nep-for New Economics Papers
on Forecasting
Issue of 2021‒05‒03
five papers chosen by
Rob J Hyndman
Monash University

  1. The Mean Squared Prediction Error Paradox By Pincheira, Pablo; Hardy, Nicolas
  2. Extending the Heston Model to Forecast Motor Vehicle Collision Rates By Darren Shannon; Grigorios Fountas
  3. Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach By Davide Ferrari; Francesco Ravazzolo; Joaquin Vespignani
  4. The bias and efficiency of the ECB inflation projections: a State dependent analysis By Granziera, Eleonora; Jalasjoki, Pirkka; Paloviita, Maritta
  5. Bayesian Local Projections By Miranda-Agrippino, Silvia; Ricco, Giovanni

  1. By: Pincheira, Pablo; Hardy, Nicolas
    Abstract: In this paper, we show that traditional comparisons of Mean Squared Prediction Error (MSPE) between two competing forecasts may be highly controversial. This is so because when some specific conditions of efficiency are not met, the forecast displaying the lowest MSPE will also display the lowest correlation with the target variable. Given that violations of efficiency are usual in the forecasting literature, this opposite behavior in terms of accuracy and correlation with the target variable may be a fairly common empirical finding that we label here as "the MSPE Paradox." We characterize "Paradox zones" in terms of differences in correlation with the target variable and conduct some simple simulations to show that these zones may be non-empty sets. Finally, we illustrate the relevance of the Paradox with two empirical applications.
    Keywords: Mean Squared Prediction Error, Correlation, Forecasting, Time Series, Random Walk.
    JEL: C1 C10 C12 C18 C2 C22 C4 C40 C5 C52 C53 C58 E0 E00 E30 E31 E37 E44 E47 E52 E58 F30 F31 F37 G00 G12 G15 G17 Q0 Q00 Q02 Q1 Q2 Q3 Q33 Q4 Q43 Q47
    Date: 2021–04–24
  2. By: Darren Shannon; Grigorios Fountas
    Abstract: We present an alternative approach to the forecasting of motor vehicle collision rates. We adopt an oft-used tool in mathematical finance, the Heston Stochastic Volatility model, to forecast the short-term and long-term evolution of motor vehicle collision rates. We incorporate a number of extensions to the Heston model to make it fit for modelling motor vehicle collision rates. We incorporate the temporally-unstable and non-deterministic nature of collision rate fluctuations, and introduce a parameter to account for periods of accelerated safety. We also adjust estimates to account for the seasonality of collision patterns. Using these parameters, we perform a short-term forecast of collision rates and explore a number of plausible scenarios using long-term forecasts. The short-term forecast shows a close affinity with realised rates (95% accuracy). The long-term scenarios suggest that modest targets to reduce collision rates (1.83% annually) and targets to reduce the fluctuations of month-to-month collision rates (by half) could have significant benefits for road safety. The median forecast in this scenario suggests a 50% fall in collision rates, with 75% of simulations suggesting that an effective change in collision rates is observed before 2044. The main benefit the model provides is eschewing the necessity for setting unreasonable safety targets that are often missed. Instead, the model presents the effects that modest and achievable targets can have on road safety over the long run, while incorporating random variability. Examining the parameters that underlie expected collision rates will aid policymakers in determining the effectiveness of implemented policies.
    Date: 2021–04
  3. By: Davide Ferrari (Free University of Bozen-Bolzano, Italy); Francesco Ravazzolo (Free University of Bozen-Bolzano, Italy; BI Norwegian Business School, Norway); Joaquin Vespignani (University of Tasmania, Tasmanian School of Business and Economics, Australia)
    Abstract: This paper focuses on forecasting quarterly nominal global energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the global GDP. To deal with the information on this large database, we apply dynamic factor models based on a penalized maximum likelihood approach that allows to shrink parameters to zero and to estimate sparse factor loadings. The estimated latent factors show considerable sparsity and heterogeneity in the selected loadings across variables. When the model is extended to predict energy commodity prices up to four periods ahead, results indicate larger predictability relative to the benchmark random walk model for 1-quarter ahead for all energy commodities and up to 4 quarters ahead for gas prices. Our model also provides superior forecasts than machine learning techniques, such as elastic net, LASSO and random forest, applied to the same database.
    Keywords: Energy Prices; Forecasting; Dynamic Factor model; Sparse Estimation; Penalized Maximum Likelihood.
    JEL: C1 C5 C8 E3 Q4
    Date: 2021–04
  4. By: Granziera, Eleonora; Jalasjoki, Pirkka; Paloviita, Maritta
    Abstract: We test for bias and efficiency of the ECB inflation forecasts using a confidential dataset of ECB macroeconomic quarterly projections. We investigate whether the properties of the forecasts depend on the level of inflation, by distinguishing whether the inflation observed by the ECB at the time of forecasting is above or below the target. The forecasts are unbiased and efficient on average, however there is evidence of state dependence. In particular, the ECB tends to overpredict (underpredict) inflation at intermediate forecast horizons when inflation is below (above) target. The magnitude of the bias is larger when inflation is above the target. These results hold even after accounting for errors in the external assumptions. We also find evidence of inefficiency, in the form of underreaction to news, but only when inflation is above the target. Our findings bear important implications for the ECB forecasting process and ultimately for its communication strategy.
    JEL: C12 C22 C53 E31 E52
    Date: 2021–04–29
  5. By: Miranda-Agrippino, Silvia (Bank of England, CfM and CEPR); Ricco, Giovanni (University of Warwick, OFCE-Sciences Po and CEPR)
    Abstract: We propose a Bayesian approach to Local Projections that optimally addresses the empirical bias-variance tradeo inherent in the choice between VARs and LPs. Bayesian Local Projections (BLP) regularise the LP regression models by using informative priors, thus estimating impulse response functions potentially better able to capture the properties of the data as compared to iterative VARs. In doing so, BLP preserve the exibility of LPs to empirical model misspeci cations while retaining a degree of estimation uncertainty comparable to a Bayesian VAR with standard macroeconomic priors. As a regularised direct forecast, this framework is also a valuable alternative to BVARs for multivariate out-of-sample projections.
    Keywords: Local Projections ; VARs JEL Classification: C11 ; C14
    Date: 2021

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