this article is a comparative study of estimation power of artificial neural networks and autoregressive time series models in inflation forecasting. using 37 years iran’s inflation data, neural networks performs better on average for short horizons than autoregressive models. this study shows usefulness of early stopping technique in learning stage of neural networks for estimating time series...

Many economists expect inflation to rise in 1995. These expectations are based on various approaches to forecasting inflation. One approach is based on the standard economic theory that inflation rises when slack is eliminated from the economy and production exceeds capacity constraints. According to this view, measures of economic slack such as unemployment and capacity utilization provide use...

We propose simulation-based forecasting methods for the noncausal vector autoregressive model proposed by Lanne and Saikkonen (2012). Simulation or numerical methods are required because the prediction problem is generally nonlinear and, therefore, its analytical solution is not available. It turns out that different special cases of the model call for different simulation procedures. Simulatio...

a r t i c l e i n f o In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcompo-nents of the CPI at the highest degree of disaggregation. The data set contains 54 macroeconomic series and 243 CPI subcomponents from 1992 to 2009 for Mexico...

L abor costs have recently come under scrutiny by policymakers, business economists, and financial market participants. The primary concern has been that tight labor markets might lead to faster compensation growth and, ultimately, to upward pressure on general inflation. The employment cost index (ECI) has received particularly close attention because many analysts consider it to be one of the...

Most studies on U.S. inflation forecasting have focused on predicting the mean inflation using time series and Phillips Curve (PC) models. The findings indicate that using real economic indicators (such as unemployment or the output gap) improve out-of-sample forecasting performance during the late 1970s and the first half of the 1980s. But after 1985, PC based forecasts do not lead to forecast...

This paper focuses on modeling and forecasting inflation in India using an augmented Phillips curve framework. Both demand and supply factors are seen as drivers of inflation. Demand conditions are found to have a stronger impact on non-food manufactured products (NFMP) inflation vis-a-vis headline wholesale price inflation; moreover, NFMP inflation is found to be more persistent than headline ...

A substantial decline in market-based measures of inflation expectations has raised concerns about low future inflation. An important question to address is whether these measures contain information that can improve upon alternative forecasting methods. This analysis finds that market-based inflation forecasts generally are no more accurate than surveys of professional forecasters or simple fo...