in this paper, i develop three forecasting models: namely structural, times series, and artificial neural networks; to forecast iranian inflation rates. the structural model uses aggregate demand and aggregate supply approach, the time series model is based on the standard arlma technique, and the artificial neural network applies multi-layer back propagation model the latter, which is rooted i...

In this paper, Inflation constitutes one of the major economic problems in emerging market economies that requires monetary authorities to elaborate tools and policies to prevent high volatility in prices and long periods of inflation. This paper outlines to forecast monthly inflation rate of India by using neural networks on the evaluation of set of variables. The data used for estimating the ...

We test the importance of multivariate information for modelling and forecasting inflation’s conditional mean and variance. In the literature, the existence of inflation’s conditional heteroskedasticity has been debated for years, as it seemed to appear only in some datasets and for some lag lengths. This phenomenon might be due to the fact that inflation depends on a linear combination of econ...

today, the discussion of regularity monetary policy in achieving inflation and output gap stabilization is important. in this paper, we investigated the persistence of the output gap in iran economy and then by using a hybrid new keynesian model based on quarterly data for 1990:1- 2011:3. we compared three alternative instrumental rules in monetary policy for iran economy. the results indicated...

This paper studies the effects the implementation of an inflation targeting (IT) regime has on the dynamics of inflation in Mexico. We contextualize the need for a regime change and study whether the implementation of this regime has made a considerable change in the dynamics of inflation in Mexico. Consumer prices are modelled using a markup model. The markup model suggests a long run relation...

Guy Debelle’s paper makes a strong case for the adoption of an inflation-targeting framework by east Asian countries. This is in stark contrast to the conclusion reached by Masson, Savastano and Sharma (1997), who claim that for most developing countries, inflation targeting is not suitable because central banks lack the ability to conduct an independent monetary policy, their inflation forecas...

This paper evaluates the usefulness of neural networks for inflation forecasting. In a pseudo out-of-sample forecasting experiment using recent U.S. data, neural networks outperform univariate autoregressive models on average for short horizons of 1 and 2 quarters. A simple specification of the neural network model and specialized estimation procedures from the neural networks literature appear...

Bank of Canada working papers are theoretical or empirical works-in-progress on subjects in economics and finance. The views expressed in this paper are those of the authors. No responsibility for them should be attributed to the Bank of Canada. Abstract We provide a decomposition of nominal yields into real yields, expectations of future inflation and inflation risk premiums when real bonds or...

We develop a parsimonious bivariate model of inflation and unemployment that allows for persistent variation in trend inflation and the NAIRU. The model, which consists of five unobserved components (including the trends) with stochastic volatility, implies a time-varying VAR for changes in the rates of inflation and unemployment. The implied backwards-looking Phillips curve has a time-varying ...

The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is UK inflation and we utilize monthly data from 1969-2003. The RS-VAR and the RNN perform approximately on par over both monthly a...