Monetary Policy Analysis in Real-Time. Vintage Combination from a Real-Time Dataset.

This paper provides a general strategy for analyzing monetary policy in real time which accounts for data uncertainty without explicitly modelling the revision process. The strategy makes use of all the data available from a real-time data matrix and averages model estimates across all data releases. Using standard forecasting and policy models to analyze monetary authorities’ reaction functions, we show that this simple method can improve forecasting performance and provide reliable estimates of the policy model coefficients associated with small central bank losses, in particular during periods of high macroeconomic uncertainty.monetary policy, Taylor rule, real-time data, great moderation, forecasting

Monetary Policy Analysis in Real-Time. Vintage combination from a real-time dataset

This paper provides a general strategy for analyzing monetary policy in real time which accounts for data uncertainty without explicitly modelling the revision process. The strategy makes use of all the data available from a real-time data matrix and averages model estimates across all data releases. Using standard forecasting and policy models to analyze monetary authorities’ reaction functions, we show that this simple method can improve forecasting performance and provide reliable estimates of the policy model coe¢cients associated with small central bank losses, in particular during periods of high macroeconomic uncertainty.Monetary policy, Taylor rule, Real-time data, Great Moderation, Forecasting.

Forecasting and Forecast Combination in Airline Revenue Management Applications

Predicting a variable for a future point in time helps planning for unknown future situations and is common practice in many areas such as economics, finance, manufacturing, weather and natural sciences. This paper investigates and compares approaches to forecasting and forecast combination that can be applied to service industry in general and to airline industry in particular. Furthermore, possibilities to include additionally available data like passenger-based information are discussed

Adaptive forecasting in the presence of recent and ongoing structural change

We consider time series forecasting in the presence of ongoing structural change where both the time series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially weighted moving averages, known to be robust to historical structural change, are found to be also useful in the presence of ongoing structural change in the forecast period. A crucial issue is how to select the degree of downweighting, usually defined by an arbitrary tuning parameter. We make this choice data dependent by minimizing forecast mean square error, and provide a detailed theoretical analysis of our proposal. Monte Carlo results illustrate the methods. We examine their performance on 191 UK and US macro series. Forecasts using data-based tuning of the data discount rate are shown to perform well

Forecasting Time Series Subject to Multiple Structural Breaks

This paper provides a novel approach to forecasting time series subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks over the forecast horizon, taking account of the size and duration of past breaks (if any) by means of a hierarchical hidden Markov chain model. Predictions are formed by integrating over the hyper parameters from the meta distributions that characterize the stochastic break point process. In an application to US Treasury bill rates, we find that the method leads to better out-of-sample forecasts than alternative methods that ignore breaks, particularly at long horizons.structural breaks, forecasting, hierarchical hidden Markov chain model, Bayesian model averaging.

Review of Nature-Inspired Forecast Combination Techniques

Effective and efficient planning in various areas can be significantly supported by forecasting a variable like an economy growth rate or product demand numbers for a future point in time. More than one forecast for the same variable is often available, leading to the question whether one should choose one of the single models or combine several of them to obtain a forecast with improved accuracy. In the almost 40 years of research in the area of forecast combination, an impressive amount of work has been done. This paper reviews forecast combination techniques that are nonlinear and have in some way been inspired by nature

Forecast quality and simple instrument rules: a real-time data approach

We start from the assertion that a useful monetary policy design should be founded on more realistic assumptions about what policymakers can know at the time when policy decisions have to be made. Since the Taylor rule – if used as an operational device - implies a forward looking behaviour, we analyze the reliability of the input information. We investigate the forecasting performance of OECD projections for GDP growth rates and inflation. We diagnose a much better forecasting record for inflation rates compared to GDP growth rates, which for most countries are almost uninformative at the time a Taylor rule should sensibly be applied. Using this data set, we find significant differences between Taylor rules estimated over revised data compared to real-time data. There is evidence that monetary policy seems to react more actively in real time than rules estimated over revised data suggest. Given the evidence of systematic errors in OECD forecasts, in a next step we attempt to correct for these forecast biases and check to which extent this can lower the errors in interest rate policy setting. An ex-ante simulation for the years 1991 to 2001 supports the proposal that correcting for forecast errors and biases based on an error model can lower the resulting policy error in interest rate setting for most countries under consideration. In addition we investigate to what extent structural changes in the policy reaction behaviour can be handled with moving instead of expanding samples. Our results point out that the information set available needs a careful examination when applied to instrument rules like those of the Taylor type. Limited forecast quality and significant data revisions recommend a more sophisticated handling of the dated information, for which we present an operational procedure that has the potential of reducing the risk of severe policy errors. --Monetary policy rules,economic forecasting,OECD,real-time data

Forecasting Organic Food Prices: Testing and Evaluating Conditional Predictive Ability

Organic farmers, wholesalers, and retailers need reliable price forecasts to improve their decision- making practices. This paper presents a methodology and protocol to select the best-performing method from several time and frequency domain candidates. Weekly farmgate prices for organic fresh produce are used. Forecasting methods are evaluated on the basis of an aggregate accuracy measure and several out-of-sample predictive ability tests. Combining forecasts to improve on individual forecasts is investigated.Demand and Price Analysis,