Second homework
You can get at most 11 points for the second homework. You should upload a shortly commented, transparent R script and a textual analysis with at least 3000 characters (Times New Roman, 12pt, 1.5 line space) in Word or PDF to the Moodle. You should summarize the applied statistical methods, interpret the gained results and make logical conclusions. The Word/PDF file should have a title page with Your name, the date, and it also has to contain the name of the dataset. You should include the important plots of Your analysis, You should compare different models with the help of some structured tables, and include the equation of the estimated models.
Homeworks that are really similar to each other will get 0 points. Deadline: 2022.04.24. Sunday 11:55 pm, Moodle
1. Choose a stock for which the closing prices are available for (at least) the last 3 years on Yahoo Finance. Download the closing prices of the chosen stock for the last 3 years with the help of the quantmod package of R.
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2. Apply the total Box-Jenkins methodology on the time series of the closing prices.
3. Estimate the model, which fits best according to the second task, when You do not use the prices from the last 5 days. With the model that is estimated based on this shorter time period, create a forecast for the omitted 5 days, and evaluate the
accuracy of the forecasts based on this ¡®test data set¡¯.
4. Download the final consumption time series of the European countries with the
help of the get_eurostat function of the eurostat R package. For this the ID parameter of the function should be ¡®namq_10_fcs¡¯, and the time format should be ¡®date¡¯. You can read more about the package and the function here.
5. Choose a country and use the time series of its final consumption that is seasonally not adjusted (s_adj = “NSA”), valued on today¡¯s exchange rate (na_item = “P31_S14”), in million Euros (unit = “CP_MEUR”).
6. Apply the total Box-Jenkins methodology on the time series that You chose in the fifth task. The examined period should start from 2000.01.01. and last to the most recent available observation.
7. Forecast for the next 1 year based on the best model that You got in the sixth task. Here You do not have to evaluate the accuracy of the forecasts on a ¡®test data set¡¯.
The adequate R script (code) worth 5 points, while the analysis is 6 points at most.
You have to interpret the parameters and statistical measures, but the analysis has to be even wider. You have to make conclusions based on the results. For instance: Are the signs of the parameters correspond to the case that we expected? What could be the reason behind the examined shocks or structural breaks? Etc.
If You have any questions, do not hesitate to write me an e-mail corvinus.hu) or write me on Teams. Good luck! 🙂
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