Peru

The states chosen for Peru have, like Sargent et al. (2009), a configuration for the process that follows the deficit of 3 states for the mean (low, intermediate, and high) and 2 for the variance (low and high). The estimates use as input the month-by-month, seasonally adjusted inflation in the period from January 1957 to October 2022.

Inflation and Seasonally Adjusted Inflation

Three periods can be identified for inflation in Peru. In the first, which began in the mid-1950s and ended in the mid-1980s, there was a relative calm with occasional episodes of price rises. Subsequently, between the mid-1980s and the mid-1990s, there were periods of hyperinflation. Finally, in relation to a series of fiscal reforms and the granting of independence to the Central Bank, it can be considered that inflation has remained stable since the mid-1990s.

Notes: Month-to-month percentage changes of the Consumer Price Index. ”a.e”, refers to seasonally adjusted data.
Sample: January 1957 to October 2022. Source: With data from the Central Reserve Bank of Peru.


Recent Inflation Data

Estimated parameters for the model

The following table shows the parameters resulting from the numerical solution of the optimization problem for the likelihood function associated with the SWZ model. Refer to the model description for a discussion on the intuition behind the model. In addition, the interested reader is referred to Sargent, Williams and Zha (2009), and Ramos-Francia, García-Verdú and Sánchez-Martínez (2018) for further details.

Below is a brief description of the model parameters.

The model assumes an adaptive inflation expectation mechanism with constant gain. This means that agents form their inflation expectation for the next period based on their expectation of the present period and the observed inflation. The parameter ν determines the weight that the agents give to the observed inflation to generate their expectation. Thus, a parameter ν close to 0 indicates that the agents take into account only their past inflation expectation. In contrast, a parameter ν close to 1 indicates that the agents only take into account the observed inflation. It is called constant because the ν parameter is fixed.

The parameter λ measures the sensitivity of the demand for money to changes in expected inflation and can take values between 0 and 1. In this model, such demand for money (in real terms) depends linearly and with a negative sign on the expected price level. In this model, such demand for money (in real terms) depends linearly and negatively on the expected price level.

It is assumed that the parameters of the deficit distribution follow two independent Markov processes. In these processes each state has an associated set of values that indicate the probability of remaining in the same state or of moving to a neighboring state in the next period. In the table, the probability of remaining in the same state is presented. If there is only one possible neighbor state, the probability of transiting to it is the unit minus the probability of remaining in the original state. On the other hand, if you are in a state with two possible neighboring states, it is assumed that there is the same probability of transiting to any of them.

The parameter σ(π) measures the standard deviation of the process that determines the adjustment of inflation and inflation expectations in the case of a cosmetic reform. In such a case, inflation and its expectations are readjusted to the value of the balance for the state of the average associated with the low level (which is stable) plus some noise.