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Machine Learning
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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
Purchasing Power Parity theory follows the concept of one price. A dollar should have same value in every country. If one product is worth one dollar in country 'A', it should be worth same one dollar in country 'B'. And if there are any differences, market forces will adjust those differences by showing their affect on imports/exports.
Net Exports (Exports-Imports) have inverse correlation with the real exchange rate. The lower the exchange rate, the less expensive are domestic goods relative to foreign goods, thus the greater are our net exports.
If your home currency is stronger than the foreign currency, that means foreign goods are cheaper and therefore net exports will decrease. However if local currency is weak, that means domestic goods are cheaper, therefore exports will increase and imports will decrease causing an increase in net exports.
Let us test this theory on the actual data of Pakistan.
As discovered by the above experiment, the theory does have a point.
Year
Real_Exchange_Rate
Exports
Imports
1980
202.12499
3232901092.57224
6347873098.03869
1981
229.14610
3389497117.86622
6601897629.02912
1982
209.89335
3154729520.61265
6721839746.71199
1983
202.73821
3662621180.44509
6607694675.66819
1984
207.03042
3286530740.03267
7355744358.94291
1985
193.37273
3509101707.26676
7090127076.54375
1986
159.58181
4035982897.93706
7200329462.07479
1987
141.10554
4928011006.59509
7570955417.21271
1988
136.78800
5282184414.62331
8623660010.59930
1989
128.15180
6005657389.31981
9112947789.20635
1990
121.18933
6834726234.04086
10205366734.07660
1991
118.69088
7941736412.83459
10997458784.58850
1992
116.62686
8472573666.60565
12399961066.49240
1993
115.20198
8366368531.48480
12018661354.09040
1994
114.38995
8869455611.72760
11884759296.53890
1995
113.64966
10213602183.15970
14185295078.08500
1996
110.64181
10523484820.74010
15622529174.60080
1997
112.16187
9975895498.67247
13408451099.94280
1998
113.73868
9155000000.00000
11996000000.00000
1999
105.24956
8945000000.00000
11565000000.00000
2000
103.26181
9997000000.00000
12026000000.00000
2001
93.83781
10471000000.00000
11952000000.00000
2002
97.18598
12158000000.00000
12566000000.00000
2003
94.46481
14775000000.00000
15210000000.00000
2004
93.78331
16027010000.00000
22007000000.00000
2005
96.66489
19105000000.00000
29275200000.00000
2006
99.39681
20540000000.00000
35098000000.00000
2007
98.14519
21946000000.00000
37586000000.00000
2008
94.50396
25472500000.00000
47929000000.00000
2009
95.13067
22313000000.00000
35151430000.00000
2010
100.00000
28056000000.00000
40016000000.00000
2011
102.82343
31433000000.00000
47151000000.00000
2012
104.38923
31374000000.00000
48902000000.00000
2013
102.30597
30043000000.00000
49167000000.00000
2014
109.74218
30600000000.00000
51141000000.00000
2015
119.81198
28604000000.00000
48622000000.00000
2016
122.51267
26813000000.00000
51599000000.00000
2017
124.78722
28885000000.00000
63573000000.00000
2018
107.09026
30130900000.00000
67869240000.00000
2019
97.09834
30566250000.00000
56336292000.00000