Watchdog
Watchdog.pk
🏠 Home
Twitter
Facebook
❓ About
Research
Research
📃 Articles
🔬 Corporate/Financial Research and Analysis
🔬 Economic/Social Research and Analysis
🔬 Political Research and Analysis
Economy
Economic/Social Indicators
🌐 Economic/Social Data and Indicators
🤞 Central Government Debt
🚢 International Trade (Imports/Exports)
🏭 Manufacturing of Goods
💸 Workers Remittances from Abroad
💵 Foreign Exchange Reserves (SBP and Banks)
🛒 Prices and Sensitive Price Index (SPI)
Finance
Finance
📈 PSX Stock/Share Position And Analysis (Companies)
📈 PSX Stock/Share Position And Analysis (Others)
💱 Exchange Rates Data and Analysis
💰 Market Treasury Bills
Corporate
Corporate
🆔 Company Profile
📃 Financial Statements and Ratio Analysis
📝 Narrative Financial Analysis
📒 Corporate Accounting/Financial Information and Analysis
📔 Corporate Accounting/Financial Information and Analysis (Comparative)
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
As currency depreciates, inflation rate increases. In other words, with an increase in percentage change in nominal interest rate, inflation differential also tends to increase.
Theory suggests that there is a possitive correlation between nominal exchange rate and inflation.
After applying the theory on actual data, the results affirm that currency depreciation causes increase in inflation and vise versa.
Year
Inflation_Rate_PAK
Inflation_Rate_USA
Nominal_Exchange_Rate
1960
6.94736
1.45797
4.76190
1961
1.64041
1.07072
4.76190
1962
-0.51646
1.19877
4.76190
1963
1.45648
1.23966
4.76190
1964
4.17958
1.27891
4.76190
1965
5.56863
1.58516
4.76190
1966
7.22762
3.01507
4.76190
1967
6.81139
2.77278
4.76190
1968
0.17062
4.27179
4.76190
1969
3.18698
5.46238
4.76190
1970
5.34984
5.83825
4.76190
1971
4.73069
4.29276
4.76190
1972
5.18323
3.27227
8.68138
1973
23.07008
6.17776
9.99424
1974
26.66303
11.05480
9.90000
1975
20.90450
9.14314
9.90000
1976
7.15832
5.74481
9.90000
1977
10.13296
6.50168
9.90000
1978
6.13869
7.63096
9.90000
1979
8.26704
11.25447
9.90000
1980
11.93823
13.54920
9.90000
1981
11.87991
10.33471
9.90000
1982
5.90352
6.13142
11.84746
1983
6.36203
3.21243
13.11697
1984
6.08716
4.30053
14.04633
1985
5.61483
3.54564
15.92839
1986
3.50641
1.89804
16.64750
1987
4.68121
3.66456
17.39880
1988
8.83793
4.07774
18.00329
1989
7.84426
4.82700
20.54149
1990
9.05213
5.39795
21.70737
1991
11.79127
4.23496
23.80076
1992
9.50904
3.02881
25.08279
1993
9.97366
2.95165
28.10718
1994
12.36819
2.60744
30.56659
1995
12.34357
2.80541
31.64268
1996
10.37380
2.93120
36.07868
1997
11.37549
2.33768
41.11152
1998
6.22800
1.55227
45.04666
1999
4.14263
2.18802
49.50069
2000
4.36666
3.37685
53.64818
2001
3.14826
2.82617
61.92716
2002
3.29034
1.58603
59.72378
2003
2.91413
2.27009
57.75199
2004
7.44462
2.67723
58.25786
2005
9.06332
3.39274
59.51447
2006
7.92108
3.22594
60.27133
2007
7.59868
2.85267
60.73851
2008
20.28612
3.83910
70.40803
2009
13.64776
-0.35554
81.71289
2010
13.88113
1.64004
85.19381
2011
11.91676
3.15684
86.34338
2012
9.68505
2.06933
93.39519
2013
7.68950
1.46483
101.62889
2014
7.19167
1.62222
101.10008
2015
2.53951
0.11862
102.76927
2016
3.75259
1.26158
104.76911
2017
4.08537
2.13011
105.45516
2018
5.07805
2.44258
121.82406
2019
10.57836
1.81221
150.03625