Forecasting papers 2008-04-17

In this issue we have: Forecasting the South African Economy ; Forecasting economic and financial variables with global VARs ; Forecast Comparisons in Unstable Environments ; Seasonal dynamic factor analysis and bootstrap inference ; Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change, and more.


  1. Forecasting the South African Economy: A DSGE-VAR Approach
    Date: 2008
    By: Liu, G.
    Gupta, R.
    Schaling, E. (Tilburg University, Center for Economic Research)
    Journal of Economic Literature Classification: E17, E27, E32, E37, E47
    Keywords: DSGE Model;VAR and BVAR Model;Forecast Accuracy;DSGE Forecasts;VAR Forecasts;BVAR Forecasts
  2. Forecasting economic and financial variables with global VARs
    Date: 2008
    By: M. Hashem Pesaran
    Til Schuermann
    L. Vanessa Smith
    This paper considers the problem of forecasting real and financial macroeconomic variables across a large number of countries in the global economy. To this end, a global vector autoregressive (GVAR) model previously estimated over the 1979:Q1-2003:Q4 period by Dees, de Mauro, Pesaran, and Smith (2007) is used to generate out-of-sample one-quarter- and four-quarters-ahead forecasts of real output, inflation, real equity prices, exchange rates, and interest rates over the period 2004:Q1-2005:Q4. Forecasts are obtained for 134 variables from twenty-six regions made up of thirty-three countries and covering about 90 percent of world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the paper examines the effects of model and estimation uncertainty on forecast outcomes by pooling forecasts obtained from different! GVAR models estimated over alternative sample periods. Given the size of the modeling problem and the heterogeneity of the economies considered, industrialized, emerging, and less developed countries, as well as the very real likelihood of multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts performed better than the benchmark forecasts, especially for output, inflation, and real equity prices.
    Keywords: Economic forecasting ; Time-series analysis ; Econometric models ; Vector autoregression
  3. Forecast Comparisons in Unstable Environments
    Date: 2008
    By: Giacomini, Raffaella
    Rossi, Barbara
    We propose new methods for comparing the relative out-of-sample forecasting performance of two competing models in the presence of possible instabilities. The main idea is to develop a measure of the relative "local forecasting performance" for the two models, and to investigate its stability over time by means of statistical tests. We propose two tests (the "Fluctuation test" and the test against a "One-time Reversal") that analyze the evolution of the models' relative performance over historical samples. In contrast to previous approaches to forecast comparison, which are based on measures of "global performance", we focus on the entire time path of the models' relative performance, which may contain useful information that is lost when looking for the model that forecasts best on average. We apply our tests to the analysis of the time variation in the out-of-sample forecasting performance of! monetary models of exchange rate determination relative to the random walk.
    Keywords: Predictive Ability Testing, Instability, Structural Change, Forecast Evaluation
    JEL: C22 C52 C53
  4. Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting
    Date: 2008-03
    By: andrés M. Alonso
    Carolina Garcia-Martos
    Julio Rodriguez
    Maria Jesus Sanchez
    Year-ahead forecasting of electricity prices is an important issue in the current context of electricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in previous published works. Moreover, methodology developed for the short-term does not work properly for long-term forecasting. In this paper we provide a seasonal extension of the Non-Stationary Dynamic Factor Analysis, to deal with the interesting problem (both from the economic and engineering point of view) of long term forecasting of electricity prices. Seasonal Dynamic Factor Analysis (SeaDFA) allows to deal with dimensionality reduction in vectors of time series, in such a way that extracts common and specific components. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal one, by means of common factors following a multiplicative seasonal VARIMA(p,d,q)×(P,D,Q)s mode! l. Besides, a bootstrap procedure is proposed to be able to make inference on all the parameters involved in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing to enhance the coverage of forecast confidence intervals. Concerning the innovative and challenging application provided, bootstrap procedure developed allows to calculate not only point forecasts but also forecasting intervals for electricity prices.
    Keywords: Dynamic factor analysis, Bootstrap, Forecasting, Confidence intervals
    JEL: C32 C53
  5. Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change
    Date: 2008-02
    By: Banerjee, Anindya
    Marcellino, Massimiliano
    Masten, Igor
    We conduct a detailed simulation study of the forecasting performance of diffusion index-based methods in short samples with structural change. We consider several data generation processes, to mimic different types of structural change, and compare the relative forecasting performance of factor models and more traditional time series methods. We find that changes in the loading structure of the factors into the variables of interest are extremely important in determining the performance of factor models. We complement the analysis with an empirical evaluation of forecasts for the key macroeconomic variables of the Euro area and Slovenia, for which relatively short samples are officially available and structural changes are likely. The results are coherent with the findings of the simulation exercise, and confirm the relatively good performance of factor-based forecasts in short samples with structural change.
    Keywords: Factor models; forecasts; parameter uncertainty; short samples; structural change; time series models
    JEL: C32 C53 E37
  6. The Inefficient Use of Macroeconomic Information in Analysts' Earnings Forecasts in Emerging Markets
    Date: 2008-03-03
    By: Zwart, G. de
    Dijk, D.J.C. van (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
    This paper presents empirical evidence that security analysts do not efficiently use publicly available macroeconomic information in their earnings forecasts for emerging market stocks. Analysts completely ignore forecasts on political stability, while these provide valuable information for firm-level earnings growth. Analysts do incorporate output growth forecasts, but these actually bear no relevant information for firm-level earnings growth. Inflation forecasts are taken into account correctly. In addition, the information environment appears to be crucially important in emerging markets, as we find evidence that analysts handle macroeconomic information in a better way for more transparent firms.
    Keywords: analysts' earnings forecasts;emerging markets;macroeconomic forecasts;forecast accuracy
  7. Short-term Forecasts of Euro Area GDP Growth
    Date: 2008-03
    By: Angelini, Elena
    Camba-Mendez, Gonzalo
    Giannone, Domenico
    Reichlin, Lucrezia
    Rünstler, Gerhard
    This paper evaluates models that exploit timely monthly releases to compute early estimates of current quarter GDP (now-casting) in the euro area. We compare traditional methods used at institutions with a new method proposed by Giannone, Reichlin and Small, 2005. The method consists in bridging quarterly GDP with monthly data via a regression on factors extracted from a large panel of monthly series with different publication lags. We show that bridging via factors produces more accurate estimates than traditional bridge equations. We also show that survey data and other `soft' information are valuable for now-casting.
    Keywords: Factor Model; Forecasting; Large data-sets; Monetary Policy; News; Real Time Data
    JEL: C33 C53 E52
  8. Factor-MIDAS for now- and forecasting with ragged-edge data: A model comparison for German GDP
    Date: 2008-02
    By: Marcellino, Massimiliano
    Schumacher, Christian
    This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the `ragged edge' of the data. Using a large monthly dataset of the German economy, we compare the performance of different factor models in the presence of the ragged edge: static and dynamic principal components based on realigned data, the Expectation-Maximisation (EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors are used to estimate current quarter GDP, called the `nowcast', using different versions of what we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible combinations of factor estimation methods and Factor-MIDAS projections wit! h respect to nowcast performance. Additionally, we compare the performance of the nowcast factor models with the performance of quarterly factor models based on time-aggregated and thus balanced data, which neglect the most timely observations of business cycle indicators at the end of the sample. Our empirical findings show that the factor estimation methods don't differ much with respect to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor models that can exploit ragged-edge data
    Keywords: business cycle; large factor models; MIDAS; missing values; mixed-frequency data; nowcasting
    JEL: C53 E37
  9. Why so Glum? The Meese-Rogoff Methodology Meets the Stock Market
    Date: 2008-02
    By: Flood, Robert P
    Rose, Andrew K
    This paper applies the Meese-Rogoff (1983a) methodology to the stock market. We compare the out-of-sample forecasting accuracy of various time-series and fundamentals-based models of aggregate stock prices. We stick as close as possible to the original Meese-Rogoff sample and methodology. Just as Meese and Rogoff found for the case of exchange rates, we find that a random walk model of stock prices performs as well as any estimated model at one to twelve month horizons, even though we base forecasts on actual future fundamentals of dividends and earnings. Using this metric and for this sample period, aggregate stock prices seem to be as difficult to model empirically as exchange rates.
    Keywords: aggregate; dividend; earning; exchange; forecast; fundamental; growth; model; rate
    JEL: F37 G12

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