Forecasting Chilean Inflation From Disaggregate Components

In this paper an exercise is performed to determine the usefulness of utilizing disaggregated price data to forecast headline inflation more accurately. A number of methods based on univariate and multivariate autoregressive models are used for different levels of disaggregation for a period of stable inflation and a period of accelerating inflation. The results show that a certain level of disaggregation could be beneficial when inflation is not low and stable, suggesting that under certain circumstances the disaggregate approach captures the underlying dynamics of inflation more efficiently. The benefits are noticeable for the three-, six- and twelve-month horizons, as opposed to the one-month horizon, where improvements seem negligible.

Considerations on economic forecasting: method developed in the bulletin of EU and US inflation and macroeconomic analysis

This article presents economic forecasting as an activity acquiring full significance when it is involved in a decision-making process. The activity requires a sequence of functions consisting of gathering and organising data, the construction of econometric models and ongoing forecast evaluations to maintain a continuous process involving correction, perfecting and enlarging the data set and the econometric models used, systematically improving forecasting accuracy. With this approach, economic forecasting is an activity based on econometric models and statistical methods, applied economic research with all its general problems. One of these is related to economic data. The widespread belief that if economic information is published, it is valid fo

Forecasting linear dynamical systems using subspace methods

A new procedure to predict with subspace methods is presented in this paper. It is based on combining multiple forecasts obtained from setting a range of values for a specic parameter that is typically xed by the user in the subspace methods literature. An algorithm to compute these predictions and to obtain a suitable number of combinations is provided. The procedure is illustrated by forecasting the German gross domestic product.Forecasting, Subspace methods, Combining forecasts.

FORECAST CONTENT AND CONTENT HORIZONS FOR SOME IMPORTANT MACROECONOMIC TIME SERIES

For quantities that are approximately stationary, the information content of statistical forecasts tends to decline as the forecast horizon increases, and there exists a maximum horizon beyond which forecasts cannot provide discernibly more information about the variable than is present in the unconditional mean (the content horizon). The pattern of decay of forecast content (or skill) with increasing horizon is well known for many types of meteorological forecasts; by contrast, little generally-accepted information about these patterns or content horizons is available for economic variables. In this paper we attempt to develop more information of this type by estimating content horizons for variety of macroeconomic quantities; more generally, we characterize the pattern of decay of forecast content as we project farther into the future. We find wide variety of results for the different macroeconomic quantities, with models for some quantities providing useful content several years into the future, for other quantities providing negligible content beyond one or two months or quarters.

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201

Should we be surprised by the unreliability of real-time output gap estimates? Density estimates for the Euro area

Recent work has found that, without the benefit of hindsight, it can prove difficult for policy-makers to pin down accurately the current position of the output gap; real-time estimates are unreliable. However, attention primarily has focused on output gap point estimates alone. But point forecasts are better seen as the central points of ranges of uncertainty; therefore some revision to real-time estimates may not be surprising. To capture uncertainty fully density forecasts should be used. This paper introduces, motivates and discusses the idea of evaluating the quality of real-time density estimates of the output gap. It also introduces density forecast combination as a practical means to overcome problems associated with uncertainty over the appropriate output gap estimator. An application to the Euro area illustrates the use of the techniques. Simulated out-of-sample experiments reveal that not only can real-time point estimates of the Euro area output gap be unreliable, but so can measures of uncertainty associated with them. The implications for policy-makers use of Taylor-type rules are discussed and illustrated. We find that Taylor-rules that exploit real-time output gap density estimates can provide reliable forecasts of the ECB's monetary policy stance only when alternative density forecasts are combinedOutput gap; Real-Time; Density Forecasts; Density Forecast Combination; Taylor Rules