Contents

README HW1

README HW1

Tasks

Part I. HICP data from SDW

The Harmonised Index of Consumer Prices (HICP) is constructed by aggregating price subindices of different item groups, which in turn have their own subgroups, etc. For example, the FOOD AND NON-ALCOHOLIC BEVERAGES category has 2 subgroups: Food and Non-alcoholic beverages. In turn, Food has items such as Bread and cereals, Meat, Fish, etc. You can find a listing of all items in the table HICP Code List Description at the ECB’s SDW site here

  • For the item group assigned to you, get the codes of all available level 3 subindices. For example, in the case of the FOOD AND NON-ALCOHOLIC BEVERAGES category, find all indices of the type 01XX00. For the the ALCOHOLIC BEVERAGES, TOBACCO category, find all indices of the type 02XX00. And so on.

  • download monthly inflation data (annual rate of change) for the euro area (geo=U2) for each subindex you found.

Hint: The SDW site has different search options. The series you are looking for will have a description that will be something like this

Euro area (changing composition) - HICP - XXXXXXX, Annual rate of change, Eurostat, Neither seasonally nor working day adjusted

where XXXXXXX is the name of the particular item you are looking for.

  • in a Jupyter notebook and using the pandas library, create a single dataframe containing all series you downloaded, with a proper datetime index. Rename/shorten column names as you see fit.

  • save as a csv file under the name of the item group, for example "FOOD_AND_NON-ALCOHOLIC_BEVERAGES.csv"

  • for the full period

    • compute the means and standard deviations for each series and display them in the separate cell in the notebook.

    • compute the means and standard deviations for each calendar month for each series and display them in the separate cell in the notebook.

  • redo the above computations for the period between January 2017 and December 2021.

Part II. GDP data from EUROSTAT

  • Download from Eurostat the available time series data for Gross domestic product at market prices for the Euro area - 19 countries (from 2015) The series should be Seasonally and calendar adjusted and the units should be Chain linked volumes (2010), million euro.

  • import with pandas and create a dataframe with a datetime index matching the timing of the series you downloaded

  • save as a csv file under the name “Real-GDP-EA.csv”

  • create a new datafame containing the GDP data and the inflation indices from the first task.

  • save as a csv file named “merged-GDP-inflation.csv”

Submission

  • submit as a pull request to this Github repo

  • as with HW0, fork and clone the repo

  • use notebook HW1-empty.ipynb as a starting point. Here is a copy of that notebook.

  • when you finish, rename the notebook to “HW1-First-Last” using your first and last names.

  • make the pull request

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