Evaluating real-time forecasts in real-time

The accuracy of real-time forecasts of macroeconomicvariables that are subject to revisions may crucially depend on thechoice of data used to compare the forecasts against. We put forwarda flexible time-varying parameter regression framework to obtainearly estimates of the final value of macroeconomic variables basedupon the initial data release that may be used as actuals in currentforecast evaluation. We allow for structural changes in theregression parameters to accommodate benchmark revisions anddefinitional changes, which fundamentally change the statisticalproperties of the variable of interest, including the relationshipbetween the final value and the initial release. The usefulness ofour approach is demonstrated through an empirical applicationcomparing the accuracy of forecasts of US GDP growth rates from theSurvey of Professional Forecasters and the Greenbook.forecast evaluation;Bayesian estimation;structural breaks;data revision;parameter uncertainty

Predictive gains from forecast combinations using time-varying model weights

Several frequentist and Bayesian model averaging schemes, including a new one that simultaneously allows for parameter uncertainty, model uncertainty and time varying model weights, are compared in terms of forecast accuracy over a set of simulation experiments. Artificial data are generated, characterized by low predictability, structural instability, and fat tails, which is typical for many financial-economic time series. Sensitivity of results with respect to misspecification of the number of included predictors and the number of included models is explored. Given the set up of our experiments, time varying model weight schemes outperform other averaging schemes in terms of predictive gains both when the correlation among individual forecasts is low and the underlying data generating process is subject to structural locations shifts. In an empirical application using returns on the S&P 500 index, time varying model weights provide improved forecasts with substantial economic gains in an investment strategy including transaction costs.Bayesian model averaging;forecast combination;stock return predictability;time-varying weight combination

A New monthly indicator of global real economic activity

In modelling macroeconomic time series, often a monthly indicator of global real economic activity is used. We propose a new indicator, named World steel production, and compare it to other existing indicators, precisely the Kilian’s index of global real economic activity and the index of OECD World industrial production. We develop an econometric approach based on desirable econometric properties in relation to the quarterly measure of World or global gross domestic product to evaluate and to choose across different alternatives. The method is designed to evaluate short-term, long-term and predictability properties of the indicators. World steel production is proven to be the best monthly indicator of global economic activity in terms of our econometric properties. Kilian’s index of global real economic activity also accurately predicts World GDP growth rates. When extending the analysis to an out-ofsample exercise, both Kilian’s index of global real economic activity and the World steel production produce accurate forecasts for World GDP, confirming evidence provided by the econometric properties. Specifically, a forecast combination of the three indices produces statistically significant gains up to 40% at nowcast and more than 10% at longer horizons relative to an autoregressive benchmark

World steel production: a new monthly indicator of global real economic activity

In this paper we propose a new indicator of monthly global real economic activity, named world steel production. We use world steel production, OECD industrial production index and Kilian’s rea index to forecast world real GDP, and key commodity prices. We find that world steel production generates large statistically significant gains in forecasting world real GDP and oil prices, relative to an autoregressive benchmark. A forecast combination of the three indices produces statistically significant gains in forecasting world real GDP, oil, natural gas, gold and fertilizer prices, relative to an autoregressive benchmark

Bayesian near-boundary analysis in basic macroeconomic time series models

Several lessons learnt from a Bayesian analysis of basic macroeconomic time series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time series models likeunit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.MCMC;Bayesian model averaging;Gibbs sampler;autocorrelation;error correction models;nonstationarity;random effects panel data models;reduced rank models;state space models

Forecasting Financial Time Series Using Model Averaging

In almost all cases a decision maker cannot identify ex ante the true process. This observation has led researchers to introduce several sources of uncertainty in forecasting exercises. In this context, the research reported in these pages finds an increase of forecasting power of financial time series when parameter uncertainty, model uncertainty and optimal decision making are included. The research contained herein evidences that although the implementation of these techniques is not often straightforward and it depends on the exercise studied, the predictive gains are statistically and economically significant over different applications, such as stock, bond and electricity markets

Stock and foreign exchange markets in the Pacific Basin Rim

The thesis examines the stock and foreign exchange markets of a group of Pacific Basin countries. The main purpose is to investigate the role of foreign ownership restrictions and taxes on potential linkages between these markets, and their interrelations with the rest of the world. The overall analysis highlights the presence of substantial financial links at the regional and global level. In particular, it shows close financial links even for markets with extensive capital controls. It also finds linkages between their stock and foreign exchange markets and that foreign currency risk is a significant component of domestic stock returns. When examining for potential sources of these close financial links, the research indicates that Country Funds have provided indirect ways of foreign participation in the local stock markets and contributed to these financial links. Furthermore, the thesis also emphasised the role of economic integration. of the Pacific Basin countries for their financial integration. The study found that the Asian financial crisis of mid 1997 had some effects on the financial links of the Pacific Basin Rim at the regional and global level. In addition, while the turmoil has increased the economic integration at the regional level, it has reduced economic integration with the U. S. However, the thesis shows that neither Japan, nor the U. S., dominates the Pacific Basin Rim. Some countries, such as Thailand, present closer links with the U. S., and others, such as Korea and Taiwan, with Japan. The evidence provided in the thesis has implications for international portfolio diversification and for the use of foreign exchange restrictions to isolate local capital markets from world market influences

Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data

We propose a multivariate combination approach to prediction based on a distributional state space representation of the weights belonging to a set of Bayesian predictive densities which have been obtained from alternative models. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models are individually misspecified. The approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of US macroeconomic time series and surveys of stock market prices. For the macro series we find that incompleteness of the models is relatively large in the 70's, the beginning of the 80's and during the recent financial crisis; structural changes like the Great Moderation are empirically identified by our model combination and the predicted probabilities of recession accurately compare with the NBER business cycle dating. Model weights have substantial uncertainty attached and neglecting this may seriously affect results. With respect to returns of the S&P 500 series, we find that an investment strategy using a combination of predictions from professional forecasters and from a white noise model puts more weight on the white noise model in the beginning of the 90's and switches to giving more weight to the left tail of the professional forecasts during the start of the financial crisis around 2008

Markov Switching Panel with Endogenous Synchronization Effects

This paper introduces a new dynamic panel model with multi-layer network effects. Series-specific latent Markov chain processes drive the dynamics of the observable processes, and several types of interaction effects among the hidden chains allow for various degrees of endogenous synchronization of both latent and observable processes. The interaction is driven by a multi-layer network with exogenous and endogenous connectivity layers. We provide some theoretical properties of the model, develop a Bayesian inference framework and an efficient Markov Chain Monte Carlo algorithm for estimating parameters, latent states, and endogenous network layers. An application to the US-state coincident indicators shows that the synchronization in the US economy is generated by network effects among the states. The inclusion of a multi-layer network provides a new tool for measuring the effects of the public policies that impact the connectivity between the US states, such as mobility restrictions or job support schemes. The proposed new model and the related inference are general and may nd application in a wide spectrum of datasets where the extraction of endogenous interaction effects is relevant and of interest.This paper introduces a new dynamic panel model with multi-layer network effects. Series-specific latent Markov chain processes drive the dynamics of the observable processes, and several types of interaction effects among the hidden chains allow for various degrees of endogenous synchronization of both latent and observable processes. The interaction is driven by a multi-layer network with exogenous and endogenous connectivity layers. We provide some theoretical properties of the model, develop a Bayesian inference framework and an efficient Markov Chain Monte Carlo algorithm for estimating parameters, latent states, and endogenous network layers. An application to the US-state coincident indicators shows that the synchronization in the US economy is generated by network effects among the states. The inclusion of a multi-layer network provides a new tool for measuring the effects of the public policies that impact the connectivity between the US states, such as mobility restrictions or job support schemes. The proposed new model and the related inference are general and may find application in a wide spectrum of datasets where the extraction of endogenous interaction effects is relevant and of interest