of workbook from which to be imported, provide the name of the workbook (Question) and then name of the member if necessary. The dataset is stored in the ‘library’.To undergo regression, type the following command in the Editor box. Click on Run in the menu bar and then on Submit. This provides the regression result in the Log box. Question 1Question 2