ECOM20001 Intro Econometrics assignment 代写
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ECOM20001 Intro Econometrics assignment 代写
ECOM20001 Intro Econometrics
ECOM20001 Intro Econometrics Semester 1, 2017
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Assignment 1 Due Friday April 28 th 1:00PM.
This assignment is evaluated for a total of 30 marks. It is worth 10% of your final mark. No late
assignments will be accepted. Please submit it electronically using from LMS. This is an individual
project each person is expected to do their own assignment. It is due by 1pm on Friday the 28 th of April.
Make sure to use the table below to record your name and student id, your tutor’s name and tutorial
time/location.
This assignment has two parts the first is worth 10 marks and the second is worth 20. The first is
a question taken from last year’s final examination. The second requires that you use the data
(UN_HDI.wf1)for the assignment to perform some estimation and interpretation.
Please limit your total response to no more than 10 A4 pages. You may cut and paste the Eviews
output in your file. In most cases any figures will probably need to be reduced in size. Make sure to
keep a copy of what you submit and include your full name, ID number, your tutor’s name, the
time/day/location of your tutorial as shown below:
Name ID number Tutor Tutorial day &
time
Tutorial
location
ECOM20001 Intro Econometrics Semester 1, 2017
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1. Question from last year’s exam (10 marks total)
Show all your workings in answering these questions.
1. Two specifications of an equation that explains the expenditures per adult on EGMs (pokies) for
the fiscal year 2012/2013 (exp_per_adult) in terms of the number of EGMs per 1000 adults in 2013
(EGM) and the unemployment rate (unemployed) for a sample of 70 towns in Victoria, Australia were
estimated. The results are reported in Table 1 with standard errors in parentheses.
Table 1 The estimated parameters and standard errors for the two specifications
ExplanatoryVariables Specification 1: Specification 2:
Constant -108.06 (67.527) -218.83 (75.725)
EGM 44.87 (7.430) 99.71 (20.972)
EGM 2 -3.71 (1.334)
unemployed 56.98 (10.677) 45.36 (11.004)
2
R
0.542 0.583
Questions:
1.a.(2 marks) Discuss which specification you prefer. In your discussion you must use the
results of at least two criteria.
1.b(2 marks) Estimate the expenditures per capita of 200 EGM per 100 people on the predicted
exp_per_adult for specification 1.
1.c(2 marks) Estimate the expenditures per capita of 200 EGM per 100 people on the predicted
exp_per_adult for specification 2.
1.d. (2 marks) From specification 2, what can we conclude about the returns to scale of more
EGMs?
1.e. (2 marks) From specification 2, how many EGMs per capita would there have to be in a town
to have negative returns to scale?
ECOM20001 Intro Econometrics Semester 1, 2017
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2. The Productive and Fair Econometrician (20 marks total)
Recently the United Nation Development Programme released their latest Human Development
Report. 1 In this assignment you are asked to run some regressions to determine the influence of various
measures of human development such as measures of education and health, inequality and economic
activity to examine the relationship between some of these measures. In order to perform this analysis a
selection of data from the World Bank was combined with the UN data is contained in UN_HDI.wf1. 2
Table 2. Descriptive statistics for variables in UN_HDI.wf1
mnemonic Description Mean Max Min SD. Obs
ARTICLES Scientific articles per 1000 population 6.588 49.480 0.016 10.85 134
CO2 Carbon dioxide emissions per capita, (tonnes), 2011 4.684 43.893 0.022 6.293 187
EN_SEC Secondary enrolment, (% of sec school–age population), 2008–2014 79.9 135.5 15.9 26.6 178
EN_TER Tertiary enrolment, (% of ter school–age population), 2008–2014 36.07 116.62 0.81 27.35 167
EQ_MATH Performance of 15-year-old students, Mathematics, 2012 471 613 368 55 63
EQ_READING Performance of 15-year-old students, Reading, 2012 473 570 384 47 63
EQ_SCIENCE Performance of 15-year-old students, Science, 2012 477 580 373 51 63
EQ_SEC Population with some secondary education, (% ages 25 and older) 57 100 2 29 156
FER_2010 Total fertility rate, (births per woman),2010/2015 2.875 7.580 1.130 1.418 183
GDP_CAP Gross domestic product (GDP)Per capita (2011 PPP $), 2013 17005 127562 584 18867 183
GR_HDI Average annual HDI growth, (%), 1990–2014 0.811 2.889 -0.041 0.511 143
HDI Rank of Human Development Index 2013 94 188 1 54 188
HDI_VALUE Human Development Index, Value, 2014 0.692 0.944 0.348 0.155 188
IMMIGRANTS Stock of immigrants as a % of population, 2013 9.039 83.746 0.060 13.426 188
INEQ_GINI Income inequality, Gini coefficient, 2005–2013 39.16 65.77 24.82 9.16 142
INEQ_PALMA Income inequality, Palma ratio2005–2013 2.070 7.979 0.849 1.317 142
INEQ_QUIN Income inequality, Quintile ratio, 2005–2013 8.569 40.239 3.445 5.626 142
INTERNET Communication, Internet users, (% of population), 2014 44.207 98.160 0.990 29.104 185
LIFE_EXP Life expectancy at birth 62.184 76.045 41.452 8.191 134
MED_AGE Population, Median age, (years), 2015 28.757 46.543 14.957 8.680 183
MORT_INF Mortality rates, (per 1,000 live births), Infant, 2013 25.5 107.2 1.6 23.7 186
POP Population, Total, (millions), 2014 38.2 1393.8 0.0 141.7 188
POP_URBAN Population, Urban, (%), 2014 56.6 100.0 11.8 23.2 183
PRIS_POP Prison population, (per 100,000 people), 2002–2013 168 716 16 132 185
PRODUCTIVITY Labour productivity, Output per worker, (2011 PPP $), 2005-2012 33500 149978 1675 28440 135
R_N_D Research and development expenditure(% of GDP), 2005–2012 0.915 4.039 0.013 0.975 116
TOURISTS Human mobility, International inbound tourists, (thousands), 2013 5709 84700 0 12002 187
WOMEN_MPS % of seats in parliament held by women, 2014 20.624 57.547 0.000 11.601 185
In answering this question do not try to fit numerous alternative models. Estimate only a few and
answer the question. In some cases there are no single “right” answers – you are evaluated on your
interpretation of what you find.
Just as in the case of real applications (this is actual data), not all variables have the same number
of observations. Some countries do not have data for all variables as noted in the Obs column in Table 2.
When EViews runs a regression it is based on the countries (observations) for which all the variables are
non-missing, thus you should check this when using different variables when the numbers of observations
vary.
1 See http://hdr.undp.org/en/2016-report for details.
2 See http://data.worldbank.org/ for details.
ECOM20001 Intro Econometrics Semester 1, 2017
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Questions to be answered using the UN_HDI.wf1 data set. (20 marks in total)
2.a. (1 mark) The Kuznets Curve is a widely cited relationship between income and inequality. Whereby it
is hypothesised that as income increases inequality increases up to a point but after that point
the inequality decreases with higher income, thus higher income countries have lower
inequality than lower income countries. This phenomenon implies that the function between
inequality (as dependent variable) and the log of income (as independent variable) will
exhibit an inverted U-shape. Using the log of the measure of income (GDP_CAP) examine
the relationship between the three measures of inequality (INEQ_GINI, INEQ_QUIN,
INEQ_PALMA) 3 in this data series using scatter plots.
2.b (1 mark) What can you conclude about the existence of the existence of the Kuznets Curve from the
plots you generated in part 2.a?
2.c (3 marks) Follow up the descriptive analysis you performed in 2.a and 2.b with the estimation of three
regressions where you allow for the inverted U-shaped relationship with a quadratic
specification in the log of GDP_Cap. 4
2.d (3 marks) From these regressions locate a limiting value of the income where the relationship between
inequality and income reverses as Kuznets proposed.
2.e (2 mark) Is this “turning point” the same for the three different measures of inequality? Propose
reasons why would these turning points might differ between the models?
2.f (1 mark) Labour productivity is considered an important measure of the health of an economy.
Recently, the impacts of immigration to different countries have become a hot issue for
political debate. Estimate a model of the log of labour productivity as measured by the log of
output per worker (PRODUCTIVITY) variable with the % of the population that are
immigrants (IMMIGRANTS), the % of tertiary aged people enrolled in tertiary institutions
(EN_TER) and the % of the population that live in urban areas (POP_URBAN) as the
explanatory variables.
ECOM20001 Intro Econometrics assignment 代写
2.g (3 marks) Interpret these parameter estimates found in part 2.f and draw conclusions as to the impact of
these regressors in explaining productivity.
2.h (1 mark) Using the number of scholarly articles per capita (ARTICLES), as a measure of a society’s
education and the capacity for non-subsistence effort, estimate a model with this measure as
the dependent variable and the % of tertiary aged people who are attending tertiary
institutions (EN_TER), the % of the population that have access to the internet
(INTERNET), and a social measure such as the % of members of parliament that are women
(WOMEN_MPS).
2.i (5 marks) Interpret your findings and establish if these variables influence the number of articles only
in a monotonic way. In addition, suggest one or two alternative regressors that could be
included in this model. (don’t attempt to use more than 5 other variables). Make sure to
provide reasons as to why they could be used.
3 You can find definitions of these measures on line.
4 Note that a typical quadratic regression of x on y would be specified in EViews as y x x^2 c for the regression
2
1 2 3
y x x .
ECOM20001 Intro Econometrics assignment 代写
