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on Forecasting |
| By: | Gloria Gonzalez-Rivera (Department of Economics, University of California Riverside); Yingying Sun |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:201606&r=for |
| By: | Jean-Thomas Bernard (Department of Economics, University of Ottawa); Lynda Khalaf (Carleton University); Maral Kichian (Graduate School of Public and International Affairs, University of Ottawa); Clement Yelou (Laval University) |
| Abstract: | Expert outlooks on the future path of oil prices are often relied on by industry participants and policymaking bodies for their forecasting needs. Yet little attention has been paid to the extent to which these are accurate. Using the regular publications by the Energy Information Administration (EIA), we examine the accuracy of annual recursive oil price forecasts generated by the National Energy Modeling System model of the Agency for forecast horizons of up to 15 years. Our results reveal that the EIA model is quite successful at beating the benchmark random walk model, but only at either end of the forecast horizons. We also show that, for the longer horizons, simple econometric forecasting models often produce similar if not better accuracy than the EIA model. Among these, time-varying specifications generally also exhibit stability in their forecast performance. Finally, while combining forecasts does not change the overall patterns, some additional accuracy gains are obtained at intermediate horizons, and in some cases forecast performance stability is also achieved. |
| Keywords: | Oil price, expert outlooks, long run forecasting, forecast combinations |
| JEL: | Q47 C20 |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:ott:wpaper:1510e&r=for |
| By: | Christina Christou (Department of Banking & Financial Management, University of Pireaus, Greece); Rangan Gupta (Department of Economics, University of Pretoria) |
| Abstract: | This paper investigates whether the news-based measure of economic policy uncertainty (EPU) could help in forecasting the equity premium (excess returns) in ten (Canada, France, Germany, Italy, Japan, The Netherlands, South Korea, Spain, United Kingdom (UK), and United States (US)) Organization for Economic Co-operation and Development (OECD) countries. We analyze the monthly out-of-sample period of 2007:01-2014:12, given an in-sample period of 2003:03-2006:12, using panel data-based predictive frameworks, which controls for heterogeneity, cross-sectional dependence, persistence and endogeneity. Our results show that while, time series based predictive regression models fail to beat the benchmark of historical average, the panel data models consistently beat the benchmark in a statistically significant fashion. In general, our results highlight the importance of pooling information when trying to forecast excess stock returns based on a news-based measure of domestic EPU, as well as that of the US. Length: 22 pages |
| Keywords: | Equity Premium, Economic Policy Uncertainty, OECD Countries, Panel Predictive Regressions |
| JEL: | C33 C53 G1 |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201622&r=for |
| By: | Till Weigt; Bernd Wilfling |
| Abstract: | This paper formally establishes a new forecast combination approach, which is based on VAR modeling of the forecast errors resulting from alternative forecast models. We apply our approach to volatility forecasting by combining several structural time series models with implied volatility. Using a multi-currency data set, we conduct in-sample and out-of-sample forecasting analyses in order (a) to demonstrate the statistical significance of our approach, and (b) to assess its forecasting superiority over alternative forecasting models and combinations. |
| Keywords: | Forecast combination, volatility forecasting, realized volatility, implied volatility, exchange rates |
| JEL: | C53 G17 |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:cqe:wpaper:4616&r=for |
| By: | Lakkakula, Prithviraj |
| Abstract: | This paper focuses on forecasting US high fructose corn syrup (HFCS) prices using a seasonal autoregressive integrated moving average model. We use both monthly and quarterly data to forecast HFCS prices for the 1994–2015 period. We perform the Augmented Dickey–Fuller test for ensuring that the HFCS prices are stationary. We use mean absolute error, in–sample root mean square error, and out–of–sample root mean square error for evaluating the predictive accuracy of the models. Based on the out–of–sample performance, we found that the quarterly model performed well in predicting HFCS prices compared to monthly model. The results will help make better decision concerning the operation of corn-wet milling plant and HFCS production. |
| Keywords: | HFCS, FORECASTING, SEASONAL ARIMA, Agribusiness, |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:ags:saea16:230133&r=for |
| By: | Jo, Jisung; Lusk, Jayson L. |
| Abstract: | Predicting future food prices is important not only for projecting and adjusting the cost of government programs but also for business and household planning. This study asks whether unconventional consumer-oriented measures might be useful in the predicting Bureau of Labor Statistics (BLS) Food and Beverages Consumer Price Index (CPI). We investigate the ability of Internet search-based index related to food prices (the Google trends index) and survey-based sentiment indices (the index of consumer sentiment) to predict changes in food-related BLS prices from January 2004 to July 2015. We consider several forecasting models and find that a vector autoregression model (VAR) results in the lowest root mean square error and mean absolute percentage error. We also ask whether our model can out predict USDA Economic Research Service food-related CPI forecasts. Rolling window comparison and encompassing tests are conducted, and we find that our new model including consumer-oriented measures outperforms the USDA model in terms of predictive accuracy. |
| Keywords: | consumer sentiment, Internet search, food prices, forecasting, Agricultural and Food Policy, Demand and Price Analysis, C53, Q11, |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:ags:saea16:230028&r=for |
| By: | Fuchun Li; Hongyu Xiao |
| Abstract: | We propose an early warning model for predicting the likelihood of a financial stress event for a given future time, and examine whether credit plays an important role in the model as a non-linear propagator of shocks. This propagation takes the form of a threshold regression in which a regime change occurs if credit conditions cross a critical threshold. The in-sample and out-of-sample forecasting performances are encouraging. In particular, the out-of-sample forecasting results suggest that the model based on the credit-regime-switching approach outperforms the benchmark models based on a linear regression and signal extraction approach across all forecasting horizons and all criteria considered. |
| Keywords: | Econometric and statistical methods, Financial stability |
| JEL: | C12 C14 G01 G17 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:16-21&r=for |
| By: | Iversen, Jens (Monetary Policy Department, Central Bank of Sweden); Laséen, Stefan (IMF); Lundvall, Henrik (National Institute of Economic Research (NIER)); Söderström, Ulf (Monetary Policy Department, Central Bank of Sweden) |
| Abstract: | We evaluate forecasts made in real time to support monetary policy decisions at Sveriges Riksbank (the central bank of Sweden) from 2007 to 2013. We compare forecasts made with a DSGE model and a BVAR model with judgemental forecasts published by the Riksbank, and we evaluate the usefulness of conditioning information for the model-based forecasts. We also study the perceived usefulness of model forecasts for central bank policymakers when producing the judgemental forecasts. |
| Keywords: | Real-time forecasting; Forecast evaluation; Monetary policy; Inflation targeting |
| JEL: | E37 E52 |
| Date: | 2016–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:rbnkwp:0318&r=for |
| By: | Dimitrios P. Louzis (Bank of Greece) |
| Abstract: | This article proposes methods for estimating a Bayesian vector autoregression (VAR) model with an informative steady state prior which also accounts for possible structural changes in the long-term trend of the macroeconomic variables. I show that, overall, the proposed time-varying steady state VAR model can lead to superior point and density macroeconomic forecasting compared to constant steady state VAR specifications. |
| Keywords: | Steady states; time-varying parameters; macroeconomic forecasting |
| JEL: | C32 |
| Date: | 2016–03 |
| URL: | http://d.repec.org/n?u=RePEc:bog:wpaper:204&r=for |
| By: | Kruttli, Mathias S. |
| Abstract: | This paper compares consumption-based asset pricing models on the basis of whether they can improve the forecast accuracy of investors who try to predict the equity premium out-of-sample with valuation ratios. Model-based priors are derived from three prominent consumption-based asset pricing models: Habit Formation, Long Run Risk, and Prospect Theory. A simple Bayesian framework is proposed through which the investors impose these model-based priors on the parameters of their predictive models. An investor whose prior beliefs are rooted in the Long Run Risk model achieves more accurate forecasts overall. The greatest difference in performance occurs during the bull market of the late 1990s. During this period, the weak predictability of the equity premium implied by the Long Run Risk model helps the investor to not prematurely anticipate falling stock prices. |
| Keywords: | Bayesian econometrics ; consumption-based asset pricing ; return predictability |
| JEL: | G11 G12 G17 |
| Date: | 2016–03–29 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2016-27&r=for |
| By: | Amendola, Alessandra; Candila, Vincenzo; Scognamillo, Antonio |
| Abstract: | Modeling crude oil volatility is of substantial interest for both energy researchers and policy makers. This paper aims to investigate the impact of the U.S. monetary policy on crude oil future price (COFP) volatility. By means of the recently proposed generalized autoregressive conditional hetroskedasticity mixed data sampling (GARCH-MIDAS) model, a proxy of the U.S. monetary policy is included into the COFP volatility equation, alongside with other macroeconomic determinants. Strong evidence that an expansionary monetary policy is associated with an increase of the COFP volatility is found. In particular, an expansionary (restrictive) variation in monetary policy anticipates a positive (negative) variation in COFP volatility. Furthermore, an out of sample forecasting procedure shows that the estimated GARCH-MIDAS model has a superior predictive ability with respect to that of the GARCH(1,1), when the U.S. monetary policy exhibits severe changes in the run-up period. |
| Keywords: | volatility, garch-midas, firecasting, crude oil, Agricultural and Food Policy, c22, c58, e30, q43, |
| Date: | 2015–06 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aiea15:207860&r=for |