The Fisher effect at the borders of the European Monetary Union: evidence from post-communist countries

Perić, Blanka Škrabić; Sorić, Petar; Arnerić, Josip

doi:10.1080/14631377.2013.813138

Cited by 1 publication

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References 32 publications

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“…Surveys of consumers (SCs) are not considered in this paper as consumers' expectations are higher than those of SPFs, although they relatively quickly incorporate changes in the inflation process. Due to respondents' heterogeneity some consumers tend to be more biased and less rational [7,31]. In the short run, the SPF can predict almost all of the variation in the time-series due to the trend and the business cycle, but that the forecasts contain little or no significant information about the variation in the irregular component [32], while [33] argues that the SPFs are sensitive to other aspects of the environment as well, though this may not always lead to improved forecast accuracy.…”

Section: Previous Studiesmentioning

confidence: 99%

Can Recurrent Neural Networks Predict Inflation in Euro Zone as Good as Professional Forecasters?

Šestanović

Arnerić

2021

Mathematics

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This paper investigates whether a specific type of a recurrent neural network, in particular Jordan neural network (JNN), captures the expected inflation better than commonly used feedforward neural networks and traditional parametric time-series models. It also considers competing survey-based and model-based expected inflation towards ex-post actual inflation to find whose predictions are more accurate; predictions from survey respondents or forecasting modelers. Further, it proposes neural network modelling strategy when dealing with nonstationary time-series which exhibit long-memory property and nonlinear dependence with respect to lagged inputs and exogenous inputs as well. Following this strategy, overfitting problem was reduced until no improvement in forecasting accuracy of expected inflation is achieved. The main finding is that JNN predicts inflation in euro zone quite accurately within forecasting horizon of 2 years. Regarding rational expectation principle we have found a set of demand-pull and cost-push inflation characteristics as exogenous inputs which helps in reducing overfitting problem of recurrent neural network even more. The sample includes euro zone aggregated monthly observations from January 2000 to December 2019. The results also confirm that inflation expectations obtained from JNN are consistent with Survey of professional forecasters (SPF), and thus, monetary policy makers can use JNN as a complementary tool in shortcomings of other inflation expectations measures.

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