Long term inflation
Underlying/Core inflation
See methodological description here
Z-Scores (Beta)
Is inflation going up or down? We are working on single metric comparable “Z-score”, based on core inflation to evaluate the underlying trends of the CPI. The score should be positive when inflation is accelerating and negative when it is going down, and be robust to volatility and seasonality. We are testing different definitions of the variable and work is still on alpha stage so expect some changes in the next weeks.
Z-score definition by country:
- Argentina: (1+QoQ) / (1+Qoq)_(t-3) Self reported core inflation
- Bolivia: (1+QoQ) / (1+Qoq)_(t-3) CPI ex food and fuels.
- Chile: (1+QoQ) / (1+Qoq)_(t-3) Self reported core inflation
- Colombia: (1+HoH) / (1+HoH)_(t-6) Self reported core inflation
- Ecuador: (1+QoQ) / (1+Qoq)_(t-3) Based on CPI ex-food and fuels
- Paraguay: (1+HoH) / (1+HoH)_(t-6) Self reported core inflation
- Peru: (1+HoH) / (1+HoH)_(t-6) Based on CPI ex-food and Energy
- Mexico: (1+QoQ) / (1+QoQ)_(t-3) Based on reported core inflation
- Uruguay: (1+HoH) / (1+HoH)_(t-6) Based on CPI Ex seasonals and regulated
COICOP Clasification
COICOP – Classification of individual consumption by purpose – is the most widely nomenclator for CPI around the world, grouped in 12 classed. Within the region most of the countries have also adopted this classification, with the exception of Peru and Brazil.
Food & non acoholic beverages
Goods & Services
Building up from the highest level of desaggregation in each country we have recreated comparable Goods and Services CPIs for every country in our sample, except Peru
Selecting the core/underlying measure
Most countries in our sample have a meassure of Core inflation to capture and filter the idiosincratic shocks prices and get the “underlying” trend of the inflationary process, which is believed to react to the monetary policy. Estimates are usually based on a combination of the following three criterias (a) Exclude highly volatile items or above certain threshold (b) exclude items that show high seassonality and (c) exclude regulated prices.
Countries that report a core inflation usually combine them
- Argentina: Core CPI excludes seasonal and regulated items
- Chile: IPCSAE excludes food & energy
- Colombia: Core inflation exclude 15% of the most volatile items.
- Mexico: Excludes volatile food items, energy, and regulated prices.
- Paraguay: self-reported underlying inflation (X1) is calculated by excluding fruits and vegetables, regulated prices and fuels.
- Peru: Excludes highly volatile items (chicken meat, urban transport, potato, onion, bread, eggs, fish, citrus, and other vegetables).
For countries that don’t report a core/underlying inflation we have selected the best indicator based on the minimization of the Standard Deviation (SD)
- Bolivia: Biggest source of volatity comes from food & Non Alcoholic Drinks. Headline MoM SD is 0.4% and 1.4% in food items. Energy, no the other hand, is very stable. The SD of the CPI excluding both items goes down to 0.1%
- Ecuador: Inflation SD is 0.3%, 0.2% if food & energy are excluded and back to 0.3% without seasonal and regulated. We select the former as our best pick.
- Uruguay: Volatility comes mostly from energy, particularly regulated electricity. It’s inflation show a high degree of seasonality, so we select CPI ex seasonals and regulated as our best pick for core inflation.
Country | Headline (A) | Food and Non Alcoholic Drinks (B) | Energy (C) | (A) ex (B & C) | Seassonal (D) | Regulated (E) | (A) ex (D & E) | Goods | Services | Self reported Core |
|---|---|---|---|---|---|---|---|---|---|---|
Argentina | 1.2% | 1.6% | 1.9% | 2.0% | 1.3% | 1.6% | 1.3% | 1.3% | ||
Bolivia | 0.4% | 1.4% | 0.2% | 0.1% | 2.8% | 0.2% | 0.3% | 0.7% | 0.2% | |
Chile | 0.3% | 0.7% | 1.0% | 0.3% | 0.6% | 0.3% | 0.2% | 0.2% | ||
Colombia | 0.3% | 0.6% | 0.3% | 0.2% | 0.2% | |||||
Ecuador | 0.3% | 0.8% | 0.7% | 0.2% | 1.1% | 0.4% | 0.3% | 0.4% | 0.3% | |
Mexico | 0.4% | 0.9% | 2.9% | 0.2% | 3.1% | 0.9% | 0.2% | 0.3% | 0.9% | 0.1% |
Uruguay | 0.6% | 0.9% | 5.1% | 0.4% | 1.9% | 2.6% | 0.3% | 0.5% | 1.2% | |
Paraguay | 0.6% | 1.7% | 1.0% | 0.2% | 0.9% | 0.4% | 0.3% | |||
Peru | 0.3% | 0.6% | 1.7% | 0.2% | 0.2% | 0.2% |
Standard deviation vs headline
Food and Non Alcoholic Drinks (B) | Energy (C) | (A) ex (B & C) | Seassonal (D) | Regulated (E) | (A) ex (D & E) | Goods | Services | CPI – Self reported Core | |
|---|---|---|---|---|---|---|---|---|---|
Argentina | 1.3 | 1.6 | 1.6 | 1.1 | 1.3 | 1.0 | 1.1 | ||
Bolivia | 3.5 | 0.4 | 0.4 | 6.8 | 0.5 | 0.6 | 1.7 | 0.5 | |
Chile | 2.3 | 3.1 | 0.8 | 0.0 | 1.8 | 0.0 | 0.9 | 0.7 | |
Colombia | 1.8 | 0.9 | 0.7 | ||||||
Ecuador | 2.6 | 2.4 | 0.8 | 3.5 | 1.3 | 1.1 | 1.3 | 0.9 | |
Mexico | 2.4 | 7.9 | 0.5 | 8.6 | 2.6 | 0.6 | 0.9 | 2.5 | 0.4 |
Uruguay | 1.4 | 8.0 | 0.6 | 3.0 | 4.1 | 0.5 | 0.8 | 1.8 | |
Paraguay | 2.8 | 1.7 | 0.4 | 1.5 | 0.6 | 0.5 | |||
Peru | 1.8 | 5.6 | 0.8 | 0.6 | 0.6 |
Remarks on the data
Comparing inflation between Latin American countries is a hard task. On the surface, all inflations look alike, but countries methods have significant differences. In this report we offer a guideline to understand them.
Bolivia: Has very good and detailed CPI information since 1991 in the INE for the country and subregions. Lastest consumption basket is 2016. does not have public measures of core of underlying inflation. We are working to create a longer version of the underlying inflation (now since 2018, extending to 2008).
Colombia The core measure smooths the headline, but fails to remove seasonality. We are working to create a new long term seasonaly adjusted core inflation.
Paraguay data access is limited, with no data available beyond “class” level. However, Central Bank has a number of analytics measures that traces back to 1994. The CPI is measured for the Metropolitan Area of Asuncion and latest change in base was in 2017.
Peru: The CPI is calculated for the city of Lima with a very outdated consumer basket (2009), and does not use the most recent COICOP. Information from the “Instituto Nacional de Estadisticas e Informatica” is very limited and reported only in PDF and opened only up to the “Class” level. Historical data is difficult to access and is found in statistical yearbooks.
The Central Bank offers additional information and more structured access to the data. It has a number of special analytics that trace back to 1990 (more on this later) but no access to raw CPI data
Mexico: INEGI is the only statistical bureau that offers biweekly measures of inflation. The current basket is 2018. They have detailed historical information on the website.
Comparing inflation between Latin American countries is a hard task. On the surface, all inflations look alike, but countries methods have significant differences. In this report we offer a guideline to understand them.
Paraguay data access is limited, with no data available beyond “class” level. However, Central Bank has a number of analytics measures that traces back to 1994. The CPI is measured for the Metropolitan Area of asuncion and latest change in base was in 2017.
Peru: The CPI is calculated for the city of Lima with a very outdated consumer basket (2009), and does not use the most recent COICOP. Information from the “Instituto Nacional de Estadisticas e Informatica” is very limited and reported only in PDF and opened only up to the “Class” level. Historical data is difficult to access and is found in statistical yearbooks.
The Central Bank offers additional information and more structured access to the data. It has a number of special analytics that trace back to 1990 (more on this later) but no access to raw CPI data
Mexico: INEGI is the only statistical bureau that offers biweekly measures of inflation. The current basket is 2018. They have detailed historical information on the website.
Uruguay: The CPI is calculated by the INE for the whole country and for Montevideo. The current base year is 2010. Historical information can be easily found on their webpage, up to the highest level of disaggregation (product)