In agricultural economics, a "supply response function" describes how supply of a commodity responds to changes in the price of that commodity.

(a) Estimate a supply response function for wheat in Manitoba for the years 1966 to 2022. Carefully explain your model, fully reference your dataset (you will have to find the data), and fully report and interpret your results. Be sure to exercise caution in choosing a price to use, and explain why you chose that price.

(b) Test the hypothesis that wheat price does not elicit a supply response at the 1% significance level. Be sure to complete all parts of the hypothesis test. Think carefully about your rejection region(s).

(c) Construct a 90% confidence interval for your slope parameter. What does this tell you?

(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.