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1

Bayesian combined forecasts and Monte Carlo simulations to improve inflation rate predictions in Romania

100%

Simionescu M.

Research Papers in Economics and Finance

|

2020

|

vol. 4

|

issue 1

7-20

EN

In this paper we applied the regression approach and Bayesian inference to obtain more accurate forecasts of the inflation rate in the case of the Romanian economy. The necessity of using the most accurate forecasts for the inflation rate is required by the realisation of economic criteria for the accession to the eurozone and by the inflation targeting strategy of the National Bank of Romania. Considering the assumption that simple econometric models provide better forecasts than complex models, in this paper we combined various forecasts from individual models using as prior information the expectations of experts. The empirical findings for Romanian inflation rate forecasts over the horizon of 2016-2018 indicated that a fixed effects model performed better than other simple models (autoregressive moving average model, dynamic model, simple and multiple linear model, VAR, Bayesian VAR, simultaneous equations model). The Bayesian combined forecasts that used experts’ predictions as priors, with a shrinkage parameter tending to infinity, improved the accuracy of all predictions using individual models, outperforming also naïve forecasts and zero and equal weights forecasts. However, predictions based on Monte Carlo simulation outperformed all the scenarios in terms of the mean error and mean absolute error.

2

Bayesian combined forecasts and Monte Carlo simulations to improve inflation rate predictions in Romania

100%

Simionescu M.

Research Papers in Economics and Finance

|

2020

|

vol. 4

|

issue 1

7-20

EN

In this paper we applied the regression approach and Bayesian inference to obtain more accurate forecasts of the inflation rate in the case of the Romanian economy. The necessity of using the most accurate forecasts for the inflation rate is required by the realisation of economic criteria for the accession to the eurozone and by the inflation targeting strategy of the National Bank of Romania. Considering the assumption that simple econometric models provide better forecasts than complex models, in this paper we combined various forecasts from individual models using as prior information the expectations of experts. The empirical findings for Romanian inflation rate forecasts over the horizon of 2016-2018 indicated that a fixed effects model performed better than other simple models (autoregressive moving average model, dynamic model, simple and multiple linear model, VAR, Bayesian VAR, simultaneous equations model). The Bayesian combined forecasts that used experts’ predictions as priors, with a shrinkage parameter tending to infinity, improved the accuracy of all predictions using individual models, outperforming also naïve forecasts and zero and equal weights forecasts. However, predictions based on Monte Carlo simulation outperformed all the scenarios in terms of the mean error and mean absolute error.

3

The Performance of Unemployment Rate Predictions in Romania. Strategies to Improve the Forecasts Accuracy

75%

Simionescu M.

Review of Economic Perspectives

|

2013

|

vol. 13

|

issue 4

161-175

EN

The evaluation and improvement of forecasts accuracy generate growth in the quality of decisional process. In Romania, the most accurate predictions for the unemployment rate on the forecasting horizon 2001-2012 were provided by the Institute for Economic Forecasting (IEF) that is followed by European Commission and National Commission for Prognosis (NCP). The result is based on U1, but if more indicators are taken into consideration at the same time using the multi-criteria ranking, the conclusion remains the same. A suitable strategy for improving the degree of accuracy for these forecasts is represented by the combined forecasts. The accuracy of NCP predictions can be improved on the horizon 2001-2012, if the initial values are smoothed using Holt-Winters technique and Hodrick-Prescott filter. The use of Monte Carlo method to simulate the forecasted unemployment rate proved to be the best way to improve the predictions accuracy. Starting from an AR(1) model for the interest variable, the uncertainty analysis was included, the simulations being made for the parameters. Actually, the means of the forecasts distributions for unemployment are considered as point predictions which outperform the expectations of the three institutions. The strategy based on Monte Carlo method is an original contribution of the author introduced in this article regarding the empirical strategies of getting better predictions.

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2 Research Papers in Economics and Finance

1 Review of Economic Perspectives

3 Simionescu M.

2 2020

1 2013

Bayesian combined forecasts and Monte Carlo simulations to improve inflation rate predictions in Romania

Bayesian combined forecasts and Monte Carlo simulations to improve inflation rate predictions in Romania

The Performance of Unemployment Rate Predictions in Romania. Strategies to Improve the Forecasts Accuracy