
on Forecasting 
By:  Konstantinos Nikolopoulos; Vassilios Assimakopoulos; Nikolaos Bougioukos; Fotios Petropoulos 
Abstract:  The Theta model created a lot of interest in academic circles due to its surprising performance in the M3competition. However, this interest was not followed by a large number of studies, with the exception of Hyndman and Billah in 2003. The present study discusses the advances in the model that have been made in the last five years and attempts to provide further insights into the research question: “Is the Theta model just a special case of Simple Exponential Smoothing with drift (SESd)?” If we do not use equally weighted extrapolations of two specific Theta Lines, L(T=0) and L(T=2) in the Theta model then we end up with a far more generic model than Simple Exponential Smoothing. The paper also examines the potential of an optimization version of SESd so as to test the results of Hyndman and Billah. In contrast to their research results, the Theta model outperforms SESd in the QuarterlyM3 and OtherM3 subsets by 0.30% and 0.36% respectively, when the Symmetric Mean Absolute Percentage Error is used to measure accuracy. 
Keywords:  Decomposition, Extrapolation, Theta model, Exponential Smoothing, M3Competition. 
Date:  2008 
URL:  http://d.repec.org/n?u=RePEc:uop:wpaper:0023&r=for 
By:  Helge Berger; PÃ¤r Ã–sterholm 
Abstract:  We use a meanadjusted Bayesian VAR model as an outofsample forecasting tool to test whether money growth Grangercauses inflation in the euro area. Based on data from 1970 to 2006 and forecasting horizons of up to 12 quarters, there is surprisingly strong evidence that including money improves forecasting accuracy. The results are very robust with regard to alternative treatments of priors and sample periods. That said, there is also reason not to overemphasize the role of money. The predictive power of money growth for inflation is substantially lower in more recent sample periods compared to the 1970s and 1980s. This cautions against using moneybased inflation models anchored in very long samples for policy advice. 
Keywords:  Inflation , Euro Area , Demand for money , Monetary policy , Monetary aggregates , 
Date:  2008–03–04 
URL:  http://d.repec.org/n?u=RePEc:imf:imfwpa:08/53&r=for 
By:  Jan J.J. Groen (Federal Reserve Bank of New York); George Kapetanios (Queen Mary, University of London) 
Abstract:  This paper revisits a number of datarich prediction methods, like factor models, Bayesian ridge regression and forecast combinations, which are widely used in macroeconomic forecasting, and compares these with a lesser known alternative method: partial least squares regression. Under the latter, linear, orthogonal combinations of a large number of predictor variables are constructed such that these linear combinations maximize the covariance between the target variable and each of the common components constructed from the predictor variables. We provide a theorem that shows that when the data comply with a factor structure, principal components and partial least squares regressions provide asymptotically similar results. We also argue that forecast combinations can be interpreted as a restricted form of partial least squares regression. Monte Carlo experiments confirm our theoretical result that principal components and partial least squares regressions are asymptotically similar when the data has a factor structure. These experiments also indicate that when there is no factor structure in the data, partial least squares regression outperforms both principal components and Bayesian ridge regressions. Finally, we apply partial least squares, principal components and Bayesian ridge regressions on a large panel of monthly U.S. macroeconomic and financial data to forecast, for the United States, CPI inflation, core CPI inflation, industrial production, unemployment and the federal funds rate across different subperiods. The results indicate that partial least squares regression usually has the best outofsample performance relative to the two other datarich prediction methods. 
Keywords:  Macroeconomic forecasting, Factor models, Forecast combination, Principal components, Partial least squares, (Bayesian) ridge regression 
JEL:  C22 C53 E37 E47 
Date:  2008–03 
URL:  http://d.repec.org/n?u=RePEc:qmw:qmwecw:wp624&r=for 
By:  Lisandro Abrego; PÃ¤r Ã–sterholm 
Abstract:  This paper investigates the sensitivity of Colombian GDP growth to the surroundingmacroeconomic environment. We estimate a Bayesian VAR model with informative steadystatepriors for the Colombian economy using quarterly data from 1995 to 2007. A variancedecomposition shows that world GDP growth and government spending are the most importantfactors, explaining roughly 17 and 16 percent of the variance in Colombian GDP growthrespectively. The model, which is shown to forecast well outofsample, can also be used toanalyse alternative scenarios. Generating both endogenous and conditional forecasts, we showthat the impact on Colombian GDP growth of a substantial downturn in world GDP growthwould be nonnegligible but still a mild decline by historical standards. 
Keywords:  Economic growth , Colombia , Private investment , Forecasting models , 
Date:  2008–02–25 
URL:  http://d.repec.org/n?u=RePEc:imf:imfwpa:08/46&r=for 
By:  YuChin Chen; Kenneth Rogoff; Barbara Rossi 
Abstract:  This paper demonstrates that "commodity currency" exchange rates have remarkably robust power in predicting future global commodity prices, both insample and outofsample. A critical element of our insample approach is to allow for structural breaks, endemic to empirical exchange rate models, by implementing the approach of Rossi (2005b). Aside from its practical implications, our forecasting results provide perhaps the most convincing evidence to date that the exchange rate depends on the present value of identifiable exogenous fundamentals. We also find that the reverse relationship holds; that is, that commodity prices Grangercause exchange rates. However, consistent with the vast postMeeseRogoff (1983a,b) literature on forecasting exchange rates, we find that the reverse forecasting regression does not survive outofsample testing. We argue, however, that it is quite plausible that exchange rates will be better predictors of exogenous commodity prices than viceversa, because the exchange rate is fundamentally forward looking. Therefore, following Campbell and Shiller (1987) and Engel and West (2005), the exchange rate is likely to embody important information about future commodity price movements well beyond what econometricians can capture with simple time series models. In contrast, prices for most commodities are extremely sensitive to small shocks to current demand and supply, and are therefore likely to be less forward looking. 
JEL:  C52 C53 F31 F47 
Date:  2008–03 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:13901&r=for 
By:  Ignacio VelezPareja 
Abstract:  In this teaching note I list some suggestions that might be useful to take into account when forecasting financial statements departing from historical data. The ideas presented in this note are the result of advising undergraduate and graduate students in the course Econ 195.96/295.96 (Crosslisted: PubPol 264.96): Cash Flow Valuation (CFV): A Basic Introduction to an Integrated Marketbased Approach at Duke University during the Fall 2005 and my previous experience of teaching the subject at Politécnico Grancolombiano in Bogotá, and other universities in Colombia. The note is divided in four sections: In Section One, Analyzing the Historical Financial Statements, is related to the analysis and use of historical information from the financial statements. In Section Two I mention some tips related to the construction of forecasted financial statements. In Section Three I present a list of tips related to the proper way to valuate the cash flows. In Section Four a brief summary is presented. There are three appendixes that can be applied to cases of ongoing concerns and to cases of new firms or projects. The first appendix has a list of possible variables to be taken into account in the forecast, the second one is a summary on how to proceed to forecast nominal price increases and nominal interest rates and the third one illustrates the use of the implicit deflator of the Gross Domestic Product, GDP and the Produces Price Index for the same task. 
Date:  2008–03–10 
URL:  http://d.repec.org/n?u=RePEc:col:000162:004560&r=for 