Neural network versus econometric models in forecasting inflation

Moshiri, Saeed; Cameron, Norman E.

doi:10.1002/(sici)1099-131x(200004)19:3<201::aid-for753>3.3.co;2-w

Cited by 46 publications

(46 citation statements)

References 2 publications

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“…For example, it is well known that ARIMA models have been a popular contender for inflation forecasting both historically and in the A C C E P T E D M A N U S C R I P T recent past [10][11][12][13]. The ETS technique too has been utilised effectively in inflation forecasting studies [13][14][15] whilst applications of NN based approaches are documented in [16][17][18][19]. However, to the best of our knowledge, the TBATS model [20] has not been evaluated in the context of inflation forecasting, and thus, it will be interesting to see how this performs.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Forecasting UK consumer price inflation using inflation forecasts

Hassani

Silva

2018

Research in Economics

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Titl eFo r e c a s ti n g UK Co n s u m e r P ric e Inf a tio n u si n g Inf a tio n Fo r e c a s t s Typ e Articl e U RL h t t p s:// u al r e s e a r c h o nli n e. a r t s . a c. u k/id/ e p ri n t/ 1 3 2 4 2/ D a t e 2 0 1 8 Cit a tio n H a s s a ni, H . a n d Silv a, E.S. (2 0 1 8) Fo r e c a s ti n g UK Co n s u m e r P ric e Inf a tio n u si n g Inf a tio n Fo r e c a s t s . R e s e a r c h in E c o n o mic s, 7 2 (3). p p . 3 6 7-3 7 8. IS S N 1 0 9 0-9 4 4 3 C r e a t o r s H a s s a ni, H . a n d Silv a, E.S. U s a g e G u i d e l i n e s Pl e a s e r ef e r t o u s a g e g ui d eli n e s a t h t t p:// u al r e s e a r c h o nlin e. a r t s. a c. u k/ p olici e s. h t ml o r al t e r n a tiv ely c o n t a c t u al r e s e a r c h o nli n e@ a r t s. a c. u k . Lic e n s e: C r e a tiv e Co m m o n s Att ri b u tio n N o n-c o m m e r ci al N o D e riv a tiv e s U nl e s s o t h e r wi s e s t a t e d, c o py ri g h t o w n e d by t h e a u t h o r AbstractThe inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modelling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modelling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Forecasting UK consumer price inflation using inflation forecasts

Hassani

Silva

2018

Research in Economics

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“…Koh and Tan (1999) and Calderon (1999) applied a traditional back-propagation network and successfully predicted the going-concern status of US companies. Moshiri and Cameron (2000) applied a similar model to forecast inflation. Pradhan and Kumar (2008), Slim (2009) and Aminian et al (2006) all showed that artificial neural networks perform better than linear models in forecasting economic data.…”

Section: Literature Reviewmentioning

confidence: 99%

A Radial Basis Function Approach to Earnings Forecast

Biscontri

2012

Intell. Sys. Acc. Fin. Mgmt.

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SUMMARY The fundamental management problem of decision making in a climate where future values of important variables are unknown and can at best be estimated using traditional statistical techniques is addressed. The incorporation of forecast models into management decision‐support systems is critical for the overall success of organizational accounting information systems, where managers require confidence in the information that they use. The neural network paradigm has been described as a promising nonparametric approach, negating the required, and sometimes restrictive, statistical assumptions. The application of the neural network paradigm to the area of earnings forecasting is presented. A radial basis function (RBF) approach is developed and tested empirically using data from the Hong Kong Hang Seng 100 Index and macroeconomic data, mimicking an actual business valuation/forecast exercise. Results show that the RBF approach is superior to regression and financial analysts in earnings forecast. Copyright © 2012 John Wiley & Sons, Ltd.

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“…National economies are heavily influenced by the stock exchange. In addition, the stock has been an available investment tool to investors and the public (Moshiri and Cameron, 2000). Forums are not only affected by the macroeconomic parameters but, also by several other factors (Stevenson and Hojati, 2001).…”

Section: Introductionmentioning

confidence: 99%

Comparing accuracy in predicting stock returns between using regression techniques and data mining approach

Kazemian¹

2012

Afr. J. Bus. Manage.

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Size and volatility of the stock price of companies listed on the stock exchange is one of the most important factors influencing the financial markets. Predicting the influencing factors such as price and stock returns in the stock market is essential to the large number of users who are interested in entering the stock market as investors or as attract-business investors. This is because this investment might be a big risk without forecasting tools for estimating possible future events. Several years ago, regression methods were used for predicting market, but neural networks and genetic algorithms have ability to learn complex nonlinear series with better performance. In this paper, from the effective elements in the stock market, stock returns based on their importance to investors are selected for prediction. The research results show that when the samples are optimized with genetic algorithm or neural network to predict stock returns in comparison to regression methods, smaller error was achieved.

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Neural network versus econometric models in forecasting inflation

Cited by 46 publications

References 2 publications

Forecasting UK consumer price inflation using inflation forecasts

Forecasting UK consumer price inflation using inflation forecasts

A Radial Basis Function Approach to Earnings Forecast

Comparing accuracy in predicting stock returns between using regression techniques and data mining approach

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