Imports (m$) IP CPI Exchange Rate(yen/dollar) 42450 61.3182 138.300 125.4614 424
ID: 1141749 • Letter: I
Question
Imports (m$) IP CPI Exchange Rate(yen/dollar) 42450 61.3182 138.300 125.4614 42447 61.7969 138.600 127.6989 43066 62.2883 139.100 132.8627 43723 62.7326 139.400 133.5395 44123 62.9558 139.700 130.7710 44637 62.9521 140.100 126.8355 45185 63.4965 140.500 125.8817 45368 63.1787 140.800 126.2310 45495 63.3148 141.100 122.5967 46352 63.8104 141.700 121.1652 46233 64.0769 142.100 123.8800 47449 64.0921 142.300 124.0409 46412 64.4088 142.800 124.9932 46169 64.6457 143.100 120.7595 49751 64.6214 143.300 117.0174 49263 64.8160 143.800 112.4114 48503 64.5884 144.200 110.3430 49034 64.7227 144.300 107.4118 48896 64.9215 144.500 107.6914 48581 64.8955 144.800 103.7650 50286 65.2022 145.000 105.5748 51285 65.6822 145.600 107.0200 50701 65.9480 146.000 107.8765 50514 66.2804 146.300 109.9130 50763 66.5468 146.300 111.4415 51888 66.5809 146.700 106.3011 53651 67.2653 147.100 105.0974 53395 67.6227 147.200 103.4843 54048 67.9940 147.500 103.7533 55758 68.4460 147.900 102.5264 56107 68.5541 148.400 98.4450 57482 68.9440 149.000 99.9404 57752 69.1631 149.300 98.7743 57875 69.7669 149.400 98.3530 59923 70.2129 149.800 98.0440 60047 70.9458 150.100 100.1824 61020 71.0841 150.500 99.7660 60518 71.0389 150.900 98.2368 62429 71.1524 151.200 90.5196 63065 71.1219 151.800 83.6895 63162 71.3093 152.100 85.1127 63682 71.5697 152.400 84.6355 62432 71.2942 152.600 87.3970 62285 72.2334 152.900 94.7383 62968 72.5002 153.100 100.5455 62133 72.3714 153.500 100.8390 62449 72.5587 153.700 101.9400 63230 72.8395 153.900 101.8495 64561 72.3593 154.700 105.7514 64736 73.5136 155.000 105.7880 65148 73.3994 155.500 105.9400 66394 74.0141 156.100 107.1995 67582 74.5297 156.400 106.3423 66094 75.1926 156.700 108.9600 66427 75.0932 157.000 109.1909 67973 75.5821 157.200 107.8659 67967 75.9835 157.700 109.9310 67376 75.9867 158.200 112.4123 68512 76.6111 158.700 112.2958 70342 77.1026 159.100 113.9810 70715 77.1900 159.400 117.9124 71506 78.1257 159.700 122.9621 71988 78.7260 159.800 122.7738 72399 78.7613 159.900 125.6377 72634 79.3048 159.900 119.1924 72263 79.6636 160.200 114.2857 73263 80.1496 160.400 115.3759 73400 81.2117 160.800 117.9295 74311 81.9190 161.200 120.8900 74475 82.4793 161.500 121.0605 73836 83.2123 161.700 125.3817 76004 83.5079 161.800 129.7341 75516 83.9210 162.000 129.5475 74929 83.9956 162.000 125.8516 77222 84.0506 162.000 129.0823 76717 84.3708 162.200 131.7536 76566 84.9363 162.600 134.8960 75214 84.4407 162.800 140.3305 74934 84.0818 163.200 140.7874 76334 85.8655 163.400 144.6800 76586 85.5895 163.500 134.4805 78376 86.2691 163.900 121.0486 78701 86.1935 164.100 120.2895 77542 86.4992 164.400 117.0709 78561 86.8865 164.700 113.2900 80728 87.2633 164.700 116.6684 80361 87.4504 164.800 119.4730 81389 87.6525 165.900 119.7723 83358 88.3035 166.000 121.9995 86298 88.1345 166.000 120.7245 87589 88.7320 166.700 119.3305 88526 89.1042 167.100 113.2268 89546 88.8555 167.800 106.8752 90388 89.9920 168.100 105.9650 92581 90.4420 168.400 104.6485 95064 91.1624 168.800 102.5843 95763 91.2332 169.300 105.2960 98557 91.5783 170.000 109.3885 100805 91.9442 171.000 106.3074 99878 92.5202 170.900 105.6270 99802 92.6592 171.200 108.3205 103512 92.7126 172.200 106.1255 103480 92.4984 172.700 108.2115 103854 92.2519 172.700 108.0804 106946 92.7005 173.600 106.8375 106493 92.2835 173.900 108.4429 105636 92.2721 174.200 109.0095 105841 91.9780 174.600 112.2090 106605 91.3166 175.600 116.6719 101479 90.7666 176.000 116.2337 102982 90.5202 176.100 121.5050 99260 90.2684 176.400 123.7710 96421 89.6126 177.300 121.7682 96076 89.0137 177.700 122.3510 94509 88.6214 177.400 124.4981 93302 88.3938 177.400 121.3670 91754 88.1055 178.100 118.6117 92069 87.6738 177.600 121.4536 90708 87.2008 177.500 122.4055 87301 87.2224 177.400 127.5945 90133 87.7600 177.700 132.6833 92266 87.7249 178.000 133.6426 92249 88.3748 178.500 131.0610 96939 88.7446 179.300 130.7718 97332 89.2026 179.500 126.3750 98775 90.0745 179.600 123.2905 98176 89.7723 180.000 117.8991 100615 89.8958 180.500 118.9927 100288 89.9425 180.800 121.0780 97808 89.6446 181.200 123.9077 102976 90.1355 181.500 121.6079 104374 89.7289 181.800 121.8929 103224 90.3564 182.600 118.8133 102714 90.6904 183.600 119.3379 106436 90.5100 183.900 118.6871 104331 89.7698 183.200 119.8950 103799 89.7575 182.900 117.3681 104701 89.7652 183.100 118.3290 106017 90.0817 183.700 118.6959 103205 89.9743 184.500 118.6624 106860 90.5213 185.100 114.8000 108027 90.4884 184.900 109.4955 108789 91.1730 185.000 109.1778 112122 91.0979 185.500 107.7377 111900 91.3636 186.300 106.2685 115250 91.8804 186.700 106.7079 119791 91.3880 187.100 108.5157 119724 91.7603 187.400 107.6564 121781 92.4272 188.200 112.1960 125702 91.6958 188.900 109.4336 124363 92.4141 189.100 109.4871 125694 92.5783 189.200 110.2336 125819 92.6156 189.800 110.0914 130264 93.5000 190.800 108.7835 133402 93.6866 191.700 104.6990 131801 94.3195 191.700 103.8104 133593 94.7075 191.600 103.3410 136373 95.3015 192.400 104.9442 132396 95.2423 193.100 105.2543 138759 95.3260 193.700 107.1938 137213 95.5000 193.600 106.5952 139048 95.9000 193.700 108.7473 139016 95.7684 194.900 111.9535 140776 95.8884 196.100 110.6065 145565 94.0153 198.800 111.2390 150303 95.1840 199.100 114.8695 148367 96.1125 198.100 118.4540 151008 96.6602 198.100 118.4624 154651 96.7540 199.300 115.4765 150188 96.8319 199.400 117.8605 152490 97.0741 199.700 117.2778 153017 97.4499 200.700 117.0695 156834 97.3416 201.300 111.7305 157147 97.7165 201.800 114.6250 158125 97.7638 202.900 115.7670 161959 97.9660 203.800 115.9243 159776 97.8370 202.800 117.2145 155055 97.7452 201.900 118.6090 155890 97.6316 202.000 117.3205 159962 98.6314 203.100 117.3220 157712 98.1608 203.437 120.4471 156669 99.2559 204.226 120.5047 164389 99.3295 205.288 117.2600 162055 100.0512 205.904 118.9324 163279 100.1109 206.755 120.7732 164954 100.1159 207.234 122.6886 165957 100.1435 207.603 121.4148 165637 100.2699 207.667 116.7335 167180 100.7148 208.547 115.0435 169156 100.2223 209.190 115.8661 173691 100.8053 210.834 111.0729 172163 100.8200 211.445 112.4490 177993 100.4978 212.174 107.8181 182938 100.3008 212.687 107.0300 178511 100.0078 213.448 100.7562 186249 99.2273 213.942 102.6777 186556 98.7652 215.208 104.3595 189761 98.5639 217.463 106.9152 197445 98.1182 219.016 106.8518 189049 96.5457 218.690 109.3624 179356 92.4795 218.877 106.5748 176485 93.2450 216.995 99.9659 152438 92.1643 213.153 96.9656 140829 89.5631 211.398 91.2750 130573 87.5481 211.952 90.1205 123019 86.9871 212.823 92.9158 123049 85.6509 212.523 97.8550 121785 84.9332 212.657 98.9200 119795 84.0667 212.998 96.6445 123321 83.7572 214.791 96.6145 131096 84.4816 214.720 94.3670 130269 85.3714 215.442 94.8971 137597 85.9532 215.880 91.2748 140834 86.2231 216.482 90.3671 144986 86.6394 217.165 89.2674 149166 87.0436 217.365 89.9509 148209 87.9640 217.478 91.1011 152072 88.3021 217.356 90.1395 156289 88.9705 217.380 90.7161 156677 89.2674 217.281 93.4527 159672 90.6754 217.230 91.9730 163770 90.8744 217.329 90.8059 161074 91.4059 217.690 87.5005 165923 91.6491 218.020 85.3727 165072 91.8829 218.319 84.3571 166351 91.5685 218.996 81.7285 166841 91.8155 219.471 82.5180 172057 92.7225 220.468 83.3376 180995 92.6088 221.067 82.6250 177012 92.1748 221.908 82.5368 184268 93.1137 223.106 81.6470 184143 92.5765 223.879 83.1771 187948 92.9126 224.747 81.1257 187253 93.0774 225.070 80.4259 187474 93.6088 225.594 79.2425 186728 94.1212 226.187 76.9657 188575 94.2216 226.753 76.7957 188446 94.7474 226.728 76.6430 189678 94.9598 227.049 77.5595 193297 95.5239 227.137 77.7967 194670 96.1959 227.605 76.9640 188318 96.6672 228.253 78.4700 199515 96.1393 228.950 82.4659 195693 96.8572 228.951 81.2524 193780 97.1042 228.648 79.6668 190058 97.1322 228.924 79.3152 188329 97.5571 228.836 78.9348 187478 96.7850 230.026 78.6909 191136 96.9549 231.227 78.1353 186635 96.8409 231.623 79.0132 194912 98.0321 231.071 81.0305 188891 98.1127 231.137 83.7905 192549 98.0345 231.198 89.0581 192933 99.0731 232.770 93.0016 186489 99.4765 232.340 94.7700 variables: total Problem 2: I have uploaded monthly data (hw xis) on four US macroeconomics imports (ún USD), total industrial production index (ündustrial production is used as a proxy of national income ). US consumer price index, and the exchange rate of the US dollar with yen. () Formulate a log linear regression model for the US import demand function (10) (b) What kind of sign do you expect for each of the partial the slope coefficient of the regression model? (Hünt: Let's say A is the partial slope coefficient of with respect to industrial production. Now if industrial production (which is the proxy of US national income) increases, then what do you expect theoretically to happen to imports? If you expect imports to go up due to an increase in industrial production, then the expected sign of A is positive. On the other hand, if you expect imports to down due to an increase in industrial production, then the expected sign of B is negative. I also want you to explain in details why you think (based on your knowledge of macroeconomics) imports should go up or down due to an increase in industrial production. You need to give similar explanation to other p coefficients.) (10) () Estimate the log linear regression model you have developed and report the results. (10) (d) Interpret the estimated results. VMore specifically interpret the estimated partial slope coefficients of each of the regressor. (10)Explanation / Answer
a) The model will be
log(Import) = 0 + 1*log(IP) + 2*log(CPI) + 3*log(EX)
IP – Industrial Production
CPI – Consumer Price Index
EX – Exchange Rate
b) The coefficient for IP, CPI and EX all are expected to be positive because as National incone increases country’s import increases. IF CPI increases i.e prices at home increases people tend to imort and if EX (yen/dollar) increases implies that yen is depreciating thus we will increase our imports.
c)
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.993104319
R Square
0.986256188
Adjusted R Square
0.986091919
Standard Error
0.05290352
Observations
255
ANOVA
df
SS
MS
F
Significance F
Regression
3
50.41101016
16.80367
6003.921
2.7073E-233
Residual
251
0.702494396
0.002799
Total
254
51.11350456
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-2.752112967
0.257364875
-10.6934
3.09E-22
-3.258982853
-2.245243082
-3.258982853
-2.245243082
log_IP
1.160670255
0.056662227
20.48402
1.74E-55
1.049076252
1.272264258
1.049076252
1.272264258
log_CPI
1.911155908
0.059056645
32.3614
1.51E-91
1.794846195
2.027465621
1.794846195
2.027465621
log_EX
-0.173336163
0.033767905
-5.13316
5.71E-07
-0.239840708
-0.106831618
-0.239840708
-0.106831618
The estimated model is
log(Import) = 2.75 + 1.16*log(IP) + 1.91*log(CPI) + 0.17*log(EX)
d) The coefficient for IP suggests that if IP increases by 1 percent the imports on average will increase by 1.16 percent. The coefficient for CPI suggests that if CPI increases by one percent then on average Import increases by 1.91 percent and the coefficient for exchange rate suggests that as the Exchange rate increases by one percent the imports on average will increase by 0.17 percent.
*p
The model will be
log(Import) = 0 + 1*log(IP) + 2*log(CPI) + 3*log(EX)
IP – Industrial Production
CPI – Consumer Price Index
EX – Exchange Rate
The coefficient for IP, CPI and EX all are expected to be positive because as National incone increases country’s import increases. IF CPI increases i.e prices at home increases people tend to imort and if EX (yen/dollar) increases implies that yen is depreciating thus we will increase our imports.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.993104319
R Square
0.986256188
Adjusted R Square
0.986091919
Standard Error
0.05290352
Observations
255
ANOVA
df
SS
MS
F
Significance F
Regression
3
50.41101016
16.80367
6003.921
2.7073E-233
Residual
251
0.702494396
0.002799
Total
254
51.11350456
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-2.752112967
0.257364875
-10.6934
3.09E-22
-3.258982853
-2.245243082
-3.258982853
-2.245243082
log_IP
1.160670255
0.056662227
20.48402
1.74E-55
1.049076252
1.272264258
1.049076252
1.272264258
log_CPI
1.911155908
0.059056645
32.3614
1.51E-91
1.794846195
2.027465621
1.794846195
2.027465621
log_EX
-0.173336163
0.033767905
-5.13316
5.71E-07
-0.239840708
-0.106831618
-0.239840708
-0.106831618
The estimated model is
log(Import) = 2.75 + 1.16*log(IP) + 1.91*log(CPI) + 0.17*log(EX)
The coefficient for IP suggests that if IP increases by 1 percent the imports on average will increase by 1.16 percent. The coefficient for CPI suggests that if CPI increases by one percent then on average Import increases by 1.91 percent and the coefficient for exchange rate suggests that as the Exchange rate increases by one percent the imports on average will increase by 0.17 percent.
P
The model will be
log(Import) = 0 + 1*log(IP) + 2*log(CPI) + 3*log(EX)
IP – Industrial Production
CPI – Consumer Price Index
EX – Exchange Rate
The coefficient for IP, CPI and EX all are expected to be positive because as National incone increases country’s import increases. IF CPI increases i.e prices at home increases people tend to imort and if EX (yen/dollar) increases implies that yen is depreciating thus we will increase our imports.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.993104319
R Square
0.986256188
Adjusted R Square
0.986091919
Standard Error
0.05290352
Observations
255
ANOVA
df
SS
MS
F
Significance F
Regression
3
50.41101016
16.80367
6003.921
2.7073E-233
Residual
251
0.702494396
0.002799
Total
254
51.11350456
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-2.752112967
0.257364875
-10.6934
3.09E-22
-3.258982853
-2.245243082
-3.258982853
-2.245243082
log_IP
1.160670255
0.056662227
20.48402
1.74E-55
1.049076252
1.272264258
1.049076252
1.272264258
log_CPI
1.911155908
0.059056645
32.3614
1.51E-91
1.794846195
2.027465621
1.794846195
2.027465621
log_EX
-0.173336163
0.033767905
-5.13316
5.71E-07
-0.239840708
-0.106831618
-0.239840708
-0.106831618
The estimated model is
log(Import) = 2.75 + 1.16*log(IP) + 1.91*log(CPI) + 0.17*log(EX)
The coefficient for IP suggests that if IP increases by 1 percent the imports on average will increase by 1.16 percent. The coefficient for CPI suggests that if CPI increases by one percent then on average Import increases by 1.91 percent and the coefficient for exchange rate suggests that as the Exchange rate increases by one percent the imports on average will increase by 0.17 percent.
*PIease upvote if possibIe.
ease upvte if possib
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.993104319
R Square
0.986256188
Adjusted R Square
0.986091919
Standard Error
0.05290352
Observations
255
ANOVA
df
SS
MS
F
Significance F
Regression
3
50.41101016
16.80367
6003.921
2.7073E-233
Residual
251
0.702494396
0.002799
Total
254
51.11350456
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-2.752112967
0.257364875
-10.6934
3.09E-22
-3.258982853
-2.245243082
-3.258982853
-2.245243082
log_IP
1.160670255
0.056662227
20.48402
1.74E-55
1.049076252
1.272264258
1.049076252
1.272264258
log_CPI
1.911155908
0.059056645
32.3614
1.51E-91
1.794846195
2.027465621
1.794846195
2.027465621
log_EX
-0.173336163
0.033767905
-5.13316
5.71E-07
-0.239840708
-0.106831618
-0.239840708
-0.106831618