# Python代写 | Personal Consumptions Expenditures Assignment

Dataset description:

PCE: Personal consumptions expenditures is the primary measure of consumer spending on goods and services in the US economy. This accounts for 2/3 of domestic spending and this is the primary engine that drives future economic growth https://www.bea.gov/resources/methodologies/nipa-handbook/pdf/chapter-05.pdf

AHE: Average hourly earnings is reported in dollars per hour and is reported monthly

PCEPI Personal consumptions expenditures price index is a measure of the average increase in prices for all domestic personal consumption. A major inflationary measure in the United States

Part 1 – 20 pts Exploratory Data Analysis

1. 10 pts Perform quantitative and qualitative EDA
2. 10 pts Give a summary of your findings

Part 2 – 40 pts Granger Causality

1. 8 pts Consider PCE and AHE, give an intuitive description of the relationship between the two economic measures. This is your opinion, looking for logic and understanding not right or wrong
2. 8 pts Difference the two variables
3. 8 pts Check Granger Causality for the direction you stated in 2A, return plot of results
4. 8 pts Check Granger Causality for the opposite direction you stated in 2A, return plot of results
5. 8 pts Summarize your findings. You must understand the underlying null hypothesis of test.

Part 3 – 40 pts VARMA modeling

1. 8 pts Using differenced data, run a search to find the best fit order for AH, PCE, and PCEIP, return a plot
2. 8 pts Fit the VAR model with chosen order, return the model summary.
3. 8 pts Explain the values in the correlation matrix of residuals. What are the implications for the model fit?
4. 8 pts Create 12 periods of forecast for all three variables, return plots of predicted values against actual values, Return variable level RMSE (3 total)

8 pts What is the advantage of this VAR model? What would ARIMA or prophet models on this dataset look like, if we were asked to forecast AHE?

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