In this issue we have: Forecasting Demand for Electricity: Some Methodological Issues and an Analysis ; Inflation Forecasting using Artificial Neural Networks ; Revisiting useful approaches to data-rich macroeconomic forecasting ; Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails ; Core measures of inflation as predictors of total inflation.
Date: | 2008-05-29 |
By: | Pillai N., Vijayamohanan |
URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:8899&r=for |
Electricity demand projection is of utmost importance as electricity has become a vital input to the wellbeing of any society, driving the demand for it from an ever-expanding set of diverse needs to grow on an increasing rate, which in turn places increasing demands on scarce resources of capital investment, material means, and man-power. More specifically, the continuing â€˜energy crisisâ€^{TM} has made crucial the need for accurate projection of electricity demand; hence the importance of the forecasting methods. The present paper critically evaluates the electricity demand forecasting methodology and proposes a methodology in the classical time series framework. | |
Keywords: | Electricity demand; Forecasting; Kerala; Time series analysis |
JEL: | C32 L94 |
Date: | 2007-07-13 |
By: | Bukhari, S. Adnan H. A. S. Bukhari Hanif, Muhammad Nadeem |
URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:8898&r=for |
An artificial neural network (ANN) is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. In previous two decades, ANN applications in economics and finance; for such tasks as pattern reorganization, and time series forecasting, have dramatically increased. Many central banks use forecasting models based on ANN methodology for predicting various macroeconomic indicators, like inflation, GDP Growth and currency in circulation etc. In this paper, we have attempted to forecast monthly YoY inflation for Pakistan by using ANN for FY08 on the basis of monthly data of July 1993 to June 2007. We also compare the forecast performance of the ANN model with that of univariate AR(1) model and observed that RMSE of ANN based forecasts is much less than the RMSE of forecasts based on AR(1) model. At least by this criterion forecast based on ANN are m! ore precise. | |
Keywords: | artificial neural network; forecasting; inflation |
JEL: | C51 C53 E31 C52 E37 |
Date: | 2008 |
By: | Jan J. J. Groen George Kapetanios |
URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:327&r=for |
This paper revisits a number of data-rich prediction methods that are widely used in macroeconomic forecasting, such as factor models, Bayesian ridge regression, and forecast combinations, and compares these methods with a lesser known alternative: partial least squares regression. In this method, linear, orthogonal combinations of a large number of predictor variables are constructed such that the linear combinations maximize the covariance between the target variable and each of the common components constructed from the predictor variables. We provide a theorem that shows that when the data comply with a factor structure, principal components and partial least squares regressions provide asymptotically similar results. We also argue that forecast combinations can be interpreted as a restricted form of partial least squares regression. Monte Carlo experiments confirm our theoretical results that principal compon! ents and partial least squares regressions are asymptotically similar when the data has a factor structure. These experiments also indicate that when there is no factor structure in the data, partial least square regression outperforms both principal components and Bayesian ridge regressions. Finally, we apply partial least squares, principal components, and Bayesian ridge regressions on a large panel of monthly U.S. macroeconomic and financial data to forecast CPI inflation, core CPI inflation, industrial production, unemployment, and the federal funds rate across different subperiods. The results indicate that partial least squares regression usually has the best out-of-sample performance when compared with the two other data-rich prediction methods. ; These experiments also indicate that when there is no factor structure in the data, partial least square regression outperforms both principal components and Bayesian ridge regressions. Finally, we apply partial least squar! es, principal components, and Bayesian ridge regressions on a large pa nel of monthly U.S. macroeconomic and financial data to forecast CPI inflation, core CPI inflation, industrial production, unemployment, and the federal funds rate across different subperiods. The results indicate that partial least squares regression usually has the best out-of-sample performance when compared with the two other data-rich prediction methods. | |
Keywords: | Time-series analysis ; Economic forecasting ; Business cycles ; Econometric models |
Date: | 2008-05-20 |
By: | Cees Diks (University of Amsterdam) Valentyn Panchenko (University of New South Wales) Dick van Dijk (Erasmus University Rotterdam) |
URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20080050&r=for |
We propose new scoring rules based on partial likelihood for assessing the relative out-of-sample predictive accuracy of competing density forecasts over a specific region of interest, such as the left tail in financial risk management. By construction, existing scoring rules based on weighted likelihood or censored normal likelihood favor density forecasts with more probability mass in the given region, rendering predictive accuracy tests biased towards such densities. Our novel partial likelihood-based scoring rules do not suffer from this problem, as illustrated by means of Monte Carlo simulations and an empirical application to daily S&P 500 index returns. | |
Keywords: | density forecast evaluation; scoring rules; weighted likelihood ratio scores; partial likelihood; risk management |
JEL: | C12 C22 C52 C53 |
Date: | 2008 |
By: | Theodore M. Crone N. Neil K. Khettry Loretta J. Mester Jason A. Novak |
URL: | http://d.repec.org/n?u=RePEc:fip:fedpwp:08-9&r=for |
Two rationales offered for policymakers' focus on core measures of inflation as a guide to underlying inflation are that core inflation omits food and energy prices, which are thought to be more volatile than other components, and that core inflation is thought to be a better predictor of total inflation over time horizons of import to policymakers. The authors' investigation finds little support for either rationale. They find that food and energy prices are not the most volatile components of inflation and that depending on which inflation measure is used, core inflation is not necessarily the best predictor of total inflation. However, they do find that combining CPI and PCE inflation measures can lead to statistically significant more accurate forecasts of each inflation measure, suggesting that each measure includes independent information that can be exploited to yield better forecasts. | |
Keywords: | Inflation (Finance) |