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on Forecasting |
| By: | Dong Hwan Oh; Andrew J. Patton |
| Abstract: | Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis- speci ed model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspeci cation of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we nd signi cant fore- cast improvements from applying the proposed method across distinct empirical analyses including volatility forecasting, risk management, and yield curve forecasting. |
| Keywords: | Model misspecification; Local maximum likelihood; Volatility forecasting; Value-at-risk and expected shortfall forecasting; Yield curve forecasting |
| JEL: | C53 C51 C58 C14 |
| Date: | 2021–11–05 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2021-71&r= |
| By: | Hu, Junjie; López Cabrera, Brenda; Melzer, Awdesch |
| Abstract: | Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. Modern and efficient machine learning methods are recalled nowadays to manage complicated structural big datasets, which are characterized by having a nonlinear temporal dependence structure. We propose different statistical nonlinear models to manage these challenges of hard type datasets and forecast 15-min frequency electricity load up to 2-days ahead. We show that the Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) models applied to the production line of a chemical production facility outperform several other predictive models in terms of out-of-sample forecasting accuracy by the Diebold-Mariano (DM) test with several metrics. The predictive information is fundamental for the risk and production management of electricity consumers. |
| Keywords: | Short Term Load Forecast,Deep Neural Network,Hard Structure Load Process |
| JEL: | C51 C52 C53 Q31 Q41 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021020&r= |
| By: | Oya Celasun; Mr. Allan Timmermann; Jungjin Lee; Mr. Mico Mrkaic |
| Abstract: | This paper examines the performance of World Economic Outlook (WEO) growth forecasts for 2004-17. Short-term real GDP growth forecasts over that period exhibit little bias, and their accuracy is broadly similar to those of Consensus Economics forecasts. By contrast, two- to five-year ahead WEO growth forecasts in 2004-17 tend to be upward biased, and in up to half of countries less accurate than a naïve forecast given by the average growth rate in the recent past. The analysis suggests that a more efficient use of available information on internal and external factors—such as the estimated output gap, projected terms of trade, and the growth forecasts of major trading partners—can improve the accuracy of some economies’ growth forecasts. |
| Keywords: | Forecasting, forecasting bias and efficiency; WEO growth forecast; forecasting bias; Consensus Economics forecast; World Economic Outlook growth; forecast error; Emerging and frontier financial markets; Terms of trade; GDP forecasting; Output gap; Global financial crisis of 2008-2009; Caribbean; Middle East; North Africa; Global; Europe |
| Date: | 2021–08–06 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/216&r= |
| By: | Graziano Moramarco |
| Abstract: | Using a large quarterly macroeconomic dataset over the period 1960Q1-2017Q4, this paper documents the usefulness of selected financial ratios from the housing market and firms' aggregate balance sheets for predicting GDP in the United States over multi-year horizons. A house price-to-rent ratio adjusted for the business cycle and the liabilities-to-income ratio of the nonfinancial noncorporate business sector provide the best in-sample fit and out-of-sample forecasts of cumulative GDP growth over horizons of 1-5 years, outperforming all other predictors as well as popular high-dimensional forecasting models and forecast combinations. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2111.00822&r= |
| By: | Tae-Hwy Lee (Department of Economics, University of California Riverside); He Wang (University of International Business and Economics, Beijing); Zhou Xi (Citigroup); Ru Zhang (JP Morgan Chase) |
| Abstract: | We consider a multiplicative decomposition of the financial returns to improve the density forecasts of financial returns. The multiplicative decomposition is based on the identity that financial return is the product of its absolute value and its sign. Advantages of modeling the two components are discussed. To reduce the effect of the estimation error due to the multiplicative decomposition in estimation of the density forecast model, we impose a moment constraint that the conditional mean forecast is set to match with the sample mean. Imposing such a moment constraint operates a shrinkage and tilts the density forecast of the decomposition model to produce the improved maximum entropy density forecast. An empirical application to forecasting density of the daily stock returns demonstrates the benefits of using the decomposition and imposing the moment constraint to obtain the improved density forecast. We evaluate the density forecast by comparing the logarithmic score, the quantile score, and the continuous ranked probability score. We contribute to the literature on the density forecast and the decomposition models by showing that the density forecast of the decomposition model can be improved by imposing a sensible constraint in the maximum entropy framework. |
| Keywords: | Decomposition, Copula, Moment constraint, Maximum entropy, Density forecast, Logarithmic score, Quantile score, VaR, Continuous ranked probability score. |
| JEL: | C1 C3 C5 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:ucr:wpaper:202115&r= |
| By: | Laura Liu; Hyungsik Roger Moon; Frank Schorfheide |
| Abstract: | We use a dynamic panel Tobit model with heteroskedasticity to generate forecasts for a large cross-section of short time series of censored observations. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of heterogeneous coefficients and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. In addition to density forecasts, we construct set forecasts that explicitly target the average coverage probability for the cross-section. We present a novel application in which we forecast bank-level loan charge-off rates for small banks. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.14117&r= |