|
on Forecasting |
| By: | Markus Heinrich; Magnus Reif |
| Abstract: | This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying parameter VAR with stochastic volatility (MF-TVP-SV-VAR). Overall, the MF-TVP-SV-VAR delivers accurate now- and forecasts and, on average, outperforms its competitors. We assess the models’ accuracy relative to expert forecasts and show that the MF-TVP-SV-VAR delivers better inflation nowcasts in this regard. Using an optimal prediction pool, we moreover demonstrate that the MF-TVP-SV-VAR has gained importance since the Great Recession. |
| Keywords: | time-varying parameters, forecasting, nowcasting, mixed-frequency models, Bayesian methods |
| JEL: | C11 C53 C55 E32 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8054&r=all |
| By: | Ekaterina Abramova; Derek Bunn |
| Abstract: | Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast the German electricity price spreads between different hours of the day, as revealed in the day-ahead auctions. The four specifications of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the location, scale and shape parameters of the densities to respond hourly to such factors as weather and demand forecasts. The best fitting and forecasting specifications for each spread are selected based on the Pinball Loss function, following the closed-form analytical solutions of the cumulative distribution functions. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2002.10566&r=all |
| By: | Anton Gerunov (Faculty of Economics and Business Administration, Sofia University ÒSt. Kliment Ohridski") |
| Abstract: | This article investigates the performance of 136 different classification algorithms for economic problems of binary choice. They are applied to model five different choice situations Ð consumer acceptance during a direct marketing campaign, predicting default on credit card debt, credit scoring, forecasting firm insolvency, and modelling online consumer purchases. Algorithms are trained to generate class predictions of a given binary target variable, which are then used to measure their forecast accuracy using the area under a ROC curve. Results show that algorithms of the Random Forest family consistently outperform alternative methods and may be thus suitable for modelling a wide range of discrete choice situations. |
| Keywords: | Bdiscrete choice, classification, machine learning algorithms, modelling decisions. |
| JEL: | C35 C44 C45 D81 |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:sko:wpaper:bep-2020-02&r=all |
| By: | Andrejs Bessonovs (Bank of Latvia); Olegs Krasnopjorovs (Bank of Latvia) |
| Abstract: | This paper develops a Short-Term Inflation Projections (STIP) model, which captures cointegrated relationships between highly disaggregated consumer prices and their determinants. We document a significant pass-through of domestic labour costs, crude oil and global food commodity prices to consumer prices in Latvia. We also assess the model's forecast accuracy of Latvia's inflation during 2014–2018 and find that the STIP model statistically significantly outperforms a na?ve benchmark model in real time. |
| Keywords: | inflation forecasting, autoregressive distributed lag model, pass-through, oil prices, food commodity prices, labour costs |
| JEL: | C32 C51 C52 C53 E31 |
| Date: | 2020–01–29 |
| URL: | http://d.repec.org/n?u=RePEc:ltv:wpaper:202001&r=all |
| By: | Boriss Siliverstovs (Bank of Latvia); Daniel Wochner (ETH Zurich) |
| Abstract: | This paper re-examines the findings of Stock and Watson (2012b) who assessed the predictive performance of DFMs over AR benchmarks for hundreds of target variables by focusing on possible business cycle performance asymmetries in the spirit of Chauvet and Potter (2013) and Siliverstovs (2017a; 2017b; 2020). Our forecasting experiment is based on a novel big macroeconomic dataset (FRED-QD) comprising over 200 quarterly indicators for almost 60 years (1960–2018; see, e.g. McCracken and Ng (2019b)). Our results are consistent with this nascent state-dependent evaluation literature and generalize their relevance to a large number of indicators. We document systematic model performance differences across business cycles (longitudinal) as well as variable groups (cross-sectional). While the absolute size of prediction errors tend to be larger in busts than in booms for both DFMs and ARs, DFMs relative improvement over ARs is typically large and statistically significant during recessions but not during expansions (see, e.g. Chauvet and Potter (2013)). Our findings further suggest that the widespread practice of relying on full sample forecast evaluation metrics may not be ideal, i.e. for at least two thirds of all 216 macroeconomic indicators full sample rRMSFEs systematically over-estimate performance in expansionary subsamples and under-estimate it in recessionary subsamples (see, e.g. Siliverstovs (2017a; 2020)). These findings are robust to several alternative specifications and have high practical relevance for both consumers and producers of model-based economic forecasts. |
| Keywords: | forecast evaluation, dynamic factor models, business cycle asymmetries, big macroeconomic datasets, US |
| JEL: | C32 C45 C52 E17 |
| Date: | 2020–02–11 |
| URL: | http://d.repec.org/n?u=RePEc:ltv:wpaper:202002&r=all |
| By: | Krüger, Jens; Ruths Sion, Sebastian |
| Abstract: | In this paper we document the results of a forecast evaluation exercise for the real world price of crude oil using VAR models estimated by sparse (regularization) estimators. These methods have the property to constrain single parameters to zero. We find that estimating VARs with three core variables (real price of oil, index of global real economic activity, change in global crude oil production) by the sparse methods is associated with substantial reductions of forecast errors. The transformation of the variables (taking logs or differences) is also crucial. Extending the VARs by further variables is not associated with additonal gains in forecast performance as is the application of impulse indicator saturation before the estimation. |
| Keywords: | oil price prediction,vector autoregression,regularization |
| JEL: | C32 Q47 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:darddp:237&r=all |