Forecasting Inflation Expectations from the CESifo World Economic Survey: An Empirical Application in Inflation Targeting
The purpose of this paper is twofold. First, we evaluate the responses to the questions on inflation expectations in the World Economic Survey for sixteen inflation targeting countries. Second, we compare inflation expectation forecasts across countries by using a two-step approach that selects the most accurate linear or non-linear forecasting method for each country. Then, using Self Organizing Maps, we cluster the inflation expectations, setting June 2014 as a benchmark. At this time there was a sharp decline in oil prices and by analyzing inflation expectations in the context of this price change, we can discriminate between countries that anticipated the oil shock smoothly and those that had to significantly adjust their expectations. Our main findings from the in-sample comparison of the WES surveys suggest that expert forecasts of inflation expectations are systematically distorted in 83 percent of the countries in the sample. On the other hand, our out of sample forecast analysis indicates that Non-linear Artificial Neural Networks combined with Bayesian regularization outperform ARIMA linear models for longer forecasting horizons. This holds true for countries with both soft and brisk changes of expectations. However, when forecasting one step ahead, the performance between the two methods is similar.
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| Subjects: | Inflation, Inflation Targeting, C02 - Mathematical Methods, C22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes, C45 - Neural Networks and Related Topics, C63 - Computational Techniques • Simulation Modeling, E27 - Forecasting and Simulation: Models and Applications, |
| Online Access: | http://dx.doi.org/10.18235/0001264 https://publications.iadb.org/en/forecasting-inflation-expectations-cesifo-world-economic-survey-empirical-application-inflation |
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dig-bid-node-130262021-04-21T20:33:55ZForecasting Inflation Expectations from the CESifo World Economic Survey: An Empirical Application in Inflation Targeting 2018-07-31T00:00:00+0000 http://dx.doi.org/10.18235/0001264 https://publications.iadb.org/en/forecasting-inflation-expectations-cesifo-world-economic-survey-empirical-application-inflation Inter-American Development Bank Inflation Inflation Targeting C02 - Mathematical Methods C22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes C45 - Neural Networks and Related Topics C63 - Computational Techniques • Simulation Modeling E27 - Forecasting and Simulation: Models and Applications The purpose of this paper is twofold. First, we evaluate the responses to the questions on inflation expectations in the World Economic Survey for sixteen inflation targeting countries. Second, we compare inflation expectation forecasts across countries by using a two-step approach that selects the most accurate linear or non-linear forecasting method for each country. Then, using Self Organizing Maps, we cluster the inflation expectations, setting June 2014 as a benchmark. At this time there was a sharp decline in oil prices and by analyzing inflation expectations in the context of this price change, we can discriminate between countries that anticipated the oil shock smoothly and those that had to significantly adjust their expectations. Our main findings from the in-sample comparison of the WES surveys suggest that expert forecasts of inflation expectations are systematically distorted in 83 percent of the countries in the sample. On the other hand, our out of sample forecast analysis indicates that Non-linear Artificial Neural Networks combined with Bayesian regularization outperform ARIMA linear models for longer forecasting horizons. This holds true for countries with both soft and brisk changes of expectations. However, when forecasting one step ahead, the performance between the two methods is similar. Inter-American Development Bank Héctor Zárate Daniel R. Zapata-Sanabria Working Papers application/pdf IDB Publications Colombia en |
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Inflation Inflation Targeting C02 - Mathematical Methods C22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes C45 - Neural Networks and Related Topics C63 - Computational Techniques • Simulation Modeling E27 - Forecasting and Simulation: Models and Applications Inflation Inflation Targeting C02 - Mathematical Methods C22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes C45 - Neural Networks and Related Topics C63 - Computational Techniques • Simulation Modeling E27 - Forecasting and Simulation: Models and Applications |
| spellingShingle |
Inflation Inflation Targeting C02 - Mathematical Methods C22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes C45 - Neural Networks and Related Topics C63 - Computational Techniques • Simulation Modeling E27 - Forecasting and Simulation: Models and Applications Inflation Inflation Targeting C02 - Mathematical Methods C22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes C45 - Neural Networks and Related Topics C63 - Computational Techniques • Simulation Modeling E27 - Forecasting and Simulation: Models and Applications Inter-American Development Bank Forecasting Inflation Expectations from the CESifo World Economic Survey: An Empirical Application in Inflation Targeting |
| description |
The purpose of this paper is twofold. First, we evaluate the responses to the questions on inflation expectations in the World Economic Survey for sixteen inflation targeting countries. Second, we compare inflation expectation forecasts across countries by using a two-step approach that selects the most accurate linear or non-linear forecasting method for each country. Then, using Self Organizing Maps, we cluster the inflation expectations, setting June 2014 as a benchmark. At this time there was a sharp decline in oil prices and by analyzing inflation expectations in the context of this price change, we can discriminate between countries that anticipated the oil shock smoothly and those that had to significantly adjust their expectations. Our main findings from the in-sample comparison of the WES surveys suggest that expert forecasts of inflation expectations are systematically distorted in 83 percent of the countries in the sample. On the other hand, our out of sample forecast analysis indicates that Non-linear Artificial Neural Networks combined with Bayesian regularization outperform ARIMA linear models for longer forecasting horizons. This holds true for countries with both soft and brisk changes of expectations. However, when forecasting one step ahead, the performance between the two methods is similar. |
| author2 |
Héctor Zárate |
| author_facet |
Héctor Zárate Inter-American Development Bank |
| format |
Working Papers |
| topic_facet |
Inflation Inflation Targeting C02 - Mathematical Methods C22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes C45 - Neural Networks and Related Topics C63 - Computational Techniques • Simulation Modeling E27 - Forecasting and Simulation: Models and Applications |
| author |
Inter-American Development Bank |
| author_sort |
Inter-American Development Bank |
| title |
Forecasting Inflation Expectations from the CESifo World Economic Survey: An Empirical Application in Inflation Targeting |
| title_short |
Forecasting Inflation Expectations from the CESifo World Economic Survey: An Empirical Application in Inflation Targeting |
| title_full |
Forecasting Inflation Expectations from the CESifo World Economic Survey: An Empirical Application in Inflation Targeting |
| title_fullStr |
Forecasting Inflation Expectations from the CESifo World Economic Survey: An Empirical Application in Inflation Targeting |
| title_full_unstemmed |
Forecasting Inflation Expectations from the CESifo World Economic Survey: An Empirical Application in Inflation Targeting |
| title_sort |
forecasting inflation expectations from the cesifo world economic survey: an empirical application in inflation targeting |
| publisher |
Inter-American Development Bank |
| url |
http://dx.doi.org/10.18235/0001264 https://publications.iadb.org/en/forecasting-inflation-expectations-cesifo-world-economic-survey-empirical-application-inflation |
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