nep-for New Economics Papers
on Forecasting
Issue of 2011‒04‒02
twelve papers chosen by
Rob J Hyndman
Monash University

  1. Evaluating GDP Forecasting Models for Korea By Li Zeng
  2. How useful are estimated DSGE model forecasts? By Rochelle M. Edge; Refet S. Gurkaynak
  3. New Indicators for Tracking Growth in Real Time By Troy Matheson
  4. Commodity prices, commodity currencies, and global economic developments By Paolo A. Pesenti; Jan J.J. Groen
  5. Macroeconomic Variables and South African Stock Return Predictability By Rangan Gupta; Mampho P. Modise
  6. Are Forecast Updates Progressive? By Chia-Lin Chang; Philip Hans Franses; Michael McAleer
  7. Real-Time Nowcasting of GDP: Factor Model versus Professional Forecasters By Liebermann, Joelle
  8. Ranking Multivariate GARCH Models by Problem Dimension By Massimiliano Caporin; Michael McAleer
  9. The expected real return to equity By Missaka Warusawitharana
  10. Macro-financial vulnerabilities and future financial stress: assessing systemic risks and predicting systemic events By Marco Lo Duca; Tuomas A. Peltonen
  11. Fiscal Expectations Under the Stability and Growth Pact: Evidence from Survey Data By Marcos Poplawski-Ribeiro; Jan-Christoph Rulke
  12. A Century of Inflation Forecasts By D'Agostino, Antonello; Surico, Paolo

  1. By: Li Zeng
    Abstract: This paper develops a new forecasting framework for GDP growth in Korea to complement and further enhance existing forecasting approaches. First, a range of forecast models, including indicator- and pure time-series models, are evaluated for their forecasting performance. Based on the evaluation results, a new forecasting framework is developed for GDP projections. The framework also generates a data-driven reference band for the projections, and is therefore convenient to update. The framework is applied to the current World Economic Outlook (WEO) forecast period and the Great Recession to compare its performance to past projections. Results show that the performance of the new framework often improves the forecasts, especially at quarterly frequency, and the forecasting exercise will be better informed by cross-checking with the new data-driven framework projections.
    Keywords: Economic models , Forecasting models , Gross domestic product , Korea, Republic of ,
    Date: 2011–03–11
  2. By: Rochelle M. Edge; Refet S. Gurkaynak
    Abstract: DSGE models are a prominent tool for forecasting at central banks and the competitive forecasting performance of these models relative to alternatives--including official forecasts--has been documented. When evaluating DSGE models on an absolute basis, however, we find that the benchmark estimated medium scale DSGE model forecasts inflation and GDP growth very poorly, although statistical and judgmental forecasts forecast as poorly. Our finding is the DSGE model analogue of the literature documenting the recent poor performance of macroeconomic forecasts relative to simple naive forecasts since the onset of the Great Moderation. While this finding is broadly consistent with the DSGE model we employ--ie, the model itself implies that under strong monetary policy especially inflation deviations should be unpredictable--a "wrong" model may also have the same implication. We therefore argue that forecasting ability during the Great Moderation is not a good metric to judge the usefulness of model forecasts.
    Keywords: Economic forecasting ; Inflation (Finance) ; Econometric models
    Date: 2011
  3. By: Troy Matheson
    Abstract: We develop monthly indicators for tracking growth in 32 advanced and emerging-market economies. We test the historical performance of our indicators and find that they do a good job at describing the business cycle. In a recursive out-of-sample forecasting exercise, we find that the indicators generally produce good GDP growth forecasts relative to a range of time series models.
    Keywords: Business cycles , Developed countries , Economic growth , Economic indicators , Emerging markets , Forecasting models , Time series ,
    Date: 2011–02–24
  4. By: Paolo A. Pesenti; Jan J.J. Groen
    Abstract: In this paper we seek to produce forecasts of commodity price movements that can systematically improve on naive statistical benchmarks, and revisit the forecasting performance of changes in commodity currencies as efficient predictors of commodity prices, a view emphasized in the recent literature. In addition, we consider different types of factor-augmented models that use information from a large data set containing a variety of indicators of supply and demand conditions across major developed and developing countries. These factor-augmented models use either standard principal components or partial least squares (PLS) regression to extract dynamic factors from the data set. Our forecasting analysis considers ten alternative indices and sub-indices of spot prices for three different commodity classes across different periods. We .find that the exchange rate-based model and especially the PLS factor-augmented model are more prone to outperform the naive statistical benchmarks. However, across our range of commodity price indices we are not able to generate out-of-sample forecasts that, on average, are systematically more accurate than predictions based on a random walk or autoregressive specifications.
    JEL: E24 E62 J45
    Date: 2011–03
  5. By: Rangan Gupta (Department of Economics, University of Pretoria); Mampho P. Modise (Department of Economics, University of Pretoria and South African Treasury, Pretoria, South Africa)
    Abstract: We examine both in-sample and out-of-sample predictability of South African stock return using macroeconomic variables. We base our analysis on a predictive regression framework, using monthly data covering the in-sample period between 1990:01 and 1996:12, and the out-of sample period commencing from 1997:01 to 2010:06. For the insample test, we use the t-statistic corresponding to the slope coefficient of the predictive regression model, and for the out-of-sample tests we employ the MSE-F and the ENCNEW test statistics. When using multiple variables in a predictive regression model, the results become susceptible to data mining. To guard against this, we employ a bootstrap procedure to construct critical values that account for data mining. Further, we use a procedure that combines the in-sample general-to-specific model selection with tests of out-of-sample forecasting ability to examine the significance of each macro variable in explaining the stock returns behaviour. For the in-sample tests, our results show that different interest rate variables, world oil production growth, as well as, money supply have some predictive power at certain short-horizons. For the out-of-sample forecasts, only interest rates and money supply show short-horizon predictability. Further, the inflation rate shows very strong out-of-sample predictive power from 6-months-ahead horizons. When accounting for data mining, both the in-sample and the out-of-sample test statistics become insignificant at all horizons. The general-to-specific model confirms the importance of different interest rate variables in explaining the behaviour of stock returns, despite their inability to predict stock returns, when accounting for data mining.
    Keywords: Stock return predictability, Macro variables, In-sample tests, Out-of-sample tests, Data mining, General-to-specific model
    JEL: C22 C52 C53 G12 G14
    Date: 2011–03
  6. By: Chia-Lin Chang (NCHU Department of Applied Economics (Taiwan)); Philip Hans Franses (Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics), Erasmus Universiteit); Michael McAleer (Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics) Erasmus Universiteit, Tinbergen Instituut (Tinbergen Institute).)
    Abstract: Many macro-economic forecasts and forecast updates, such as those from the IMF and OECD, typically involve both a model component, which is replicable, as well as intuition (namely, expert knowledge possessed by a forecaster), which is non-replicable. . Learning from previous mistakes can affect both the replicable component of a model as well as intuition. If learning, and hence forecast updates, are progressive, forecast updates should generally become more accurate as the actual value is approached. Otherwise, learning and forecast updates would be neutral. The paper proposes a methodology to test whether macro-economic forecast updates are progressive, where the interaction between model and intuition is explicitly taken into account. The data set for the empirical analysis is for Taiwan, where we have three decades of quarterly data available of forecasts and their updates of two economic fundamentals, namely the inflation rate and real GDP growth rate. The empirical results suggest that the forecast updates for Taiwan are progressive, and that progress can be explained predominantly by improved intuition.
    Keywords: Macro-economic forecasts, econometric models, intuition, learning, progressive forecast updates, forecast errors.
    JEL: C53 C22 E27 E37
    Date: 2011
  7. By: Liebermann, Joelle (Central Bank of Ireland)
    Abstract: This paper performs a fully real-time nowcasting (forecasting) exercise of US real gross domestic product (GDP) growth using Giannone, Reichlin and Small (2008) factor model framework which enables one to handle unbalanced datasets as available in real-time. To this end, we have constructed a novel real-time database of vintages from October 2000 to June 2010 for a panel of US variables, and can hence reproduce, for any given day in that range, the exact information that was available to a real-time forecaster. We track the daily evolution throughout the current and next quarter of the model nowcasting performance. Similarly to Giannone et al. pseudo realtime results, we find that the precision of the nowcasts increases with information releases. Moreover, the Survey of Professional Forecasters (SPF) does not carry additional information with respect to the model best specification, suggesting that the often cited superiority of the SPF, attributable to judgment, is weak over our sample. Then, as one moves forward along the real-time data flow, the continuous updating of the model provides a more precise estimate of current quarter GDP growth and the SPF becomes stale compared to all the model specifications. These results are robust to the recent recession period.
    Keywords: Real-time data, Nowcasting, Forecasting, Factor model.
    JEL: E52 C53 C33
    Date: 2011–03
  8. By: Massimiliano Caporin (Università di Padova); Michael McAleer (Erasmus University Rotterdam)
    Abstract: In the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Some recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of models, namely BEKK, DCC, Corrected DCC (cDCC) of Aeilli (2008), CCC, Exponentially Weighted Moving Average, and covariance shrinking, using the historical data of 89 US equities. Our methods follow some of the approach described in Patton and Sheppard (2009), and contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC model and covariance shrinking. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Weighted Likelihood Ratio test of Amisano and Giacomini (2007). Third, we examine how the model rankings are influenced by the cross-sectional dimension of the problem.
    Keywords: Covariance forecasting, model confidence set, model ranking, MGARCH, model comparison.
    JEL: C32 C53 C52
    Date: 2010–12
  9. By: Missaka Warusawitharana
    Abstract: The expected return to equity--typically measured as a historical average--is a key variable in the decision making of investors. A recent literature based on analysts forecasts and practitioner surveys finds estimates of expected returns that are sometimes much lower than historical averages. This study presents a novel method that estimates the expected return to equity using only observable data. The method builds on a present value relationship that links dividends, earnings, and investment to market values via expected returns. Given a model that captures this relationship, one can infer the expected return. Using this method, the estimated expected real return to equity ranges from 4 to 5.5 percent. Furthermore, the analysis indicates that expected returns have declined by about 2 percentage points over the past forty years. These results indicate that future returns to equity may be lower than past realized returns.
    Keywords: Stock - Prices ; Forecasting ; Investments ; Securities
    Date: 2011
  10. By: Marco Lo Duca (International Policy Analysis Division, European Central Bank, Kaiserstraße 29, D-60311 Frankfurt am Main, Germany.); Tuomas A. Peltonen (Financial Stability Surveillance Division, European Central Bank, Kaiserstraße 29, D-60311 Frankfurt am Main, Germany.)
    Abstract: This paper develops a framework for assessing systemic risks and for predicting (out-of-sample) systemic events, i.e. periods of extreme financial instability with potential real costs. We test the ability of a wide range of “stand alone” and composite indicators in predicting systemic events and evaluate them by taking into account policy makers’ preferences between false alarms and missing signals. Our results highlight the importance of considering jointly various indicators in a multivariate framework. We find that taking into account jointly domestic and global macrofinancial vulnerabilities greatly improves the performance of discrete choice models in forecasting systemic events. Our framework shows a good out-of-sample performance in predicting the last financial crisis. Finally, our model would have issued an early warning signal for the United States in 2006 Q2, 5 quarters before the emergence of money markets tensions in August 2007. JEL Classification: E44, E58, F01, F37, G01.
    Keywords: Early warning Indicators, Asset Price Booms and Busts, Financial Stress, Macro-Prudential Policies.
    Date: 2011–03
  11. By: Marcos Poplawski-Ribeiro; Jan-Christoph Rulke
    Abstract: The paper uses survey data to analyze whether financial market expectations on government budget deficits changed in France, Germany, Italy, and the United Kingdom during the period of the Stability and Growth Pact (SGP). Our findings indicate that accuracy of financial expert deficit forecasts increased in France. Convergence between the European Commission's and market experts’ deficit forecasts also increased in France, Italy, and the United Kingdom, particularly during the period after SGP’s reform in 2005. Yet, convergence between markets’ forecasts and those of the French, German, and Italian national fiscal authorities seems not to have increased significantly during the SGP.
    Keywords: Budget deficits , Cross country analysis , Economic forecasting , Economic growth , European Economic and Monetary Union , Fiscal policy , Fiscal stability , France , Germany , Italy , United Kingdom ,
    Date: 2011–03–04
  12. By: D'Agostino, Antonello; Surico, Paolo
    Abstract: We investigate inflation predictability in the United States across the monetary regimes of the XXth century. The forecasts based on money growth and output growth were significantly more accurate than the forecasts based on past inflation only during the regimes associated with neither a clear nominal anchor nor a credible commitment to fight inflation. These include the years from the outbreak of World War II in 1939 to the implementation of the Bretton Woods Agreements in 1951, and from Nixon's closure of the gold window in 1971 to the end of Volcker’s disinflation in 1983.
    Keywords: monetary regimes; Phillips curve; predictability; time-varying models
    JEL: E37 E42 E47
    Date: 2011–03

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