Using additional information in estimating output gap in Peru: a multivariate unobserved component approach

One of the key inputs for inflation targeting regime is the right identification of inflationary or disinflationary pressures. These pressures are usually approximated by the output gap. In this paper we provide an estimation of the Peruvian output gap using a multivariate unobserved component (MUC) model, relying on an explicit short term relation between output gap an inflation rate (Phillips Curve) and structural restrictions over output stochastic dynamics. The estimation is carried out via Kalman Filter technique. The results show that MUC output gap estimate is less sensible to end of sample problems and exhibits more relation with the Peruvian inflation process than other estimates, calculated with the Hodrick-Prescott filter and the production function approach. Furthermore, the diagnostic statistics report that MUC estimate increases out-sample predictive power for inflation. All these features make MUC output gap more reliable than other alternatives.