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on Forecasting |
| By: | Knotek, Edward S. (Federal Reserve Bank of Cleveland); Zaman, Saeed (Federal Reserve Bank of Cleveland) |
| Abstract: | Financial data often contain information that is helpful for macroeconomic forecasting, while multistep forecast accuracy also benefits by incorporating good nowcasts of macroeconomic variables. This paper considers the role of nowcasts of financial variables in making conditional forecasts of real and nominal macroeconomic variables using standard quarterly Bayesian vector autoregressions (BVARs). For nowcasting the quarterly value of a variety of financial variables, we document that the average of the available daily data and a daily random walk forecast to fill in the missing days in the quarter typically outperforms other nowcasting approaches. Using real-time data and out-of-sample forecasting exercises, we find that the inclusion of financial variable nowcasts by themselves generally improves forecast accuracy for macroeconomic variables relative to unconditional forecasts, although we document several exceptions in which current-quarter forecast accuracy worsens with the inclusion of the financial nowcasts. Incorporating financial nowcasts and nowcasts of macroeconomic variables generally improves the forecast accuracy for all the macroeconomic indicators of interest, beyond including the nowcasts of the macroeconomic variables alone. Conditional forecasts generated from quarterly BVARs augmented with nowcasts of key financial variables rival the forecast accuracy of mixed-frequency dynamic factor models (MF-DFMs) and mixed-data sampling (MIDAS) models that explicitly link the quarterly data and forecasts to high-frequency financial data. |
| Keywords: | conditional forecasting; nowcasting; vector autoregressions; mixed-frequency models; Bayesian methods; |
| JEL: | C11 C32 C53 G17 |
| Date: | 2017–03–17 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwp:1702&r=for |
| By: | Maria D. Tito |
| Abstract: | This paper explores firm forecasting strategies. Using Italian data, we focus on two aspects of the forecasting process: how firms forecast sales and how accurate their predictions are. We relate both outcomes to current conditions, firm experience, global factors, and other firm characteristics. We find that current conditions tend to explain most of the variability in the sales forecast. While past projection errors tend to account for cross-firm differences in models of expectation formation, they are a key explanatory variable in models of forecast accuracy. Among other controls, firm size, experience, and global conditions--through the effect of price changes that the firm anticipates--shape firm expectations and influence the projection errors. Our findings suggest that models of sales expectations should take firm characteristics and market heterogeneity into account. |
| Keywords: | Exporting ; Forecast Accuracy ; Sales Forecasting |
| JEL: | F14 |
| Date: | 2017–02–28 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2017-27&r=for |
| By: | Peter Exterkate (University of Sydney and CREATES); Oskar Knapik (Aarhus University and CREATES) |
| Abstract: | In a recent review paper, Weron (2014) pinpoints several crucial challenges outstanding in the area of electricity price forecasting. This research attempts to address all of them by i) showing the importance of considering fundamental price drivers in modeling, ii) developing new techniques for probabilistic (i.e. interval or density) forecasting of electricity prices, iii) introducing an universal technique for model comparison. We propose new regime-switching stochastic volatility model with three regimes (negative jump, normal price, positive jump (spike)) where the transition matrix depends on explanatory variables. Bayesian inference is explored in order to obtain predictive densities. The main focus of the paper is on shorttime density forecasting in Nord Pool intraday market. We show that the proposed model outperforms several benchmark models at this task. |
| Keywords: | Electricity prices, density forecasting, Markov switching, stochastic volatility, fundamental price drivers, ordered probit model, Bayesian inference, seasonality, Nord Pool power market, electricity prices forecasting, probabilistic forecasting |
| JEL: | C22 C24 Q41 Q47 |
| Date: | 2601 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2017-03&r=for |
| By: | Fran\c{c}ois Lafond; Aimee Gotway Bailey; Jan David Bakker; Dylan Rebois; Rubina Zadourian; Patrick McSharry; J. Doyne Farmer |
| Abstract: | Experience curves are widely used to predict the cost benefits of increasing the scale of deployment of a technology. But how good are such forecasts? Can one predict their accuracy a priori? In this paper we answer these questions by developing a method to make distributional forecasts for experience curves. We test our method using a dataset with proxies for cost and experience for 46 products and technologies and show that it works reasonably well. The framework that we develop helps clarify why the experience curve method often gives similar results to simply assuming that costs decrease exponentially. To illustrate our method we make a distributional forecast for prices of solar photovoltaic modules. |
| Date: | 2017–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1703.05979&r=for |