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on Forecasting |
| By: | Cook, Thomas R. (Federal Reserve Bank of Kansas City); Smalter Hall, Aaron (Federal Reserve Bank of Kansas City) |
| Abstract: | Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models {{p}} that exhibit model dependence and have high data demands. {{p}} We explore deep neural networks as an {{p}} opportunity to improve upon forecast accuracy with limited data and while remaining agnostic as to {{p}} functional form. We focus on predicting civilian unemployment using models based on four different neural network architectures. Each of these models outperforms bench- mark models at short time horizons. One model, based on an Encoder Decoder architecture outperforms benchmark models at every forecast horizon (up to four quarters). |
| Keywords: | Neural networks; Forecasting; Macroeconomic indicators |
| JEL: | C14 C45 C53 |
| Date: | 2017–09–29 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedkrw:rwp17-11&r=for |
| By: | Charles Engel; Dohyeon Lee; Chang Liu; Chenxin Liu; Steve Pak Yeung Wu |
| Abstract: | Recent research has found that the Taylor-rule fundamentals have power to forecast changes in U.S. dollar exchange rates out of sample. Our work casts some doubt on that claim. However, we find strong evidence of a related in-sample anomaly. When we include U.S. inflation in the well-known uncovered interest parity regression of the change in the exchange rate on the interest-rate differential, we find that the inflation variable is highly significant and the interest-rate differential is not. Specifically, high U.S. inflation in one month forecasts dollar appreciation in the subsequent month. We introduce a model in which a Taylor rule determines monetary policy, but in which not only monetary shocks but also liquidity shocks drive nominal interest rates. This model can potentially account for the empirical findings. |
| JEL: | F3 F31 F41 |
| Date: | 2017–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24059&r=for |
| By: | Angelica Gianfreda; Francesco Ravazzolo; Luca Rossini |
| Abstract: | This paper compares alternative univariate versus multivariate models, probabilistic versus Bayesian autoregressive and vector autoregressive specifications for hourly day-ahead electricity prices, with and without renewable energy sources. The accuracy of point and density forecasts are inspected in four main European markets (Germany, Denmark, Italy and Spain) characterized by different levels of renewable energy power generation. Our results show that the Bayesian VAR specifications with exogenous variables dominate other multivariate and univariate specifications, in terms of both point and density forecasting. |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1801.01093&r=for |
| By: | Piyabha Kongsamut; Christian Mumssen; Anne-Charlotte Paret; Thierry Tressel |
| Abstract: | How can information on financial conditions be used to better understand macroeconomic developments and improve macroeconomic projections? We investigate this question for France by constructing country-specific financial conditions indices (FCIs) that are tailored to movements in GDP, investment, private consumption and exports respectively. We rely on a VAR approach to estimate the weights of the financial components of each FCI, including equity market returns (which turn out having a relatively strong weight across all FCIs), private sector risk premiums, long-term interest rates, and banks’ credit standards. We find that the tailored FCIs are useful as leading indicators of GDP, investment, and exports, and as a contemporaneous indicator of private consumption. Credit volumes turn out to be lagging indicators of growth. The indices inform us on macro-financial linkages in France and are used to improve the accuracy of quarterly forecasting models and high-frequency “nowcast” models. We show that FCI-augmented models could have significantly improved forecasts during and after the global financial crisis. |
| Date: | 2017–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:17/269&r=for |
| By: | Monica Jain; Christopher S. Sutherland |
| Abstract: | We construct a 23-country panel data set to consider the effect of central bank projections and forward guidance on private-sector forecast disagreement. We find that central bank projections and forward guidance matter mainly for private-sector forecast disagreement surrounding upcoming policy rate decisions and matter less for private-sector macroeconomic forecasts. Further, neither central banks’ provision of policy rate path projections nor their choice of policy rate assumption used in their macroeconomic projections appear to matter much for private-sector forecast disagreement. |
| Keywords: | Central bank research, Inflation targets, Monetary Policy, Monetary policy communications, Transmission of monetary policy |
| JEL: | D83 E37 E52 E58 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:18-2&r=for |
| By: | Julien Prat (CREST; CNRS; Université Paris- Saclay; CESifo; IZA); Walter Benjamin (CREST; Université Paris-Saclay) |
| Abstract: | We propose a model which uses the Bitcoin/US dollar exchange rate to predict the computing power of the Bitcoin network. We show that free entry places an upper-bound on mining revenues and we devise a structural framework to measure its value. Calibrating the model’s parameters allows us to accurately forecast the evolution of the network computing power over time. We establish the accuracy of the model through out-of-sample tests and investigation of the entry rule. |
| Keywords: | Bitcoin; Blockchain; Miners; Industry Dynamics |
| URL: | http://d.repec.org/n?u=RePEc:crs:wpaper:2017-15&r=for |