‎This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...

inflation forecasting has been one of the requirements for the implementation of monetary policy in countries which their monetary authorities are pursuing inflation targeting regime. however, owing to central bank independence in one hand and as well as the lagged effects of monetary policies on inflation in the other, the monetary authorities should have the sound perspective about the future...

Analysts typically use a variety of techniques to forecast inflation. These include both ‘bottom-up’ approaches, for near-term forecasting, as well as econometric methods (such as mark-up models of inflation, which have been found to perform quite well for Australia – see de Brouwer and Ericsson (1998)). One of the econometric approaches to inflation forecasting which is sometimes considered is...

Much of the inflation forecasting literature examines the ability of macroeconomic indicators to accurately predict mean inflation. For the period after 1984, existing empirical evidence largely suggests that the likelihood of accurately predicting inflation using macroeconomic indicators is no better than a random walk model. We expand the scope of inflation predictability by exploring whether...

This paper investigates the forecasting accuracy of the trimmed mean inflation rate of the Personal Consumption Expenditure (PCE) deflator. Earlier works have examined the forecasting ability of limited-influence estimators (trimmed means and the weighted median) of the Consumer Price Index but none have compared the weighted median and trimmed mean of the PCE. Also addressed is the systematic ...

We examine whether the U.S. rate of price inflation has become harder to forecast and, to the extent that it has, what changes in the inflation process have made it so. The main finding is that the univariate inflation process is well described by an unobserved component trend-cycle model with stochastic volatility or, equivalently, an integrated moving average process with time-varying paramet...

The paper researches on the inflation level forecasting for China and the United States in one kind of univariate and three kinds of bivariate cases, using Vector Autoregressive and Bayesian Vector Autoregressive Models, based on the rolling-sample forecasts and the mean absolute percentage error standard. Empirical tests show that, both China and the United States’ inflation level series are s...

We compare the Bank of England’s Inflation Report (IR) quarterly forecasts for growth and inflation to real-time forecasts using a variety of statistical forecasting models that have previously been found useful as forecasting benchmarks. These include linear and non-linear univariate models, and VARs. The results reveal the well-known difficulty of forecasting in a stable macroeconomic environ...

In this paper we evaluate the empirical performance of a medium–scale DSGE model (Smets andWouters 2007) when agents form expectations about forward variables by using small forecasting models. Agents learn about these simple AR and VAR forecasting models through Kalman filter estimation and they combine them either using a prediction basedweighting scheme or fixed weights. The results indicate...

Recent work in the macroeconometric literature considers the problem of summarising efficiently a large set of variables and using this summary for a variety of purposes including forecasting. This paper applies a new factor extraction method to the extraction of core inflation and forecasting of UK inflation in the recent past. JEL Codes:C13, C32