For this question, create a new column in the dataset where total rainfall is just the sum of our three separate rainfall variables. (Solve this question by Excel)(a) plot crop yield vs. time. Does yield appear to be stationary? Why or why not? (b) plot total rainfall vs. time. Does total rainfall appear to be stationary? Why or why not? (c) plot the first-difference of crop yield vs. time. Does this series appear to be stationary? Why or why not? (d) formally test whether crop yield, rainfall and the first-difference of crop yield are stationary using the appropriate test. Be sure to do all parts of the hypothesis tests. After these tests, what can you say the order of integration is for each of the variables?(e) estimate a model where yield is a function of rainfall and time. You do not have to worry about the time variable being stationary or not, but the other two must be stationary (you might need to difference one or both of them to make it stationary). Fully report your results.(f) test your model for autocorrelation using both a bounds test and an LM test. Be sure to do all parts of the hypothesis test. Dataset:YieldTimeRgRdRf1.350811.7081.4750.9160.947621.5441.5520.4781.256931.5611.6921.0050.914242.6681.4760.6461.102251.1632.1030.3821.189761.5063.2071.3221.068972.9341.2040.6910.900782.6241.0670.5251.223191.3112.8520.4961.2434101.3811.0780.5211.1427111.7871.7770.6810.7998121.0671.7360.3950.8468132.4521.3860.8970.8938142.812.560.7270.9006151.9043.190.631.0955161.8751.7790.9910.988171.2051.5271.0880.9408182.6641.9430.320.8939192.1951.5941.0040.3629201.1750.7560.1211.7111212.0250.9010.7511.5152220.7931.0891.4151.5135230.9881.7880.381.8519241.7821.71.3621.2963251.9441.9960.6361.5072261.1142.0761.0741.5867271.0581.2530.781.3538281.021.4840.761.6757291.4881.480.6281.475301.371.1720.2041.3289311.1921.2940.651.28323.1581.6620.4991.5357331.4191.3060.6061.2079341.8961.870.8261.6154351.5141.220.6521.6497360.7421.5580.4561.6914371.8361.6760.3392.1408381.4641.6680.522.3132391.5221.110.772.2897400.92.0760.8042.0983411.9921.7050.761.9555421.631.860.9061.8192431.2981.5580.892.3881441.9391.2360.4762.2106452.0092.0170.872.4475461.3332.3071.1572.3959470.8921.6340.792.9669481.3761.3580.656

Answered: For this question, create a new column…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Related questions

Question

For this question, create a new column in the dataset where total rainfall is just the sum of our three separate rainfall variables. (Solve this question by Excel)
(a) plot crop yield vs. time. Does yield appear to be stationary? Why or why not?
(b) plot total rainfall vs. time. Does total rainfall appear to be stationary? Why or why not?
(c) plot the first-difference of crop yield vs. time. Does this series appear to be stationary? Why or why not?
(d) formally test whether crop yield, rainfall and the first-difference of crop yield are stationary using the appropriate test. Be sure to do all parts of the hypothesis tests. After these tests, what can you say the order of integration is for each of the variables?
(e) estimate a model where yield is a function of rainfall and time. You do not have to worry about the time variable being stationary or not, but the other two must be stationary (you might need to difference one or both of them to make it stationary). Fully report your results.
(f) test your model for autocorrelation using both a bounds test and an LM test. Be sure to do all parts of the hypothesis test.

Dataset:

Yield	Time	Rg	Rd	Rf
1.3508	1	1.708	1.475	0.916
0.9476	2	1.544	1.552	0.478
1.2569	3	1.561	1.692	1.005
0.9142	4	2.668	1.476	0.646
1.1022	5	1.163	2.103	0.382
1.1897	6	1.506	3.207	1.322
1.0689	7	2.934	1.204	0.691
0.9007	8	2.624	1.067	0.525
1.2231	9	1.311	2.852	0.496
1.2434	10	1.381	1.078	0.521
1.1427	11	1.787	1.777	0.681
0.7998	12	1.067	1.736	0.395
0.8468	13	2.452	1.386	0.897
0.8938	14	2.81	2.56	0.727
0.9006	15	1.904	3.19	0.63
1.0955	16	1.875	1.779	0.991
0.988	17	1.205	1.527	1.088
0.9408	18	2.664	1.943	0.32
0.8939	19	2.195	1.594	1.004
0.3629	20	1.175	0.756	0.121
1.7111	21	2.025	0.901	0.751
1.5152	22	0.793	1.089	1.415
1.5135	23	0.988	1.788	0.38
1.8519	24	1.782	1.7	1.362
1.2963	25	1.944	1.996	0.636
1.5072	26	1.114	2.076	1.074
1.5867	27	1.058	1.253	0.78
1.3538	28	1.02	1.484	0.76
1.6757	29	1.488	1.48	0.628
1.475	30	1.37	1.172	0.204
1.3289	31	1.192	1.294	0.65
1.28	32	3.158	1.662	0.499
1.5357	33	1.419	1.306	0.606
1.2079	34	1.896	1.87	0.826
1.6154	35	1.514	1.22	0.652
1.6497	36	0.742	1.558	0.456
1.6914	37	1.836	1.676	0.339
2.1408	38	1.464	1.668	0.52
2.3132	39	1.522	1.11	0.77
2.2897	40	0.9	2.076	0.804
2.0983	41	1.992	1.705	0.76
1.9555	42	1.63	1.86	0.906
1.8192	43	1.298	1.558	0.89
2.3881	44	1.939	1.236	0.476
2.2106	45	2.009	2.017	0.87
2.4475	46	1.333	2.307	1.157
2.3959	47	0.892	1.634	0.79
2.9669	48	1.376	1.358	0.656