EC114 Introductory Macroeconomics Applications Lecture 3

Introduction

Midterm-related material.
Exam-related material.
GDP-related material.

But first: Enhancement Week

Come and play Settlers, 11:30am-2pm Monday, 12-2pm Tuesday.

But second…

Can you be filling in this form: http://goo.gl/forms/CeLshAJxzY

Midterm-related

Sample midterm examination: http://rpubs.com/jjreade/ec114_sample_ex
- Note: not a mock!
- I will provide guideline answers next week.
1 hour exam:
- Same format as final exam: 3 sections.
- Sec. 1: 30 marks, spend $60(30/100)=$18 minutes here.
  - 10 definitions.
- Sec. 2: 35 marks, spend $60(35/100)=$21 minutes here.
  - Choice of 1 from 3.
- Sec. 3: 35 marks, spend $60(35/100)=$21 minutes here.
  - 1 questions which you must answer.
  - On economic facts and economic theories. See lecture 1.

Final-exam related

Mock exam available: http://rpubs.com/jjreade/sample_exam
Extra questions: http://rpubs.com/jjreade/additional_qs
Final exam is two hours in length.

GDP-related material

## [1] 0.8202618

My forecast was much too high.
The consensus forecast was too high: fxstreet says it was 0.6%.

GDP-related material

Joe Grice next week may talk a little more about GDP…
This is the “flash” estimate.
- See http://www.ons.gov.uk/ons/rel/gva/gross-domestic-product--preliminary-estimate/q4-2014/stb-gdp-preliminary-estimate--q4-2014.html
- Done via output method, so no regional breakdown yet.
- All flash estimates get revised, question is how much…

But first: the reaction…

Now some detail…

Reflection in the Media…

Measurement Error?

Do we care about measurement error?
We can think systematically about it…
If we can assume that, on average, it’s zero and has constant variance:
- As in, errors $e_t \sim (0,\sigma^2)$.
- Then on average, errors random, noise, over longer term can be ignored.
If, however, there is serial correlation, if mean non-zero:
- Suggests there are systematic errors being made.
Can get GDP revisions (full) history:
- http://www.ons.gov.uk/ons/guide-method/method-quality/specific/economy/national-accounts/gdp-revisions-triangles-and-real-time-database/index.html

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   -0.04   -0.01    0.00    0.00    0.01    0.04   68846

Distribution of revisions from first release appears stable.
- What is likelihood of different sizes of adjustment?

revisions <- as.numeric(as.vector(t(gdpr.g1[-1,-1])))
revisions <- revisions[is.na(revisions)==F]
pcs <- data.frame("percentiles"=quantile(revisions,seq(0,1,0.01)))
library(knitr)
kable(pcs, format = "markdown")

	percentiles
0%	-0.0391679
1%	-0.0272120
2%	-0.0230263
3%	-0.0203457
4%	-0.0179335
5%	-0.0159794
6%	-0.0149120
7%	-0.0144557
8%	-0.0136929
9%	-0.0129356
10%	-0.0122505
11%	-0.0118399
12%	-0.0113726
13%	-0.0108127
14%	-0.0102547
15%	-0.0096976
16%	-0.0091805
17%	-0.0086457
18%	-0.0081625
19%	-0.0078756
20%	-0.0075355
21%	-0.0070509
22%	-0.0065821
23%	-0.0062307
24%	-0.0057092
25%	-0.0052178
26%	-0.0048487
27%	-0.0045424
28%	-0.0042716
29%	-0.0039756
30%	-0.0036748
31%	-0.0033048
32%	-0.0030348
33%	-0.0027570
34%	-0.0025005
35%	-0.0021907
36%	-0.0019497
37%	-0.0016368
38%	-0.0013289
39%	-0.0010441
40%	-0.0007798
41%	-0.0005221
42%	-0.0002947
43%	-0.0000820
44%	0.0000000
45%	0.0001888
46%	0.0003889
47%	0.0005767
48%	0.0007945
49%	0.0009589
50%	0.0011102
51%	0.0012934
52%	0.0015093
53%	0.0017026
54%	0.0019898
55%	0.0022062
56%	0.0023460
57%	0.0025110
58%	0.0027470
59%	0.0029331
60%	0.0031541
61%	0.0033199
62%	0.0035536
63%	0.0037157
64%	0.0039335
65%	0.0041517
66%	0.0043267
67%	0.0045760
68%	0.0048992
69%	0.0051691
70%	0.0054170
71%	0.0057042
72%	0.0060557
73%	0.0064294
74%	0.0068203
75%	0.0071686
76%	0.0074656
77%	0.0080468
78%	0.0084892
79%	0.0088702
80%	0.0092537
81%	0.0097983
82%	0.0106153
83%	0.0113474
84%	0.0120416
85%	0.0128854
86%	0.0136546
87%	0.0145962
88%	0.0152511
89%	0.0162666
90%	0.0171879
91%	0.0183868
92%	0.0195124
93%	0.0207788
94%	0.0218054
95%	0.0235918
96%	0.0259986
97%	0.0276709
98%	0.0296740
99%	0.0327496
100%	0.0436356

From looking at the distribution:
- 43% chance of downward revision, 57% chance of upward.
- Only about a 1–2% chance of revision of +0.03.
- About 6–7% chance of revision >+0.02.
- Biggest ever negative revision 0.04, so no chance of negative growth.
Hence more than 50/50 chance consensus was right all along…

GDP, but Well-Being?

Point: It’s remarkable how much is being measured these days.
- If we care about wellbeing, there is plenty of material out there.
- http://www.ons.gov.uk/ons/rel/wellbeing/measuring-national-well-being/analysis-of-social-well-being--social-capital--in-the-uk---2013-14/art-measuring-national-well-being---an-analysis-of-social-capital-in-the-uk.html
If we wish to dismiss the importance of wellbeing, we need good reasons why.

Inequality is Good?

If we wish to dismiss inequality as a problem, we also need good reasons why.
Basic micro: We’re all paid our marginal revenue product.
- We’re not all equally productive, neither should our pay be.
But can labour move freely between sectors, regions and countries?
- Reality is MRP varies between sectors, regions, countries, yet we can’t necessarily move.
Additionally, does economic power enable some to extract rents?
- Extract higher pay than their MRP suggests, and suppress pay of others?
More tomorrow:
- What labour market impediments/regulations decrease likelihood of getting job?
- Do they contribute to inequality? Is that a good thing?

Rounding Up

GDP growth: Up and down, rebalanced, settled?
Inequality and the labour market - to be continued.
Forecasting competition this week is oil prices: http://goo.gl/forms/9GyvB6rkJf

EC114 Introductory Macroeconomics Applications Lecture 3

J James Reade

26/01/2015

Introduction

But first: Enhancement Week

But second…

But first: the reaction…

Now some detail…

Reflection in the Media…

Measurement Error?

GDP, but Well-Being?

Inequality is Good?

Rounding Up