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ECS3706 EXAM PACK

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ECONOMETRICS STUDY PACK

MAY/JUNE 2016

Question 1

(a) (i) Describing economic reality

(ii) Testing hypothesis about economic theory

(iii) Forecasting future economic activity

(b) (i) regression analysis is used to make quantitative estimates of economic

relationship that previously have been completely theoretical in nature.

(ii) usually population size may be too large or unknown therefore inference is made
on the sample drawn from the population using the estimated regression equation which
is the statistical technique that attempts to explain movements in one dependent
variable given independent variables

(iii) TSS is the squared variation of Y around its mean it is measured by

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∑ . ESS is the variation of Y around its mean which is explained by the
equation it is measured by ∑ . RSS is the variation of Y around its mean
which is not explained by the equation, measured by ∑ .

TSS= ESS+RSS

(c) is the co-efficient of determination which measures the goodness of fit of the
whole equation. The problem with is that whenever an independent variable is added
it will increase even when it is an irrelevant variable. measures the percentage of the
variation of Y around its mean that is explained by the regression equation, but adjusted
for the degrees of freedom. Usually a higher fit does not guarantee a good estimation
because the fit can be only measure used.

Question 2

(a)(i) Theory which underlies the model

(ii) The statistical significance of the t-test

(iii) The which determines the goodness of fit of the whole equation

(iv) Bias

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(b) Sequential specification involves the initial specification of an equation and
estimates it, then adding other independent variables until a plausible equation is found
while data mining involves estimating a variety of alternative specifications before that
best equation has been chosen.

(c) (i) Specification bias occurs when an independent variable is omitted which is
relevant in the equation.

(ii) Unbiased estimates could mean the equation is correctly specified or an

independent variable is omitted those results in a high variance while biased estimates
may decrease the variance. The importance of each of the two would depend on the
importance of either type 1 or type 2 errors.

(iii) To avoid specification the four important specification criteria must be followed
(refer to question 2 (a).)

Question 3

(a) The stochastic error term is added to the regression equation because it helps
capture the minor variations in Y which are not captured by the Xs. It is virtually
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 impossible to avoid some sort of measurement error hence the stochastic error
captures that. The underlying theory may differ from the chosen functional form
hence the need for an error term.

(b) OLS means ordinary least squares, it is a technique that calculates the Bs so as
to minimize the sum of p squared residuals which is calculated as .

(c) (i) Review the literature and develop the theoretical model

(ii) Specify the model

(iii) Hypothesize the expected signs of the coefficients

(iv) Collect the data, inspect and clean the data

(v) Estimate and evaluate the equation

(vi) Document the results

Question 4

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 (a) They are 2800 households in the sample which is given by ∑

(b) Given Y=B0 +B1X1 + Ɛ

Solving for B1= = = 0.7550

Solving for B0= Y- B1 X1

= 22.6-0.755(11.2)

= 14.144

Y = 14.14+0.755X

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B0 is the estimated constant which could be called the y-intercept. While B1 is the
estimated slope coefficient of the equation.

=1 -

= 1-

=0.90.

The explanatory variable are a good fit because 40% of the variation in Y is explained
by the Xs.

Section B

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Question B1

(a). When it comes to the interest rate using the rule of 2 we fail to reject the null
hypothesis although the test statistic carries the correct negative sign but interest rates
when coming to the determination of money supply they are statistically insignificant.
This could be because both money supply and interest rates are the proprieties
(instruments) of monetary policy with the authorities being able to control the supply of
money which is done through the accommodation policy while on the other hand the
authorities control interest rate via the repo rate, therefore the direct influence of one
over the other may be limited.

Using the rule of 2 on the test statistic of GDP we reject the null hypothesis. Plus the
test carries the correct expected sign. This means GDP is statistically significant when it
comes to explaining the changes in money supply. Theoretically changes in GDP would
refer to changes in money supply via inflation effect plus the changes in economic
growth. The overall goodness of fit as measured by the seems to be good enough to
infer that it is a good equation.

(b) The equation seems to carry an irrelevant variable which is interest rate in the
determination of money supply.

(c) Yes it changes the explanations to the answer above. A simple correlation coefficient
of -0.89 shows a strong negative relationship between GDP and interest rates. This
means multicollinearity exists with a variance inflation factor exceeding S that means
the multicollinearity is severe

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We could drop the redundant variable in this case interest rate since it is statistically
insignificant, we could increase the sample size to see if multicollinearity fades away

Question B2

(a) Bpc is the coefficient which estimates the substitute for keep; it implies that a R1
increase in the price of chicken results in a 1.4kg decrease in the quantity of beef.
Although this is theoretical incorrect showing signs of statistically insignificance.

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(b) T-value is calculated as = = -2.21

variable Expected sign Ho and Ha T-test Decision

Household Positive Ho! B=0 1.94 We reject Ho (t-

income Ha! B>0 test>t-critically)
Price of Beef Negative Ho! B=0 -2.21 We reject Ho (t-
(PB) Ha! B<0 test<t-critical)
Price of chicken Positive Ho B=0 -0.22 We do not
(PC) Ha! B>0 reject Ho
(unexpected
sign)
Price of Negative Ho! B=0 1.38 We do not
substitutes (PS) Ha! B>0 reject Ho (t-
test<t critical)

(d) This regression equation has been incorrectly specified when it comes to PC
therefore PC should be dropped or its coefficient specified. Multicollinearity is present in
the independent variables with both PS and PC explaining almost the same outlook.

Question 3

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(a)The above equation is a double log. The values of Y and the Xs have been logged so
as to draw a meaning-full inference out of them because naturally the values of Y and X
are relatively too large.

Variable Expected sign Ho and Ha t-value Decision

Log of average Positive Ho! B=0 1.05/0.16=6.56 Reject Ho (t-

income (Y) Ha! B>0 test>t-critical)
Log of current Positive Ho! B=0 0.01/0.11=0.09 Do not reject
income (Y) Ha! B>0 Ho (t-test<t-
critical)
Log of the rate Negative Ho! B=0 0.75/0.07= 10.7 Do not reject
of interest (IR) Ha! B<0 Ho (unexpected
sign)

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This econometric problem carries multicollinearity and misspecification of equations

(d) Yes they are problems related to Y which have been caused by multicolinearity
between the log of average income and the log of current income. Again serial
correlation exists between the log of the rate of interest and the log of average income.

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MAY/JUNE 2015

Question 1

(a) (i) Zero mean error means the mean is normally distributed and most of its variables
are located to zero

(ii) Homoscedastic errors means the variance of the error terms are constant over a
wide variety of samples.

Both assumptions ensure that the regression equation BLUE.

(b) May June 2016

(c) May June 2016

Question 2

(a) October November 2015 Question (a) and May June Question 1 (a)

(b) Constant term is the y-intercept which represents. Bo sometimes it may be

insignificant economically however in cases it may be important in areas like the cost
function due to this it is not tested

(c) The theorem focuses on the classical assumption I and VI with 4 concepts paid
attention to:

 Unbiased
 Minimum variance
 Consistent
 Normal distribution

Question 3

(a)

 Hypothesis testing is used to check for statistical difference

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  Ho is what the researcher does not expect
 Ha is what the researcher expects
 Critical value is used to draw up the rejection rigor usually given a 45%
confidence interval
 Degrees of freedom give us the extent to which our t-distribution can be allowed
to vary
 Type I error: we reject a true null hypothesis
 Type II error: we fail to reject a false null
 One sided used when we are sure of the expected sign
 Two sided used when we are not sure of the expected sign

(b) V I F Cooks at the extent to which a given explanatory variable can be explained by
all the other explanatory variables in the equation

(c) October November 2015 Question 3 (c)

Question 4

(a)

 Linear multivariate regression is an equation which contains more than one

independent variable Y= Bo+B1X1+B2X2+B3X3+Ɛ
 Linear univariate regression is an equation which contains only one explanatory
Y=Bo+B1X1+Ɛ
 Steps for regression May June 2016 Question 3 (c)

(b) October November 2015

(c)

 Dummies are used when equation is qualitative

 Assumes value of 1 and 0
 When the dummy is under question we make it 1 otherwise 0

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  We introduce m-1 dummies with the other dummy being the slope dummy

Section B

Question B1

(a) May June 2016 Question B3 (b)

(b) October November 2015 Question B2 (a)

(c) The equation might contain multicollinearity because GDP and GDP R(-1) may be
one and the same thing but on of 0.979 implies that 97.9% of changes in Y is
explained by the independent variables which implies an overole good fit of the model.

Question B2

(a) October November 2015 Question B3 (e)

(b)

Variables Expected Ho and Ha t-value Decision

sign
Degree of Freedom n-k-1- 1.699
Price of pork Negative -0.017415/0.004992=-3.489 Reject Ho
Price of beef Positive 0.006684/0.00287=2.33 Reject Ho (t-
test>t-
critical)
Income Positive 0.271935/0.290844=0.935 Do not reject
Ho (t-test
<critical)
KG Pork Positive 3.100112/0.234566=13.2164 Reject Ho (t-
test >t-

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 critical)
Degrees of Freedom KV2 n-k-1=2.045
D1 Unsure 0.007147/0.099133=0.721 Do not reject
Ho (t-test<t-
critical)
D2 Unsure 0.004917/0.135639=0.036 Do not reject
Ho (t-test<t-
critical)

Question B3

 Linear: GDP=19000+1.285 (M3)

A 1% increase in M3 leads to a 1.3% increase in GDP

Change in Y divided by Change in X

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  Double log: log (GDP)=0.63+0.682(log(M3))

Slope:

Percentage change in Y is brought about by a percentage change in X

 Semi (log Y)! Log(GDP)=15.68+0.00000275(M3)

A one unit change in M3 leads to a log (0.00000275) change in GDP

Slope:

A unit change in M3 leads too percentage change in GDP

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  Semi log (logX): GDP=356214+47254 (log(M3))

A percentage increase in M3 leads to a 5.67 unit increase GDP

Slope=

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OCTOBER/NOVEMBER 2015

Question 1

(a) Econometrics use regression analysis to make qualitative estimates of economic

relationship that previously have been completed theoretically in nature. 3 inputs are:
economic theory, statistics and economic data

(b) Usually the population size is too large or unknown therefore estimating the true may
be difficult. We use the estimate to draw inference about the true linear regression
equation. The true is represented by B while the estimated is represented by B. B-B=0
which means the estimated B is an unbiased estimator of the true B.

(c) r measures the strength of the relationship between two variables. It runs from -1 to
1 with -1 showing a strong negative relationship and +1 showing a strong positive
relationship. measures the goodness of fit of the whole equation.

Question 2

(a) Discrete random variable has a countable number of possible values. Each outcome
can only take one possible number as the outcome. When throwing a dice you can only
get a number between 1 and 6, one cannot get 2.5. The probability of getting any
number when throwing a dice randomly P(X) = , there is a one in six chances that the

dice will drop on 4.

(b)

 Stochastic error term is the value which captures the variations in Y not
explained by the changes in X
 May June 2016 Question # (a)
 October November 2015 Question 1 (b)

Question 3

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(a) A linear functional relationship between 2 or more independent variables that is so
strong that it can significantly affect estimation of the coefficients of the variables. It
could be detected using high simple correlation coefficients and high variance inflation
factors (V I Fs)

(b) May June 2016 Question 1 (b) (iii) and (c) combined

(c) Irrelevant variables lead to (i) No bias (ii) then increase the variance. Both for the
dependent and independent variables

(d) Histograms are used to read statistical outcome of the data. Usually most economic
data are normally distributed the histogram would be well shaped. However discrete
random variables which have an equal probability usually follow a uniform distribution
and the histogram will be equal throughout.

Question 4

(a) Pure serial correlation occurs when classical assumption IV which assumes
uncorrelated observations of the error term is violated in a correctly specified equation.
Impure social correlation is caused by a specification error such as an omitted variable
or an incorrect function form.

(b) Given the following hypothesis

Ho! The dependent is innocent

Ha! The dependent is guilty

Type I error: sending an innocent dependent to jail

Type II error: freeing guilty dependent

(c) B1= = = 0.523

Bo= Y-B1X1 = 5.9-0.523(11.6) = -0.1668

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The y-intercept us -0.1668 while the slope is 0.523.

Section B

Question B1

(a) A 1% increase in total household income will lead to a 0.036 kilowatt decrease in
electricity consumption by households. A 1% increase in the average price of electricity
leads to a decrease in electricity consumption by 10%.

(b)

Variable Expected sign Ho and Ha T-value Decision

Degree of Freedom Txn-k-1 1.667
Inc Positive Ho! B=0 -0.36/0.012=-3 Do not reject
Ha! B>0 Ho (unexpected
sign)
Hh Positive Ho! B=0 2.25/0.25=9 Reject Ho t-
Ha! B>0 test>t critical
Pop Positive Ho! B=0 -0.04/0.03=-1.3 Do not reject
Ha! B>0 Ho (unexpected
sign)
Price Negative Ho! B=0 -10/2.5=-4 Reject Ho t-
Ha! B<0 test<t critical

Season Unsure Ho! B=0 0.24/0.08=3 Reject Ho t-

Ha! B≠0 test>t critical

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(c) Reject Ho if t-test > t- critical. Do not reject Ho if t-test ≤ t- critical. T test = 245. T
critical = 2.35. Reject Ho because t-test>t-critical.

(d) Electricity has been log alongside income on total number of residence. Advantage
is that correct inference can be drawn from the logged numbers since the absolute
original values may be too large to draw inference.

Question B2

(a) Positive serial correlation may be present because total population of area (pop) and
total number of household resident within area are SD are related.

Positive serial correlation:

Ho! p≤0

Ha! p>0

DW-d= 2.08

dl= 1.49

du= 1.77

Do not reject Ho because d>du

(b) When C is omitted

Qt=Bo+BaAt+BbBt+Ɛ** where the Ɛ** contains the omitted variable which could then be
written as Ɛ**= Ɛi-BcCt

(c)

 Form 1 is a semi log

 The alternative function for Form 1 is Y= a
 Y increases at a geometric rate with respect to X given by
 Y could be the years of experience while X is the salary
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  Form 2 is a double lag
 Alternative function for Form 2 is
 A 1% change in X will cause a b% change in Y
 When both variables change in percentages where Y is electricity consumption
and X is income of households

Question B3

(a) They is slope dummy variable and the coefficient dummy variables

(b) The first quarter dummy variable serves as the benchmark dummy variable which is
the y-intercept

(c) Yes it makes sense because during the last quarter of the year it is the coldest
season in the USA therefore little or no sales are made

(d) Frig= 456.2 + 2.7 (500) + 2.42.5 (0) + 325.1 (1) – 86.1(0)

Sales for the 3rd quarter where 2132 fridges

(e) This is a double lag with elasticity of

The production function shows the percentage change in output brought about by
production change in capital input, technology and technology input.

The advantage is that absolute output maybe large to draw inference therefore it is
important to log it

(f) Y=a+bX used where Y and X change in real values

Log (Y)= log (a) + b log (X) where percentage change in Y is caused by percentage
change in X

(g) measures well variables of linear form when transformed variables may have a
small which means poor fit however this cold ne am incorrect indication.

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