Forecasting
http://lists.repec.org/mailman/listinfo/nep-for
Forecasting
2020-03-30
On bootstrapping tests of equal forecast accuracy for nested models
http://d.repec.org/n?u=RePEc:een:camaaa:2020-27&r=for
The asymptotic distributions of the recursive out-of-sample forecast accuracy test statistics depend on stochastic integrals of Brownian motion when the models under comparison are nested. This often complicates their implementation in practice because the computation of their asymptotic critical values is costly. Hansen and Timmermann (2015, Econometrica) propose a Wald approximation of the commonly used recursive F-statistic and provide a simple characterization of the exact density of its asymptotic distribution. However, this characterization holds only when the larger model has one extra predictor or the forecast errors are homoscedastic. No such closed-form characterization is readily available when the nesting involves more than one predictor and heteroskedasticity is present. We first show both the recursive F-test and its Wald approximation have poor finite-sample properties, especially when the forecast horizon is greater than one. We then propose a hybrid bootstrap method consisting of a block moving bootstrap (which is nonparametric) and a residual based bootstrap for both statistics, and establish its validity. Simulations show that our hybrid bootstrap has good finite-sample performance, even in multi-step ahead forecasts with heteroscedastic or autocorrelated errors, and more than one predictor. The bootstrap method is illustrated on forecasting core inflation and GDP growth.
Firmin Doko Tchatoka
Qazi Haque
Out-of-sample forecasts, HAC estimator, Moving block bootstrap, Bootstrap consistency
2020-03
Betting markets for English Premier League results and scorelines: evaluating a simple forecasting model
http://d.repec.org/n?u=RePEc:rdg:emxxdp:em-dp2020-03&r=for
Using betting odds from two recent seasons of English Premier League football matches, we evaluate probability and point forecasts generated from a standard statistical model of goal scoring. The bookmaker odds show significant evidence of the favourite-longshot bias for exact scorelines, which is not generally present for match results. We find evidence that the scoreline probability forecasts from the model are better than what the odds of bookmakers imply, based on forecast encompassing regressions. However, when we apply a simple betting strategy using point forecasts from the model, there are no substantial or consistent financial returns to be made over the two seasons. In other words, there is no evidence from this particular statistical model that the result, scoreline, margin of victory or total goals betting markets are on average inefficient.
J. James Reade
Carl Singleton
Leighton Vaughan Williams
Forecasting, Statistical modelling, Regression models, Prediction markets
2020-03-20
Does More Expert Adjustment Associate with Less Accurate Professional Forecasts?
http://d.repec.org/n?u=RePEc:ems:eureir:125158&r=for
Professional forecasters can rely on an econometric model to create their forecasts. It is usually unknown to what extent they adjust an econometric model‐based forecast. In this paper we show, while making just two simple assumptions, that it is possible to estimate the persistence and variance of the deviation of their forecasts from forecasts from an econometric model. A key feature of the data that facilitates our estimates is that we have forecast updates for the same forecast target. An illustration to consensus forecasters who give forecasts for GDP growth, inflation and unemployment for a range of countries and years suggests that the more a forecaster deviates from a prediction from an econometric model, the less accurate are the forecasts.
Franses, Ph.H.B.F.
Welz, M.
professional forecasters, econometric model, expert adjustment, forecast accuracy
2020-01-01
Forecasting in a complex environment: Machine learning sales expectations in a Stock Flow Consistent Agent-Based simulation model
http://d.repec.org/n?u=RePEc:jau:wpaper:2020/17&r=for
The aim of this paper is to investigate how different degrees of sophistication in agents’ behavioural rules may affect individual and macroeconomic performances. In particular, we analyze the effects of introducing into an agentbased macro model firms that are able to formulate effective sales forecasts by using machine learning. These techniques are able to provide predictions that are unbiased and present a certain degree of accuracy, especially in the case of a genetic algorithm. We observe that machine learning allows firms to increase profits, though this result in a declining wage share and a smaller long-run growth rate. Moreover, the predictive methods are able to formulate expectations that remain unbiased when shocks are not massive, thus providing firms with forecasting capabilities that to a certain extent may be consistent with the Lucas Critique.
Ermanno Catullo
Mauro Gallegati
Alberto Russo
agent-based model, machine learning, genetic algorithm, forecasting, policy shocks
2020
On bootstrapping tests of equal forecast accuracy for nested models
http://d.repec.org/n?u=RePEc:uwa:wpaper:20-06&r=for
Firmin Doko Tchatoka
Qazi Haque
Out-of-sample forecasts; HAC estimator; Moving block bootstrap; Bootstrap consistency
2020
Early Childhood Education and Life-cycle Health
http://d.repec.org/n?u=RePEc:hka:wpaper:2020-011&r=for
This paper forecasts the life-cycle treatment effects on health of a high-quality early childhood program. Our predictions combine microsimulation using non-experimental data with experimental data from a midlife long-term follow-up. The follow-up incorporated a full epidemiological exam. The program mainly benefits males and significantly reduces the prevalence of heart disease, stroke, cancer, and mortality across the life-cycle. For men, we estimate an average reduction of 3.8 disability-adjusted years (DALYs). The reduction in DALYs is relatively small for women. The gain in quality-adjusted life years (QALYs) is almost enough to offset all of the costs associated with program implementation for males and half of program costs for women.
Jorge Luis Garcia
James J. Heckman
early childhood education, life-cycle health, long-term forecasts, program evaluation, randomized trials
2020-03