An Inflation Forecasting Model for the Euro Area

Chauvin, Valérie; Devulder, Antoine

doi:10.2139/ssrn.1680375

Cited by 40 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…JNN containing only one lag of inflation yields the best forecasts. This research is not in accordance neither with [23] nor [16] who suggest that inflation forecasting using the AR model can be significantly improved by several indicators. However, it goes along with [7] showing good performance of the AR model, where the model containing some indicators is in most, but not all, cases better than the AR model, where the best indicator changes over time.…”

Section: Introductioncontrasting

confidence: 88%

“…This is not the case since only 1 or 2 out of the top 10 JNNs have all 4 variables. The research is not in accordance neither with [25] nor [16] suggesting that inflation forecasting using AR model can be significantly improved by several indicators. Both variable and sample selection play the key role in modelling and forecasting inflation where satisfying parsimony principle leads to a JNN that has both good inthe-sample and out-of-sample performances.…”

Section: Resultscontrasting

confidence: 57%

“…[20] concludes that the models that include more information are better in inflation forecasting than models based on a narrower information set, while here JNN containing only one lag of inflation yields the best forecasts. [23] and [16] suggest that inflation forecasting using AR model can be significantly improved by several indicators, which is not the case here. [7] show good performance of the AR model, where the model containing some indicators is in most but not all cases better than the AR model and the best indicator changes over time, which was proven by this paper.…”

Section: Second Scenariomentioning

confidence: 71%

“…The most commonly used variables are lagged inflation [9,29,31,32]; exchange rates [21,22,30,34]; monetary aggregates [11,27,28]; interest rates [20]; labour market variables, i.e. wages or unit labour costs [16,20]; GDP [9,20,25]; industrial production [7] and fiscal policy variables. Researchers cannot agree on the inflation determinants since they differ through countries and periods.…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Jordan neural network for inflation forecasting

Šestanović

2019

Cro. Oper. Res. Rev.

View full text Add to dashboard Cite

In times of pronounced nonlinearity of macroeconomic variables and in situations when variables are not normally distributed, i.e. when the assumption of i.i.d. is not fulfilled, neural networks (NNs) should be used for forecasting. In this paper, Jordan neural network (JNN), a special type of NNs is examined, because of its advantages in time series forecasting suitable for inflation forecasting. The variables used as inputs include labour market variable, financial variable, external factor and lagged inflation, i.e. the most commonly used variables in previous researches. The research is conducted at the aggregate level of euro area countries in the period from January 1999 to January 2017. Based on 250 estimated JNNs, which differ in selected variables, sample breaking point and varying parameters (number of hidden neurons, weight value of the context unit), the model adequacy indicators for each JNN are calculated for two periods: in-the-sample and out-of-sample. Finally, the optimal JNN for inflation forecasting is obtained as the best compromise solution between low mean squared error inthe-sample and out-of-sample and low number of parameters to estimate. This paper contributes to existing literature in using JNN for inflation forecasting since it is rarely used for macroeconomic time series prediction in general. Moreover, this paper defines which set of variables contributes to the best inflation forecast. Additionally, JNN is examined thoroughly by fixing certain parameters of the model and alternating other parameters to contribute to the JNN literature, i.e. finding the optimal JNN.

show abstract

Section: Introductioncontrasting

confidence: 88%

Section: Resultscontrasting

confidence: 57%

Section: Second Scenariomentioning

confidence: 71%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Jordan neural network for inflation forecasting

Šestanović

2019

Cro. Oper. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…Policymakers and economic observers, for example, have relied on this argument to explain the surge in retail food prices observed in many developed and developing economies between 2006 and mid-2008, as the opening quotes indicate. However, somewhat in defiance of these accounts, a number of empirical investigations have struggled to find an economically and statistically significant pass-through between international food commodity prices and final consumer prices for the euro area (see IMF, 2008a;Benalal et al, 2004;Chauvin and Devulder, 2008). 4 By contrast, there is evidence of a robust pass-through between the two variables for other major economies outside the euro area (see IMF, 2008b).…”

Section: Introductionmentioning

confidence: 99%

Food Price Pass-Through in the Euro Area - The Role of Asymmetries and Non-Linearities

2010

View full text Add to dashboard Cite

Neural network structure identification in inflation forecasting

Šestanović

Arnerić

2020

Journal of Forecasting

View full text Add to dashboard Cite

Neural networks (NNs) are appropriate to use in time series analysis under conditions of unfulfilled assumptions, i.e., non-normality and nonlinearity. The aim of this paper is to propose means of addressing identified shortcomings with the objective of identifying the NN structure for inflation forecasting. The research is based on a theoretical model that includes the characteristics of demand-pull and cost-push inflation; i.e., it uses the labor market, financial and external factors, and lagged inflation variables. It is conducted at the aggregate level of euro area countries from January 1999 to January 2017.Based on the estimated 90 feedforward NNs (FNNs) and 450 Jordan NNs (JNNs), which differ in variable parameters (number of iterations, learning rate, initial weight value intervals, number of hidden neurons, and weight value of the context unit), the mean square error (MSE), and the Akaike Information Criterion (AIC) are calculated for two periods: in-the-sample and outof-sample. Ranking NNs simultaneously on both periods according to either MSE or AIC does not lead to the selection of the 'best' NN because the optimal NN in-the-sample, based on MSE and/or AIC criteria, often has high out-ofsample values of both indicators. To achieve the best compromise solution, i.e., to select an optimal NN, the preference ranking organization method for enrichment of evaluations (PROMETHEE) is used. Comparing the optimal FNN and JNN, i.e., FNN(4,5,1) and JNN(4,3,1), it is concluded that under approximately equal conditions, fewer hidden layer neurons are required in JNN than in FNN, confirming that JNN is parsimonious compared to FNN.Moreover, JNN has a better forecasting performance than FNN.

show abstract

An Inflation Forecasting Model for the Euro Area

Abstract: Banque de France 2 1 We thank Claudine Guibert and Laëtitia Francart for their precious help and Hervé Le Bihan for his most relevant comments. Some of the results presented were published in French in the Bulletin de la Banque de France n°167, novembre 2007.

Cited by 40 publications

References 33 publications

Jordan neural network for inflation forecasting

Jordan neural network for inflation forecasting

Food Price Pass-Through in the Euro Area - The Role of Asymmetries and Non-Linearities

Neural network structure identification in inflation forecasting

Contact Info

Product

Resources

About