In agricultural economics, a “supply response function” describes how supply of a commodity responds to changes in the price of that commodity. (production is the dependent variable,Independent variable: Price) Dataset: Year Seeded Area (hectares) Harvested Area (hectares) Average Yield (kg per ha) Production (tonnes) Price per Tonne ($/tonne) 1966 1,317,000 1,317,000 1,630 2,150,000 65 1967 1,424,000 1,424,000 1,720 2,449,000 60 1968 1,376,000 1,376,000 1,800 2,477,000 48 1969 1,012,000 1,012,000 1,720 1,742,000 46 1970 567,000 567,000 1,465 830,000 52 1971 1,019,000 1,019,000 1,975 2,014,000 50 1972 1,052,000 1,052,000 1,785 1,878,000 68 1973 1,214,000 1,214,000 1,725 2,095,000 158 1974 1,133,000 1,133,000 1,415 1,605,000 147 1975 1,255,000 1,255,000 1,690 2,123,000 130 1976 1,538,000 1,538,000 1,820 2,803,000 103 1977 1,295,000 1,295,000 2,125 2,749,000 98 1978 1,376,000 1,376,000 2,055 2,831,000 133 1979 1,214,000 1,214,000 1,680 2,041,000 170 1980 1,336,000 1,336,000 1,425 1,905,000 203 1981 1,577,900 1,577,900 2,110 3,326,000 174 1982 1,619,100 1,619,100 2,290 3,701,000 165 1983 1,861,200 1,861,200 1,830 3,410,100 174 1984 1,801,200 1,801,200 2,080 3,742,300 172 1985 1,962,500 1,962,500 2,660 5,226,000 156 1986 1,999,100 1,995,100 2,200 4,458,300 121 1987 1,964,700 1,962,700 2,000 3,946,000 108 1988 1,938,400 1,934,400 1,200 2,381,500 141 1989 2,144,800 2,134,700 2,000 4,172,200 167 1990 2,209,600 2,205,500 2,700 5,884,400 140 1991 2,175,084 2,173,054 2,200 4,806,300 115 1992 2,173,000 2,084,100 2,800 5,807,700 121 1993 2,077,200 1,983,000 1,800 3,637,300 123 1994 1,661,200 1,657,200 2,200 3,696,600 139 1995 1,626,700 1,614,500 2,100 3,404,700 193 1996 1,709,700 1,699,600 2,600 4,376,500 210 1997 1,574,200 1,570,200 2,100 3,350,200 184 1998 1,313,200 1,307,100 2,500 3,219,700 178 1999 1,288,800 1,272,700 2,500 3,158,300 164 2000 1,588,300 1,572,100 2,700 4,266,000 152 2001 1,598,400 1,582,200 2,200 3,430,600 162 2002 1,375,800 1,361,700 2,400 3,325,200 180 2003 1,361,800 1,339,500 2,900 3,944,800 177 2004 1,276,600 1,220,000 3,000 3,707,300 162 2005 1,149,300 1,092,700 2,200 2,367,600 139 2006 1,331,500 1,323,300 2,900 3,794,900 154 2007 1,195,800 1,173,500 2,700 3,211,400 244 2008 1,331,500 1,289,000 3,300 4,280,900 293 2009 1,333,400 1,246,400 3,300 4,144,900 225 2010 1,230,200 1,175,600 2,800 3,250,900 212 2011 897,200 860,000 2,600 2,228,900 275 2012 1,222,000 1,205,800 3,300 3,923,000 276 2013 1,425,400 1,348,300 3,800 5,160,000 270 2014 1,246,400 1,167,500 3,300 3,797,500 217 2015 1,282,800 1,252,400 3,400 4,218,300 225 2016 1,210,500 1,192,700 3,500 4,217,400 229 2017 1,119,100 1,085,400 3,299 4,475,900 249 2018 1,186,600 1,169,100 4,099 4,791,600 251 2019 1,280,300 1,235,400 4,088 4,969,400 235 2020 3,169,400 1,278,361 4,126 5,274,100 236 2021 2,902,300 1,164,118 3,222 3,750,909 351 2022 3,094,400 3,011,400 3,905 4,758,248 436 (d) Find the expected price for wheat for this crop year, then predict how much wheat will be seeded in Manitoba in 2023 year based upon this price. Based upon recent trends in Manitoba, justify why you do or do not think this is a reasonable prediction. (e) Using the Jarque-Bera test, determine whether the data series used in this question are normally distributed. Show all your work, and be sure to do all parts of the hypothesis test. (f) Re-estimate your model, using only the last 30 years of data. How do your results change? Why do you think this is, and what does it tell you about the robustness of your model? (g) Re-estimate your model, using only the last 30 years of data, using a log-log functional form. Fully interpret your results, especially the interpretation of your new slope parameter. (h) What other variables do you think could/should be added to this model to make it better? Write out your expanded model in econometric notation. Carefully describe why any variables you add would improve the model. Ensure any variables you add keep the model consistent with Koutsoyiannis’ desirable properties of any econometric model.
In agricultural economics, a “supply response
Dataset:
|
Year |
Seeded Area (hectares) |
Harvested Area (hectares) |
Average Yield (kg per ha) |
Production (tonnes) |
Price per Tonne ($/tonne) |
|
1966 |
1,317,000 |
1,317,000 |
1,630 |
2,150,000 |
65 |
|
1967 |
1,424,000 |
1,424,000 |
1,720 |
2,449,000 |
60 |
|
1968 |
1,376,000 |
1,376,000 |
1,800 |
2,477,000 |
48 |
|
1969 |
1,012,000 |
1,012,000 |
1,720 |
1,742,000 |
46 |
|
1970 |
567,000 |
567,000 |
1,465 |
830,000 |
52 |
|
1971 |
1,019,000 |
1,019,000 |
1,975 |
2,014,000 |
50 |
|
1972 |
1,052,000 |
1,052,000 |
1,785 |
1,878,000 |
68 |
|
1973 |
1,214,000 |
1,214,000 |
1,725 |
2,095,000 |
158 |
|
1974 |
1,133,000 |
1,133,000 |
1,415 |
1,605,000 |
147 |
|
1975 |
1,255,000 |
1,255,000 |
1,690 |
2,123,000 |
130 |
|
1976 |
1,538,000 |
1,538,000 |
1,820 |
2,803,000 |
103 |
|
1977 |
1,295,000 |
1,295,000 |
2,125 |
2,749,000 |
98 |
|
1978 |
1,376,000 |
1,376,000 |
2,055 |
2,831,000 |
133 |
|
1979 |
1,214,000 |
1,214,000 |
1,680 |
2,041,000 |
170 |
|
1980 |
1,336,000 |
1,336,000 |
1,425 |
1,905,000 |
203 |
|
1981 |
1,577,900 |
1,577,900 |
2,110 |
3,326,000 |
174 |
|
1982 |
1,619,100 |
1,619,100 |
2,290 |
3,701,000 |
165 |
|
1983 |
1,861,200 |
1,861,200 |
1,830 |
3,410,100 |
174 |
|
1984 |
1,801,200 |
1,801,200 |
2,080 |
3,742,300 |
172 |
|
1985 |
1,962,500 |
1,962,500 |
2,660 |
5,226,000 |
156 |
|
1986 |
1,999,100 |
1,995,100 |
2,200 |
4,458,300 |
121 |
|
1987 |
1,964,700 |
1,962,700 |
2,000 |
3,946,000 |
108 |
|
1988 |
1,938,400 |
1,934,400 |
1,200 |
2,381,500 |
141 |
|
1989 |
2,144,800 |
2,134,700 |
2,000 |
4,172,200 |
167 |
|
1990 |
2,209,600 |
2,205,500 |
2,700 |
5,884,400 |
140 |
|
1991 |
2,175,084 |
2,173,054 |
2,200 |
4,806,300 |
115 |
|
1992 |
2,173,000 |
2,084,100 |
2,800 |
5,807,700 |
121 |
|
1993 |
2,077,200 |
1,983,000 |
1,800 |
3,637,300 |
123 |
|
1994 |
1,661,200 |
1,657,200 |
2,200 |
3,696,600 |
139 |
|
1995 |
1,626,700 |
1,614,500 |
2,100 |
3,404,700 |
193 |
|
1996 |
1,709,700 |
1,699,600 |
2,600 |
4,376,500 |
210 |
|
1997 |
1,574,200 |
1,570,200 |
2,100 |
3,350,200 |
184 |
|
1998 |
1,313,200 |
1,307,100 |
2,500 |
3,219,700 |
178 |
|
1999 |
1,288,800 |
1,272,700 |
2,500 |
3,158,300 |
164 |
|
2000 |
1,588,300 |
1,572,100 |
2,700 |
4,266,000 |
152 |
|
2001 |
1,598,400 |
1,582,200 |
2,200 |
3,430,600 |
162 |
|
2002 |
1,375,800 |
1,361,700 |
2,400 |
3,325,200 |
180 |
|
2003 |
1,361,800 |
1,339,500 |
2,900 |
3,944,800 |
177 |
|
2004 |
1,276,600 |
1,220,000 |
3,000 |
3,707,300 |
162 |
|
2005 |
1,149,300 |
1,092,700 |
2,200 |
2,367,600 |
139 |
|
2006 |
1,331,500 |
1,323,300 |
2,900 |
3,794,900 |
154 |
|
2007 |
1,195,800 |
1,173,500 |
2,700 |
3,211,400 |
244 |
|
2008 |
1,331,500 |
1,289,000 |
3,300 |
4,280,900 |
293 |
|
2009 |
1,333,400 |
1,246,400 |
3,300 |
4,144,900 |
225 |
|
2010 |
1,230,200 |
1,175,600 |
2,800 |
3,250,900 |
212 |
|
2011 |
897,200 |
860,000 |
2,600 |
2,228,900 |
275 |
|
2012 |
1,222,000 |
1,205,800 |
3,300 |
3,923,000 |
276 |
|
2013 |
1,425,400 |
1,348,300 |
3,800 |
5,160,000 |
270 |
|
2014 |
1,246,400 |
1,167,500 |
3,300 |
3,797,500 |
217 |
|
2015 |
1,282,800 |
1,252,400 |
3,400 |
4,218,300 |
225 |
|
2016 |
1,210,500 |
1,192,700 |
3,500 |
4,217,400 |
229 |
|
2017 |
1,119,100 |
1,085,400 |
3,299 |
4,475,900 |
249 |
|
2018 |
1,186,600 |
1,169,100 |
4,099 |
4,791,600 |
251 |
|
2019 |
1,280,300 |
1,235,400 |
4,088 |
4,969,400 |
235 |
|
2020 |
3,169,400 |
1,278,361 |
4,126 |
5,274,100 |
236 |
|
2021 |
2,902,300 |
1,164,118 |
3,222 |
3,750,909 |
351 |
|
2022 |
3,094,400 |
3,011,400 |
3,905 |
4,758,248 |
436 |
(d) Find the expected price for wheat for this crop year, then predict how much wheat will be seeded in Manitoba in 2023 year based upon this price. Based upon recent trends in Manitoba, justify why you do or do not think this is a reasonable prediction.
(e) Using the Jarque-Bera test, determine whether the data series used in this question are
(f) Re-estimate your model, using only the last 30 years of data. How do your results change? Why do you think this is, and what does it tell you about the robustness of your model?
(g) Re-estimate your model, using only the last 30 years of data, using a log-log functional form. Fully interpret your results, especially the interpretation of your new slope parameter.
(h) What other variables do you think could/should be added to this model to make it better? Write out your expanded model in econometric notation. Carefully describe why any variables you add would improve the model. Ensure any variables you add keep the model consistent with Koutsoyiannis’ desirable properties of any econometric model.
Step by step
Solved in 4 steps with 12 images
