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on Forecasting |
| By: | Francesca Di Iorio (University of Naples Federico II); Stefano Fachin ("Sapienza" University of Rome) |
| Abstract: | Forecasting mortality rates and life expectancy is an issue of critical importance made arguably more dicult by the e ects of current Covid-19 pandemic. In this paper we compare the performances of a simple random walk model (benchmark), three variants of the standard Lee-Carter model (Lee-Carter, Lee-Miller, Booth-Maindonald-Smith), the Hyndman-Ullah functional data analysys model, and a general factor model. We use both symmetric and asymmetric loss functions, as the latter are arguably more suitable to capture preferences of forecast users such as insurance companies and pension and health system planners. In a counterfactual study, designed exploiting the particular features of Italian data, we reproduce the likely impact of Covid-19 on forecasts using 2020 as a jump-off year. To put the results in perspective, we also carry out out a general assessment on 1950-2016 data for three countries with very diverse demographic profiles, France, Italy and USA. While the results with these latter datasets suggest that in normal conditions the Lee-Miller and Hyndman-Ullah models are somehow superior,from the counterfactual study the best option appears to be the Booth-Maindonald- Smith model. |
| Keywords: | Mortality forecasting, life expectancy forecasting, Lee-Carter, factor model, Covid-19. |
| JEL: | C12 C33 C55 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:sas:wpaper:20201&r=all |
| By: | Kitova, Olga; Dyakonova, Ludmila; Savinova, Victoria |
| Abstract: | The article describes a system of hybrid models ‘SGM Horizon’ as intellectual forecasting information system. The system of forecasting models includes a set of regression models and an expandable set of intelligent models, including artificial neural networks, decision trees, etc. Regression models include systems of regression equations that describe the behavior of forecast indicators of the development of the Russian economy in the system of national accounts. The functioning of the system of equations is determined by scenario conditions set by expert. For those indicators whose forecasts do not meet the requirements of quality and accuracy, intelligent models based on machine learning are used. Using the ‘SHM Horizon’ tools, predictive calculations were performed for a system of 30 indicators of the social sphere of the City of Moscow using hybrid models, and for8 indicators a significant increase in the quality and accuracy of the forecast was achieved with artificial neural network models. The process of models building requires considerable time, in this regard, the authors see the further development of the system in the application of the multi-criteria ranking method. |
| Keywords: | Regional economics, Forecasting, Socio-economic indicators, Hybrid models, Machine learning, Neural networks, Decision trees |
| JEL: | C40 C45 |
| Date: | 2020–07–24 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:104234&r=all |
| By: | Christa D. Court (Food and Resource Economics Department, University of Florida); Randall Jackson (Geology and Geography Department and Regional Research Institute, West Virginia University); Amanda J. Harker Steele (KeyLogic Systems LLC-NETL); Justin Adder (U.S. Department of Energy, National Energy Technology Laboratory); Gavin Pickenpaugh (U.S. Department of Energy, National Energy Technology Laboratory); Charles Zelek (U.S. Department of Energy, Office of Fossil Energy) |
| Abstract: | To comprehensively model the macroeconomic impacts that result from changes in long-term energyeconomy forecasts, the United States Department of Energy’s National Energy Technology Laboratory (NETL) partnered with West Virginia University’s (WVU) Regional Research Institute to develop the NETL/WVU econometric input-output (ECIO) model. The NETL/WVU ECIO model is an impacts forecasting model that functions as an extension of the U.S. energy-economic models available from the United States (U.S.) Energy Information Administration’s National Energy Modeling System (NEMS) and the U.S. Environmental Protection Agency’s Market Allocation (MARKAL) model. The ECIO model integrates a macroeconomic econometric forecasting model and an input-output accounting framework along derived forecast scenarios detailing a baseline of the U.S. energy-economy and an alternative forecast on how power generation resources can meet future levels of energy demand to generate estimates of the impacts to gross domestic product, employment, and labor income. This manuscript provides an overview of the model design, assumptions, and standard outputs. |
| Keywords: | Energy-Economy Forecasting, National Energy Modeling System, Input-Output Model, Econometric Model |
| JEL: | Q43 E17 O33 |
| Date: | 2020–10–07 |
| URL: | http://d.repec.org/n?u=RePEc:rri:wpaper:2020wp05&r=all |
| By: | Naftali Cohen; Srijan Sood; Zhen Zeng; Tucker Balch; Manuela Veloso |
| Abstract: | Time series forecasting is essential for agents to make decisions in many domains. Existing models rely on classical statistical methods to predict future values based on previously observed numerical information. Yet, practitioners often rely on visualizations such as charts and plots to reason about their predictions. Inspired by the end-users, we re-imagine the topic by creating a framework to produce visual forecasts, similar to the way humans intuitively do. In this work, we take a novel approach by leveraging advances in deep learning to extend the field of time series forecasting to a visual setting. We do this by transforming the numerical analysis problem into the computer vision domain. Using visualizations of time series data as input, we train a convolutional autoencoder to produce corresponding visual forecasts. We examine various synthetic and real datasets with diverse degrees of complexity. Our experiments show that visual forecasting is effective for cyclic data but somewhat less for irregular data such as stock price. Importantly, we find the proposed visual forecasting method to outperform numerical baselines. We attribute the success of the visual forecasting approach to the fact that we convert the continuous numerical regression problem into a discrete domain with quantization of the continuous target signal into pixel space. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.09052&r=all |
| By: | Gabor Katay (European Commission – JRC); Lisa Kerdelhué (Banque de France, Aix-Marseille Université); Matthieu Lequien (Institut National de la Statistique et des Études Économiques (INSEE), Paris School of Economics) |
| Abstract: | The paper assesses the impact of adding information on financial cycles on the output gap estimates for eight advanced economies using two unobserved components models: a reduced form extended Hodrick-Prescott filter, and a standard semi-structural unobserved components model. To complement these models, a semi-structural vector autoregression model is proposed in which only supply shocks are identified. The accuracy of the output gap estimates is assessed based on their performance in predicting recessions. The models with financial variables generally produce more accurate output gap estimates at the expense of increased real-time volatility. While the extended Hodrick-Prescott filter is particularly appealing for its real-time stability, it lags behind the two semi-structural models in terms of forecasting performance. The vector autoregression model augmented with financial variables features the best in-sample forecasting performance, and it has similar real-time prediction capabilities to the semi-structural unobserved components model. Overall, financial cycles appear to be relevant in Japan, Spain, the UK, and – to a lesser extent – in the US and in France, while they are relatively muted in Canada, Germany, and Italy. |
| Keywords: | unobserved components model, semi-structural VAR, output gap, financial cycle, sustainable growth, credit, house prices, advanced economies |
| JEL: | C32 E32 E44 G01 O11 O16 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:jrs:wpaper:202011&r=all |