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on Forecasting |
| By: | Gary Koop (University of Strathclyde; The Rimini Centre for Economic Analysis (RCEA)) |
| Abstract: | This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We ?nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts. |
| Keywords: | Bayesian, Minnesota prior, stochastic search variable selection, predictive likelihood |
| Date: | 2010–01 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:43_10&r=for |
| By: | N. Kundan Kishor; Evan F. Koenig |
| Abstract: | We undertake a real-time VAR analysis of the usefulness of the term spread, the junk-bond spread, the ISM's New Orders Index, and broker/dealer equity for predicting growth in non-farm employment. To get around the "apples and oranges" problem described by Koenig, Dolmas and Piger (2003), we augment each VAR we consider with a flexible state-space model of employment revisions. This methodology produces jobs forecasts consistently superior to those obtained using conventional VAR analysis. They are also superior to Federal Reserve Greenbook forecasts and to median forecasts from the Survey of Professional Forecasters. The junk-bond spread is by far the best single predictor of future jobs growth. However, the term spread has some incremental predictive power at medium-to-long horizons. The incremental predictive power of broker/dealer equity, while small, exceeds that of the ISM index at every horizon. |
| Keywords: | Employment forecasting ; Asset pricing |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddwp:1008&r=for |
| By: | Elena Andreou; Eric Ghysels; Andros Kourtellos |
| Date: | 2010–11 |
| URL: | http://d.repec.org/n?u=RePEc:ucy:cypeua:10-2010&r=for |
| By: | Candelon Bertrand; Dumitrescu Elena-Ivona; Hurlin Christophe (METEOR) |
| Abstract: | This paper proposes a new statistical framework originating from the traditional credit-scoring literature, to evaluate currency crises Early Warning Systems (EWS). Based on an assessment of the predictive power of panel logit and Markov frameworks, the panel logit model is outperforming the Markov switching specitcations. Furthermore, the introduction of forward-looking variables clearly improves the forecasting properties of the EWS. This improvement confirms the adequacy of the second generation crisis models in explaining the occurrence of crises. |
| Keywords: | macroeconomics ; |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:umamet:2010046&r=for |
| By: | Marco Shinobu Matsumura; Ajax Reynaldo Bello Moreira; José Valentim Machado Vicente |
| Abstract: | In this work we compare the interest rate forecasting performance using a broad class of linear models. The models are estimated through a MCMC procedure with data from the US and Brazilian markets. We show that a simple parametric specification has the best predictive power, but it does not outperform the random walk. We also find that macroeconomic variables and no-arbitrage conditions have little effect to improve the out-of-sample fit, while a financial variable (stock index) increases the forecasting accuracy. |
| Date: | 2010–11 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:223&r=for |
| By: | Jayne, T.S.; Rashid, Shahidur |
| Abstract: | Crop production forecasts are widely recognized as an important input into food balance sheets and for anticipating production shortfalls. However, the role of accurate crop production forecasting systems in mitigating food price instability and transitory food insecurity is often under-appreciated. This paper explains how crop production forecasting systems affect price instability and risks, and how they can be improved to stabilize the food system. |
| Keywords: | Africa, Food security, forecasts, production, Agricultural and Food Policy, Community/Rural/Urban Development, Crop Production/Industries, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, International Development, Productivity Analysis, c10, Q11, |
| Date: | 2010–10 |
| URL: | http://d.repec.org/n?u=RePEc:ags:midiwp:97032&r=for |
| By: | Cem Cakmakli (Erasmus University Rotterdam); Dick van Dijk (Erasmus Universiteit Rotterdam) |
| Abstract: | This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only include valuation ratios and interest rate related variables, and possibly individual macro variables, as well as the historical average excess return. The improvements in out-of-sample forecast accuracy are both statistically and economically significant. The factor-augmented predictive regressions have superior market timing ability and volatility timing ability, while a mean-variance investor would be willing to pay an annual performance fee of several hundreds of basis points to switch from the predictions offered by the benchmark models to those of the factor-augmented models. An important reason for the superior performance of the factor-augmented predictive regressions is the stability of their forecast accuracy, whereas the benchmark models suffer from a forecast breakdown during the 1990s. |
| Keywords: | return predictability; model uncertainty; dynamic factor models; variable selection |
| JEL: | C22 C53 G11 G12 |
| Date: | 2010–11–22 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20100115&r=for |
| By: | Pau Rabanal; Jaewoo Lee |
| Abstract: | The driving force of U.S. economic growth is expected to rotate from the fiscal stimulus and inventory rebuilding in 2009 to private demand in 2010, with consumption and particularly investment expected to be important contributors to growth. The strength of U.S. investment will hence be a crucial issue for the U.S. and global recovery. On the basis of several traditional models of investment, we forecast that the U.S. investment in equipment and software will grow by about 10 percent on average over the 2010-12 period. The contribution of investment to real GDP growth will be 0.8 percentage points on average over the same period. |
| Date: | 2010–11–03 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:10/246&r=for |
| By: | Tsyplakov, Alexander |
| Abstract: | In this paper I examine the Okun–Friedman hypothesis of the link between inflation and inflation uncertainty using historical international data on the monthly CPI. An indicator of inflation uncertainty at the two-years-ahead horizon is derived from a time-series model of inflation with time-varying parameters by means of Monte Carlo simulations. This indicator is compared to other uncertainty measures, with the short forecast horizon and based on simpler GARCH-type models. The analysis convincingly demonstrates that both the longer horizon and changing parameters are important for the regularity. The evidence obtained strongly supports the Okun–Friedman hypothesis both in the time dimension for most countries and across countries. |
| Keywords: | inflation uncertainty; inflation forecasting; Okun–Friedman hypothesis; nonlinear state space models; scoring rules |
| JEL: | C29 C32 E31 E52 C22 |
| Date: | 2010–11–22 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:26908&r=for |
| By: | Ines Kahloul; Anouar Ben Mabrouk; Slah-Eddine Hallara |
| Abstract: | We study the possibility of completing data bases of a sample of governance, diversification and value creation variables by providing a well adapted method to reconstruct the missing parts in order to obtain a complete sample to be applied for testing the ownership-structure/diversification relationship. It consists of a dynamic procedure based on wavelets. A comparison with Neural Networks, the most used method, is provided to prove the efficiency of the here-developed one. The empirical tests are conducted on a set of French firms. |
| Date: | 2010–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1011.5020&r=for |