Abstract
In this study, we proposed some inflation rate predictions based on econometric models that performed better than the targets of the National Bank of Romania. Few econometric models (multiple regressions model and a vector-autoregression) were used to predict the quarterly inflation rate in Romania during 2000:Q1-2016:Q4. The GDP growth has a negative impact on inflation rate in Romania, an increase in logarithm of GDP with one percentage point determining a decrease in inflation logarithm with less than 0.1 units according to both proposed models. However, an increase in inflation rate in the previous period determined an increase in this variable in the current period. The inverse of unemployment rate is positively correlated with the index of prices. The causal relationship between inflation rate and unemployment rate is reciprocal. In the first period the index of prices evolution is explained only by changes in this variable. The inflation rate volatility is due mainly to the evolution of this indicator, the influence decreasing insignificantly in time, not descending under 88%. More than 99% of the variation in unemployment rate is explained by the own volatility for all lags. The annual forecasts based on these models performed better than the targets on the horizon 2015-2016.
Highlights
An important objective of the Romanian monetary authorities is to maintain the inflation rate in a certain target
The GDP growth has a negative impact on inflation rate in Romania, an increase in logarithm of GDP with one percentage point determining a decrease in inflation logarithm with less than 0.1 units according to both proposed models
An increase in inflation rate in the previous period determined an increase in this variable in the current period
Summary
An important objective of the Romanian monetary authorities is to maintain the inflation rate in a certain target. We construct several econometric models to explain the evolution of quarterly index of prices, using multiple regression models and a VAR model (vectorial-autoregressive model) The VAR approach allows us to evaluate the variance decomposition of each indicator. In this way, we can determine if the variation in the variable’s evolution is mainly due to the other variable or to its own evolution. The authors showed that the Central Bank might use as inflation targets the predictions of private forecasters, but it has to take into account the structural model of the economy in order to guide the policy decisions. After the methodological approach regarding the inflation modeling, several empirical models are proposed for explaining the quarterly index of prices evolution in Romania during 2000:Q1-2014:Q4
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