Stata - Disentangle Supply and Demand Aspects of Inflation
Budget: €8 – €30 EUR
Stata Work
Collect data on relevant variables, such as inflation rate, economic activity indicators (such as GDP, employment, and wages), and other variables that may affect supply and demand conditions (such as oil prices and exchange rates).
Estimate the principal components of these variables using Stata's principal component analysis (PCA) command, such as "pca" or "pcamat". This will create a set of factors that capture the common variation across the variables.
Rotate the factors to identify supply and demand using sign restrictions imposed on factor loadings. This can be done using Stata's "varimax" or "promax" rotation methods, which aim to maximize the variance of the loadings of each factor while minimizing the correlations between factors.
Propose a set of theoretically motivated sign restrictions on the factor loadings of inflation and economic activity indicators to separate supply and demand. This involves setting prior expectations on the direction of the effect of each variable on supply and demand, based on economic theory and previous empirical studies. These restrictions can be imposed using Stata's "signres" command.
Estimate the supply and demand factors and assess their role in the dynamics of inflation using Stata's regression or VAR models. For example, one can estimate the effect of changes in the supply factor on inflation, while controlling for other factors such as demand, oil prices, and exchange rates. Similarly, one can estimate the effect of changes in the demand factor on inflation.
Given inflation data along with energy and food price data. Disentangle the aspects of each into supply and demand related factors. Use econometrical process in papers such as Killian (2009), Shapiro (2022), Adjemain (2023) and Cologni & Manera (2008).
Collect data on relevant variables, such as inflation rate, economic activity indicators (such as GDP, employment, and wages), and other variables that may affect supply and demand conditions (such as oil prices and exchange rates).
Estimate the principal components of these variables using Stata's principal component analysis (PCA) command, such as "pca" or "pcamat". This will create a set of factors that capture the common variation across the variables.
Rotate the factors to identify supply and demand using sign restrictions imposed on factor loadings. This can be done using Stata's "varimax" or "promax" rotation methods, which aim to maximize the variance of the loadings of each factor while minimizing the correlations between factors.
Propose a set of theoretically motivated sign restrictions on the factor loadings of inflation and economic activity indicators to separate supply and demand. This involves setting prior expectations on the direction of the effect of each variable on supply and demand, based on economic theory and previous empirical studies. These restrictions can be imposed using Stata's "signres" command.
Estimate the supply and demand factors and assess their role in the dynamics of inflation using Stata's regression or VAR models. For example, one can estimate the effect of changes in the supply factor on inflation, while controlling for other factors such as demand, oil prices, and exchange rates. Similarly, one can estimate the effect of changes in the demand factor on inflation.
Given inflation data along with energy and food price data. Disentangle the aspects of each into supply and demand related factors. Use econometrical process in papers such as Killian (2009), Shapiro (2022), Adjemain (2023) and Cologni & Manera (2008).