We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. We call the model Dynamic Factor GARCH, as the information contained in large macroeconomic or financial datasets is captured by a few dynamic common factors, which we assume being conditionally heteroskedastic. After describing the estimation of the model, we present simulation res...

This paper investigates core inflation defined as the best predictor of inflation. I compare forecasts obtained using the mean, weighted median, trimmed mean, and less food and energy inflation rates for the consumer price index and the personal consumption expenditure deflator for the current U.S. monetary policy regime. Another issue addressed is that of the systematic bias that exists due to...

This paper presents an application of Artificial Neural Network (ANN) to forecast inflation in India during the period 1994-2009. The study presents four different ANN models on the basis of inflation (WPI), economic growth (IIP), and money supply (MS). The first model is a univariate model based on past WPI only. The other three are multivariate models based on WPI and IIP, WPI and MS, WPI, an...

Recent studies by Gali and Gertler (1999) and Sbordone (2002) conclude that a theoretical inflation series implied by the forward-looking New Keynesian pricing model of Calvo (1983) fits post-1960 U.S. inflation closely. Their theoretical inflation series is conditional on (i) a reduced-form forecasting process for real marginal cost; and (ii) the calibration of the structural pricing equation ...

In this paper, we investigate whether incorporating common factors of CPI sub-aggregates into forecasting models increases the accuracy of forecasts of inflation. We extract factors by both static and dynamic factor models and then embed them in ARMA and VAR models. Using quarterly data of Iran’s CPI and its sub-aggregates, the models are estimated over 1990:2 to 2008:2 and out of sample ...

We examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to two years....

In this paper an ANN model is developed to forecast the monthly inflation of Bangladesh as a function of its own previous value. The model selects a feed-forward backpropagation ANN with two inputs, one hidden layer with five hidden neurons and one output as the optimum network. The model is tested with actual time series data of inflation in case of Bangladesh and forecast evaluation criteria....

In this paper we focus on the development of multiple time series models for forecasting Irish Inflation. The Bayesian approach to the estimation of vector autoregressive (VAR) models is employed. This allows the estimated models combine the evidence in the data with any prior information which may also be available. A large selection of inflation indicators are assessed as potential candidates...

There is a significant amount of empirical evidence to suggest that the yield curve is useful for forecasting inflation, recessions, and possibly even the growth rate of real output. This essay considers the theoretical reasons why the yield curve may have these predictive properties. It is found that the essential reason why the yield curve predicts inflation and recessions is that it, through...