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on Forecasting |
| By: | Magnus Reif |
| Abstract: | Can information on macroeconomic uncertainty improve the forecast accuracy for key macroeconomic time series for the US? Since previous studies have demonstrated that the link between the real economy and uncertainty is subject to nonlinearities, I assess the predictive power of macroeconomic uncertainty in both linear and nonlinear Bayesian VARs. For the latter I use a threshold VAR that allows for regimedependent dynamics conditional on the level of the uncertainty measure. I find that the predictive power of macroeconomic uncertainty in the linear VAR is negligible. In contrast, using information on macroeconomic uncertainty in a threshold VAR can significantly improve the accuracy of short-term point and density forecasts, especially in the presence of high uncertainty. |
| Keywords: | Forecasting, BVAR, nonlinearity, threshold VAR, uncertainty |
| JEL: | C11 C53 E32 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ifowps:_265&r=for |
| By: | Camille Cornand (Univ Lyon, CNRS, GATE UMR 5824, F69130 Ecully, France); Paul Hubert (Sciences Po-OFCE, 10 place de Catalogne, 75014 Paris, France) |
| Abstract: | Establishing the external validity of laboratory experiments in terms of inflation forecasts is crucial for policy initiatives to be valid outside the laboratory. Our contribution is to document whether different measures of inflation expectations based on various categories of agents (participants to experiments, households, industry forecasters, professional forecasters, financial market participants and central bankers) share common patterns by analyzing: the forecasting performances of these different categories of data; the information rigidities to which they are subject; the determination of expectations. Overall, the different categories of forecasts exhibit common features: forecast errors are comparably large and autocorrelated, forecast errors and forecast revisions are predictable from past information, which suggests the presence of information frictions. Finally, the standard lagged inflation determinant of inflation expectations is robust to the data sets. There is nevertheless some heterogeneity among the six different sets. If experimental forecasts are relatively comparable to survey and financial market data, central bank forecasts seem to be superior. |
| Keywords: | inflation expectations, experimental forecasts, survey forecasts, market-based forecasts, central bank forecasts |
| JEL: | E3 E5 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:gat:wpaper:1821&r=for |
| By: | Anthony Garratt; Shaun P. Vahey; Ynuyi Zhang |
| Abstract: | Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real-time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea based measure and update the evaluation sample to 2017:12. We model the oil price futures curve using a factor-based Nelson-Siegel specification estimated in real time to fill in missing values for oil price futures in the raw data. We find that the combined forecasts for Brent are as effective as for other oil price measures. The extended sample using the oil price measures adopted by Baumeister and Kilian (2015) yields similar results to those reported in their paper. And the futures-based model improves forecast accuracy at longer horizons. The real-time data set is available for download from https://www.niesr.ac.uk/real-time-foreca st-combinations-oil-price |
| Keywords: | Real oil price forecasting, Brent crude oil, Forecast combination |
| JEL: | C01 C32 C53 |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:niesrd:494&r=for |
| By: | Muhammad Nadim Hanif (State Bank of Pakistan); Khurrum S. Mughal (State Bank of Pakistan); Javed Iqbal (State Bank of Pakistan) |
| Abstract: | Inflation forecasting is an essential activity at central banks to formulate forward looking monetary policy stance. Like in other fields, machine learning is finding its way to forecasting; inflation forecasting is not any exception. In machine learning, most popular tool for forecasting is artificial neural network (ANN). Researchers have used different performance measures (including RMSE) to optimize set of characteristics - architecture, training algorithm and activation function - of an ANN model. However, any chosen ‘optimal’ set may not remain reliable on realization of new data. We suggest use of ‘mode’ or most appearing set from a simulation based distribution of optimum ‘set of characteristics of ANN model’; selected from a large number of different sets. Here again, we may have a different trained network in case we re-run this ‘modal’ optimal set since initial weights in training process are assigned randomly. To overcome this issue, we suggest use of ‘thickness’ to produce stable and reliable forecasts using modal optimal set. Using January 1958 to December 2017 year on year (YoY) inflation data of Pakistan, we found that our YoY inflation forecasts (based on aforementioned multistage forecasting scheme) outperform those from a number of inflation forecasting models of Pakistan economy. |
| Keywords: | Artificial Neural Networks, Inflation Forecasting |
| JEL: | C45 E31 E37 |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:sbp:wpaper:99&r=for |
| By: | Lang, Jan Hannes; Peltonen, Tuomas A.; Sarlin, Peter |
| Abstract: | This paper proposes a framework for deriving early-warning models with optimal out-of-sample forecasting properties and applies it to predicting distress in European banks. The main contributions of the paper are threefold. First, the paper introduces a conceptual framework to guide the process of building early-warning models, which highlights and structures the numerous complex choices that the modeler needs to make. Second, the paper proposes a flexible modeling solution to the conceptual framework that supports model selection in real-time. Specifically, our proposed solution is to combine the loss function approach to evaluate early-warning models with regularized logistic regression and cross-validation to find a model specification with optimal real-time out-of-sample forecasting properties. Third, the paper illustrates how the modeling framework can be used in analysis supporting both microand macro-prudential policy by applying it to a large dataset of EU banks and showing some examples of early-warning model visualizations. JEL Classification: G01, G17, G21, G33, C52, C54 |
| Keywords: | bank distress, early-warning models, financial crises, micro- and macro-prudential analysis, regularization |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20182182&r=for |