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Machine Learning
Risk Management
Expected Credit Loss Model (ECLM) for Agriculture Sector of Pakistan
Expected Credit Loss Model (ECLM) Demonstration through TensorFlow
KSE-100 Index Estimate through Machine Learning
Economy
Estimating GDP Growth of Pakistan through Machine Learning
Population Estimate through Machine Learning
Economics says that there is direct possitive correlation between income and spending. That is as income increases, so does the spending and vise versa. The more money people make, the more they spend on purchasing goods and services.There is a model by
John Maynard Keynes
which suggests that "
In a state of unemployment and unused production capacity, one can
only
enhance employment and total income by
first
increasing expenditures for either consumption or investment.
"
In "Equilibrium in the Income-Expenditure Model", it is suggested that macro equilibrium occurs at the level of GDP where national income equals aggregate expenditure. That is, when Income equals Expenditures.
On Macro level let us examine the relationship of Income and Expenditure by studying the Gross National Income and Gross National Expenditure data of Pakistan.
You must understand that Pakistan also borrows therefore consumption can be more than its income. In addition, the basic assumptions of economics such as "other factors remaining constant" do not apply to the actual data since other factors do change.
Year
GrossNationalIncome
GrossNationalExpenditure
1967
7586308273.83452
8065807853.84292
1968
8186686266.27468
8544078916.42167
1969
8808273834.52331
9081918017.63965
1970
10142377152.45700
10719074254.51490
1971
10767114657.70680
11200822742.54510
1972
9362321336.31824
9793944653.00499
1973
6499196065.44973
6500513742.55178
1974
9043636363.63636
9395630393.93939
1975
11434444444.44440
12648500232.32320
1976
13567878787.87880
14492585212.12120
1977
15810303030.30300
16598759686.86870
1978
19196262626.26260
19466400525.25250
1979
21313535353.53540
22085680212.12120
1980
25601212121.21210
26440694080.80810
1981
30392726727.27270
31106006414.14140
1982
33128721601.89570
34357446009.47870
1983
31793858937.00790
31864943267.71650
1984
34090011064.11400
34751650608.48920
1985
33672031372.03170
35004034498.68070
1986
32789275641.66150
35333278834.46990
1987
35470840669.96800
35942540547.62600
1988
40130104312.16180
41582786590.71490
1989
41632528799.63680
43330007671.55310
1990
41735288201.74820
43144393946.61800
1991
46520751747.93910
46161373674.51670
1992
49141098847.88440
50176550109.96550
1993
51862932126.41730
54636187401.52620
1994
52027351813.86940
53328125363.58830
1995
61092371435.53500
62280965147.45310
1996
63107002275.75460
66184676586.00980
1997
61935800691.61450
65360397999.71720
1998
61626071021.28220
62840084822.33070
1999
62426345308.53850
63989601438.48200
2000
73025445759.84540
74874530079.72940
2001
71373943661.97180
73070766059.77330
2002
72691117245.85900
72372182526.79440
2003
85836776506.74410
82750793921.80300
2004
100139968733.71500
96964999131.49210
2005
111767372670.05400
113744541558.28400
2006
139768396467.55700
147440549580.16300
2007
154985420769.13700
162384050585.31300
2008
173417985019.14600
188496526584.22100
2009
172564093744.70500
180394874793.46700
2010
184163841544.98100
187761012008.11000
2011
223172963541.66100
224280807629.87500
2012
235976451836.40500
242361632992.05400
2013
243216656337.79500
246893548587.01000
2014
258227372243.28500
260039272416.58900
2015
287067806084.67400
287995768589.56600
2016
296011975898.69600
298059820074.00600
2017
321596716024.88300
333364939129.10700
2018
329083939984.06100
346671925164.54800
2019
296935209818.72300
306601023870.53000