This paper provides, via Monte Carlo simulations, estimates of the classical probability of overfitting under an autoregressive environment (AR), using the information criteria (IC) of Akaike, Schwarz and Hannan-Quinn (AIC, BIC and HQ), calibrated with Chilean data of total inflation, core inflation, Imacec, and monthly return of the nominal exchange rate Chilean peso-American dollar. This probability corresponds to the number of times when a candidate model has a strictly greater number of coefficients than the true model, divided by the total number of searches. The results indicate that the increased risk of overfitting is obtained with the AIC, followed by HQ and finally the BIC. The highest probability of overfitting is achieved with the AIC, reaching 32 and 30% with the exchange rate and Imacec, respectively, followed by 25 and 22% for total and core inflation. Considering the three IC, it is more likely to obtain an overfitted model by just one coefficient. Also, it is more likely that the overfitting does not exceed 10 coefficients. These results are important as quantifying the extent to which these variables are subject to overfitting risk when represented by AR models. The potential problems carried by overfitting includes: (i) the spurious regression, (ii) distort the estimation of impulse response function, and (iii) affect the predictive accuracy of the variable of interest. The latter problem is analyzed in detail.
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