nep-for New Economics Papers
on Forecasting
Issue of 2020‒04‒13
seven papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Waiting Time to Treatment for Emergency Department Patients By Pak, Anton; Gannon, Brenda; Staib, Andrew
  2. Forecasting Income Inequality with Demographic Projections By Chong, Terence Tai Leung; Ka, Yiu Tung
  3. Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation By Ergun Yukseltan; Ahmet Yucekaya; Ayse Humeyra Bilge; Esra Agca Aktunc
  4. Some forecasting principles from the M4 competition By Jennifer L. Castle; Jurgen A. Doornik; David Hendry
  5. Predicting tail events in a RIA-EVT-Copula framework By Wei-Zhen Li; Jin-Rui Zhai; Zhi-Qiang Jiang; Gang-Jin Wang; Wei-Xing Zhou
  6. What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios By Andrew Atkeson
  7. Scheduling of Flexible Non-Preemptive Loads By Nathan Dahlin; Rahul Jain

  1. By: Pak, Anton; Gannon, Brenda; Staib, Andrew
    Abstract: Problem definition. The current systems of reporting waiting time to patients in public emergency departments (EDs) has largely relied on rolling average or median estimators which have limited accuracy. This paper proposes to use the statistical learning algorithms that significantly improve waiting time forecasts. Practical Relevance. Generating and using a large set of queueing and service flow variables, we provide evidence of the improvement in waiting time accuracy and reduction in prediction errors. In addition to the mean squared prediction error (MSPE) and mean absolute prediction error (MAPE), we advocate to use the percentage of underpredicted observations as patients are more concerned when the actual waiting time exceeds the time forecast rather than vice versa. Provision of the accurate waiting time also helps to improve satisfaction of ED patients. Methodology. The use of the statistical learning methods (ridge, LASSO, random forest) is motivated by their advantages in exploring data connections in flexible ways, identifying relevant predictors, and preventing overfitting of the data. We also use quantile regression to generate time forecasts which may better address the patient's asymmetric perception of underpredicted and overpredicted ED waiting times. Results. We find robust evidence that the proposed estimators significantly outperform the commonly implemented rolling average. Using queueing and service flow variables together with information on diurnal fluctuations, quantile regression outperforms the best rolling average by 18% with respect to MSPE and reduces by 42% the number of patients with large underpredicted waiting times. Managerial implications. By reporting more accurate waiting times, hospitals may enjoy higher patient satisfaction. We show that to increase the predictive accuracy, a hospital ED may decide to provide predictions to patients registered only during the daytime when the ED operates at full capacity translating to more predictive service rates and the demand for treatments.
    Date: 2020–03–26
  2. By: Chong, Terence Tai Leung; Ka, Yiu Tung
    Abstract: This paper provides a first attempt in the literature to forecast the future evolution of income inequality with the demographic projections. The contribution of this paper is twofold. First, we establish a framework to quantify and analyze the effects of population ageing and the secular upward trend in educational attainment on income inequality. Second, we modify the human capital model and perform microsimulations to forecast a list of standard measures of income inequality of Hong Kong for the coming years of 2021, 2026 and 2031 based on the projected changes in the demographic structure of Hong Kong’s working population. The pseudo out-of-sample forecasts are reasonably close to the corresponding realized values. Our true out-of-sample forecasts suggest that income disparity will be alleviated in the next 15 years, as a result of the increasingly equal spread of level of schooling across the workforce.
    Keywords: Income Inequality; Demographic Projections; Population Ageing.
    JEL: D63 J11
    Date: 2019–12–01
  3. By: Ergun Yukseltan; Ahmet Yucekaya; Ayse Humeyra Bilge; Esra Agca Aktunc
    Abstract: Due to expensive infrastructure and the difficulties in storage, supply conditions of natural gas are different from those of other traditional energy sources like petroleum or coal. To overcome these challenges, supplier countries require take-or-pay agreements for requested natural gas quantities. These contracts have many pre-clauses; if they are not met due to low/high consumption or other external factors, buyers must completely fulfill them. A similar contract is then imposed on distributors and wholesale consumers. It is thus important for all parties to forecast their daily, monthly, and annual natural gas demand to minimize their risk. In this paper, a model consisting of a modulated expansion in Fourier series, supplemented by deviations from comfortable temperatures as a regressor is proposed for the forecast of monthly and weekly consumption over a one-year horizon. This model is supplemented by a day-ahead feedback mechanism for the forecast of daily consumption. The method is applied to the study of natural gas consumption for major residential areas in Turkey, on a yearly, monthly, weekly, and daily basis. It is shown that residential heating dominates winter consumption and masks all other variations. On the other hand, weekend and holiday effects are visible in summer consumption and provide an estimate for residential and industrial use. The advantage of the proposed method is the capability of long term projections and to outperform time series methods.
    Date: 2020–02
  4. By: Jennifer L. Castle (Magdelen College, University of Oxford); Jurgen A. Doornik (Nuffield College, University of Oxford); David Hendry (Nuffield College, University of Oxford)
    Abstract: Economic forecasting is difficult, largely because of the many sources of nonstationarity. The M4 competition aims to improve the practice of economic forecasting by providing a large data set on which the efficacy of forecasting methods can be evaluated. We consider the general principles that seem to be the foundation for successful forecasting, and show how these are relevant for methods that do well in M4. We establish some general properties of the M4 data set, which we use to improve the basic benchmark methods, as well as the Card method that we created for our submission to the M4 competition. A data generation process is proposed that captures the salient features of the annual data in M4.
    Keywords: Automatic forecasting, Calibration, Prediction intervals, Regression, M4, Seasonality, Software, Time series, Unit roots
    Date: 2019–01–09
  5. By: Wei-Zhen Li (ECUST); Jin-Rui Zhai (ECUST); Zhi-Qiang Jiang (ECUST); Gang-Jin Wang (HNU); Wei-Xing Zhou (ECUST)
    Abstract: Predicting the occurrence of tail events is of great importance in financial risk management. By employing the method of peak-over-threshold (POT) to identify the financial extremes, we perform a recurrence interval analysis (RIA) on these extremes. We find that the waiting time between consecutive extremes (recurrence interval) follow a $q$-exponential distribution and the sizes of extremes above the thresholds (exceeding size) conform to a generalized Pareto distribution. We also find that there is a significant correlation between recurrence intervals and exceeding sizes. We thus model the joint distribution of recurrence intervals and exceeding sizes through connecting the two corresponding marginal distributions with the Frank and AMH copula functions, and apply this joint distribution to estimate the hazard probability to observe another extreme in $\Delta t$ time since the last extreme happened $t$ time ago. Furthermore, an extreme predicting model based on RIA-EVT-Copula is proposed by applying a decision-making algorithm on the hazard probability. Both in-sample and out-of-sample tests reveal that this new extreme forecasting framework has better performance in prediction comparing with the forecasting model based on the hazard probability only estimated from the distribution of recurrence intervals. Our results not only shed a new light on understanding the occurring pattern of extremes in financial markets, but also improve the accuracy to predict financial extremes for risk management.
    Date: 2020–04
  6. By: Andrew Atkeson
    Abstract: This note is intended to introduce economists to a simple SIR model of the progression of COVID-19 in the United States over the next 12-18 months. An SIR model is a Markov model of the spread of an epidemic in a population in which the total population is divided into categories of being susceptible to the disease (S), actively infected with the disease (I), and recovered (or dead) and no longer contagious (R). How an epidemic plays out over time is determined by the transition rates between these three states. This model allows for quantitative statements regarding the tradeoff between the severity and timing of suppression of the disease through social distancing and the progression of the disease in the population. Example applications of the model are provided. Special attention is given to the question of if and when the fraction of active infections in the population exceeds 1% (at which point the health system is forecast to be severely challenged) and 10% (which may result in severe staffing shortages for key financial and economic infrastructure) as well as the cumulative burden of the disease over an 18 month horizon.
    JEL: C0 E0
    Date: 2020–03
  7. By: Nathan Dahlin; Rahul Jain
    Abstract: A market consisting of a generator with thermal and renewable generation capability, a set of non-preemptive loads (i.e., loads which cannot be interrupted once started), and an independent system operator (ISO) is considered. Loads are characterized by durations, power demand rates and utility for receiving service, as well as disutility functions giving preferences for time slots in which service is preferred. Given this information, along with the generator's thermal generation cost function and forecast renewable generation, the social planner solves a mixed integer program to determine a load activation schedule which maximizes social welfare. Assuming price taking behavior, we develop a competitive equilibrium concept based on a relaxed version of the social planner's problem which includes prices for consumption and incentives for flexibility, and allows for probabilistic allocation of power to loads. Considering each load as representative of a population of identical loads with scaled characteristics, we demonstrate that the relaxed social planner's problem gives an exact solution to the original mixed integer problem in the large population limit, and give a market mechanism for implementing the competitive equilibrium.
    Date: 2020–03

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