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Evolution of monetary policy in Peru: an empirical application using a mixture innovation TVP-VAR-SV model
(Oxford University Press, 2021-12-15)
This article discusses the evolution of monetary policy (MP) in Peru in 1996Q1–2019Q4 using a mixture innovation time-varying parameter vector autoregressive (VAR) model with stochastic volatility (TVP-VAR-SV) as proposed by Koop, Leon-Gonzales and Strachan. The main empirical results are: (i) the VAR coefficients and volatilities change more gradually than the contemporaneous coefficients over time; (ii) the volatility of MP shocks was higher under the pre-Inflation Targeting (IT) regime; (iii) a surprise increase in the interest rate produces gross domestic product (GDP) growth falls and reduces inflation in the long run; (iv) the interest rate reacts more quickly to aggregate supply shocks than to aggregate demand shocks; (v) MP shocks explain a high percentage of domestic variables behavior under the pre-IT regime but their contribution decreases under the IT regime. Overall, these results show that MP has contributed in Peru to lower macroeconomic volatility by (i) reducing average long-term inflation, (ii) increasing the response of GDP growth rate to interest rate, and (iii) by becoming more predictable. (JEL codes: C11, C32, and E52).
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Forecasting Peru’s GDP Growth and Inflation Using TVP-VARMA-SV Models
(Pontificia Universidad Católica del Perú. Departamento de Economía, 2026-08)
This paper evaluates the forecasting performance of the time-varying parameter VARMA model with stochastic volatility (TVP-VARMA-SV) of Chan and Eisenstat (2017) for Peru’s GDP growth and inflation over 1994Q1–2019Q4. Seven model specifications are compared using density and point forecast evaluation metrics. The main results show that models with a movingaverage (MA) component generally deliver better forecast performance. Log predictive likelihoods (LPLs) indicate that models with MA, stochastic volatility (SV), or both components provide the best density forecasts at the one-quarter horizon, while simpler VARMA models perform better at the four-quarter horizon. Mean squared forecast errors (MSFEs) and Theil’s U are also lowest for models with an MA component. Probability integral transformation (PIT) histograms show that models with MA and SV components achieve the best calibration at the one-quarter horizon, while raw-moments tests indicate that the TVP-VARMA-SV, VARMA, VAR, and Bayesian model selection (BMS) specifications perform best at the four-quarter horizon. The model confidence set (MCS) frequently includes simpler models, particularly those with an MA term, and Diebold-Mariano tests also tend to favor models with an MA component. Overall, the BMS strategy improves forecast accuracy by dynamically selecting the best-performing models. An extension to exchange rate growth forecasts shows that MA-based models again perform best, with the TVP-VARMA-SV specification delivering the largest gains at the four-quarter horizon.
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Performance of evaluation metrics for classification in imbalanced data
(Springer Science and Business Media Deutschland GmbH, 2025-03-01)
This paper investigates the effectiveness of various metrics for selecting the adequate model for binary classification when data is imbalanced. Through an extensive simulation study involving 12 commonly used metrics of classification, our findings indicate that the Matthews Correlation Coefficient, G-Mean, and Cohen’s kappa consistently yield favorable performance. Conversely, the area under the curve and Accuracy metrics demonstrate poor performance across all studied scenarios, while other seven metrics exhibit varying degrees of effectiveness in specific scenarios. Furthermore, we discuss a practical application in the financial area, which confirms the robust performance of these metrics in facilitating model selection among alternative link functions.
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Evolving impacts of fiscal policy on macroeconomic fluctuations in Peru
(Elsevier BV, 2025-03-01)
This study assesses the evolving impact of fiscal policy on Peru's economic activity in 1995Q1-2018Q2 using unrestricted and restricted TVP-VAR-SV models as proposed by Chan and Eisenstat (2018a). The results highlight the necessity of including stochastic volatility, although there is no clear evidence for time-varying parameters. Shocks from government consumption growth and public investment growth significantly influence the forecast error variance decomposition and the historical decomposition of GDP growth. Conversely, the impact of tax revenue shocks remains weak throughout the study period. The public investment multiplier exceeds that of government consumption although both are less than 1, suggesting a limited capacity of fiscal policy to stimulate economic activity. The study also finds that external shocks (export price index growth) have a strong and positive impact on tax revenue growth. A series of robustness exercises further confirms these results.
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