nep-for New Economics Papers
on Forecasting
Issue of 2022‒08‒15
nine papers chosen by
Rob J Hyndman
Monash University

  1. Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models By Francis X. Diebold; Maximilian Goebel; Philippe Goulet Coulombe
  2. Covid-19 outbreak and beyond: A retrospect on the information content of registered short-time workers for GDP now- and forecasting. By Sylvia Kaufmann
  3. Nowcasting the Portuguese GDP with Monthly Data By Jo\~ao B. Assun\c{c}\~ao; Pedro Afonso Fernandes
  4. Dynamic Early Warning and Action Model By Mueller, H.; Rauh, C.; Ruggieri, A.
  5. Evaluating a new earnings indicator. Can we improve the timeliness of existing statistics on earnings by using salary information from online job adverts? By Jyldyz Djumalieva; Stef Garasto; Cath Sleeman
  6. Dynamic Co-Quantile Regression By Timo Dimitriadis; Yannick Hoga
  7. Predicting Economic Welfare with Images on Wealth By Jeonggil Song
  8. On the impact of serial dependence on penalized regression methods By Simone Tonini; Francesca Chiaromonte; Alessandro Giovannelli
  9. Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Road link-level By S. Roderick Zhang; Bilal Farooq

  1. By: Francis X. Diebold; Maximilian Goebel; Philippe Goulet Coulombe
    Abstract: We use "glide charts" (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice. We first use them to evaluate the simple feature-engineered linear regression (FELR) forecasts of Diebold and Goebel (2021), and to compare FELR forecasts to naive pure-trend benchmark forecasts. Then we introduce a much more sophisticated feature-engineered machine learning (FEML) model, and we use glide charts to evaluate FEML forecasts and compare them to a FELR benchmark. Our substantive results include the frequent appearance of predictability thresholds, which differ across months, meaning that accuracy initially fails to improve as the target date is approached but then increases progressively once a threshold lead time is crossed. Also, we find that FEML can improve appreciably over FELR when forecasting "turning point" months in the annual cycle at horizons of one to three months ahead.
    Date: 2022–06
  2. By: Sylvia Kaufmann (Study Center Gerzensee)
    Abstract: We document whether a simple, univariate model for quarterly GDP growth is able to deliver forecasts of yearly GDP growth in a crisis period like the Covid- 19 pandemic, which may serve cross-checking forecasts obtained from elaborate and expert models used by forecasting institutions. We include shocks to the log number of short-time workers as timely available current-quarter indicator. Yearly GDP growth forecasts serve cross-checking, in particular at the outbreak of the pandemic.
    Date: 2022–07
  3. By: Jo\~ao B. Assun\c{c}\~ao; Pedro Afonso Fernandes
    Abstract: In this article, we present a method to forecast the Portuguese gross domestic product (GDP) in each current quarter (nowcasting). It combines bridge equations of the real GDP on readily available monthly data like the Economic Sentiment Indicator (ESI), industrial production index, cement sales or exports and imports, with forecasts for the jagged missing values computed with the well-known Hodrick and Prescott (HP) filter. As shown, this simple multivariate approach can perform as well as a Targeted Diffusion Index (TDI) model and slightly better than the univariate Theta method in terms of out-of-sample mean errors.
    Date: 2022–06
  4. By: Mueller, H.; Rauh, C.; Ruggieri, A.
    Abstract: This document presents the outcome of two modules developed for the UK Foreign, Commonwealth Development Office (FCDO): 1) a forecast model which uses machine learning and text downloads to predict outbreaks and intensity of internal armed conflict. 2) A decision making module that embeds these forecasts into a model of preventing armed conflict damages. The outcome is a quantitative benchmark which should provide a testing ground for internal FCDO debates on both strategic levels (i.e. the process of deciding on country priorities) and operational levels (i.e. identifying critical periods by the country experts). Our method allows the FCDO to simulate policy interventions and changes in its strategic focus. We show, for example, that the FCDO should remain engaged in recently stabilized armed conflicts and re-think its development focus in countries with the highest risks. The total expected economic benefit of reinforced preventive efforts, as defined in this report, would bring monthly savings in expected costs of 26 billion USD with a monthly gain to the UK of 630 million USD.
    Keywords: dynamic optimisation, forecasting, internal armed conflict, prevention
    Date: 2022–06–14
  5. By: Jyldyz Djumalieva; Stef Garasto; Cath Sleeman
    Abstract: This paper examines how the salary information from online job adverts might be used to improve the timeliness of official statistics on earnings. The unique dataset underpinning the analysis contains over 51 million adverts for UK positions, collected between January 2012 and September 2018. The data was sourced from Burning Glass Technologies, a leading labour market intelligence company. We trial a mixture of forecasting approaches, including traditional econometric models and the relatively newer recurrent neural networks. For 2 out of 13 industries and for 5 out of 6 occupation groups, salaries from online job adverts are shown to improve the accuracy of earnings forecasts over and above official data on its own. More broadly, this paper provides a detailed methodology for evaluating a novel data source, such as salaries from job adverts, to inform an official statistical series, such as earnings.
    Keywords: arima models, earnings, forecasting, neural networks, online job adverts
    JEL: C18 C45 C53 J30
    Date: 2020–12
  6. By: Timo Dimitriadis; Yannick Hoga
    Abstract: The popular systemic risk measure CoVaR (conditional Value-at-Risk) is widely used in economics and finance. Formally, it is defined as an (extreme) quantile of one variable (e.g., losses in the financial system) conditional on some other variable (e.g., losses in a bank's shares) being in distress and, hence, measures the spillover of risks. In this article, we propose a dynamic "Co-Quantile Regression", which jointly models VaR and CoVaR semiparametrically. We propose a two-step M-estimator drawing on recently proposed bivariate scoring functions for the pair (VaR, CoVaR). Among others, this allows for the estimation of joint dynamic forecasting models for (VaR, CoVaR). We prove the asymptotic normality of the proposed estimator and simulations illustrate its good finite-sample properties. We apply our co-quantile regression to correct the statistical inference in the existing literature on CoVaR, and to generate CoVaR forecasts for real financial data, which are shown to be superior to existing methods.
    Date: 2022–06
  7. By: Jeonggil Song
    Abstract: Using images containing information on wealth, this research investigates that pictures are capable of reliably predicting the economic prosperity of households. Without surveys on wealth-related information and human-made standard of wealth quality that the traditional wealth-based approach relied on, this novel approach makes use of only images posted on Dollar Street as input data on household wealth across 66 countries and predicts the consumption or income level of each household using the Convolutional Neural Network (CNN) method. The best result predicts the log of consumption level with root mean squared error of 0.66 and R-squared of 0.80 in CNN regression problem. In addition, this simple model also performs well in classifying extreme poverty with an accuracy of 0.87 and F-beta score of 0.86. Since the model shows a higher performance in the extreme poverty classification when I applied the different threshold of poverty lines to countries by their income group, it is suggested that the decision of the World Bank to define poverty lines differently by income group was valid.
    Date: 2022–06
  8. By: Simone Tonini; Francesca Chiaromonte; Alessandro Giovannelli
    Abstract: This paper characterizes the impact of serial dependence on the non-asymptotic estimation error bound of penalized regressions (PRs). Focusing on the direct relationship between the degree of cross-correlation of covariates and the estimation error bound of PRs, we show that orthogonal or weakly cross-correlated stationary AR processes can exhibit high spurious cross-correlations caused by serial dependence. In this respect, we study analytically the density of sample cross-correlations in the simplest case of two orthogonal Gaussian AR(1) processes. Simulations show that our results can be extended to the general case of weakly cross-correlated non Gaussian AR processes of any autoregressive order. To improve the estimation performance of PRs in a time series regime, we propose an approach based on applying PRs to the residuals of ARMA models fit on the observed time series. We show that under mild assumptions the proposed approach allows us both to reduce the estimation error and to develop an effective forecasting strategy. The estimation accuracy of our proposal is numerically evaluated through simulations. To assess the effectiveness of the forecasting strategy, we provide the results of an empirical application to monthly macroeconomic data relative to the Euro Area economy.
    Keywords: Serial dependence; spurious correlation; minimum eigenvalue; penalized regressions; estimation accuracy.
    Date: 2022–07–27
  9. By: S. Roderick Zhang; Bilal Farooq
    Abstract: To help systematically lower anthropogenic Greenhouse gas (GHG) emissions, accurate and precise GHG emission prediction models have become a key focus of the climate research. The appeal is that the predictive models will inform policymakers, and hopefully, in turn, they will bring about systematic changes. Since the transportation sector is constantly among the top GHG emission contributors, especially in populated urban areas, substantial effort has been going into building more accurate and informative GHG prediction models to help create more sustainable urban environments. In this work, we seek to establish a predictive framework of GHG emissions at the urban road segment or link level of transportation networks. The key theme of the framework centers around model interpretability and actionability for high-level decision-makers using econometric Discrete Choice Modelling (DCM). We illustrate that DCM is capable of predicting link-level GHG emission levels on urban road networks in a parsimonious and effective manner. Our results show up to 85.4% prediction accuracy in the DCM models' performances. We also argue that since the goal of most GHG emission prediction models focuses on involving high-level decision-makers to make changes and curb emissions, the DCM-based GHG emission prediction framework is the most suitable framework.
    Date: 2022–06

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