Multivariate Forecast Evaluation and Rationality Testing

Komunjer, Ivana; Owyang, Michael T.

doi:10.1162/rest_a_00215

Cited by 49 publications

(87 citation statements)

References 35 publications

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“…The Multivariate Asymmetric Loss Function (MALF) (Komunjer and Owyang 2011) is the generalization of LinLin LF. For GS purposes, it is expressed as

L_{2} (F_{bold-italicY}_{o}, {bold-italicμ}_{s}, τ) = (∥ e ∥_{2} + {bold-italicτ}^{bold-italic'} e) ∥ e ∥_{2}

where

∥ e ∥_{2} = {(e_{1}^{2} + e_{2}^{2} + \dots + e_{t}^{2})}^{\frac{1}{2}}

is the Euclidean norm and

bold-italicτ

controls the degree of asymmetry.…”

Section: Methodsmentioning

confidence: 99%

“…A simple asymmetric LF is the linear-linear (LinLin) function (Berk, 2011), which is linear on both sides of the target distribution but with different slopes. A generalization of the LinLin function in a multivariate setting is the Multivariate Asymmetric LF (MALF) (Komunjer and Owyang 2011), which is a function of the distance between predicted BV of individuals’ and parents’ means and a parameter that controls the degree of asymmetry.…”

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confidence: 99%

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A Bayesian Decision Theory Approach for Genomic Selection

Villar-Hernández

Pérez-Elizalde

Crossa

et al. 2018

G3 Genes|Genomes|Genetics

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Plant and animal breeders are interested in selecting the best individuals from a candidate set for the next breeding cycle. In this paper, we propose a formal method under the Bayesian decision theory framework to tackle the selection problem based on genomic selection (GS) in single- and multi-trait settings. We proposed and tested three univariate loss functions (Kullback-Leibler, KL; Continuous Ranked Probability Score, CRPS; Linear-Linear loss, LinLin) and their corresponding multivariate generalizations (Kullback-Leibler, KL; Energy Score, EnergyS; and the Multivariate Asymmetric Loss Function, MALF). We derived and expressed all the loss functions in terms of heritability and tested them on a real wheat dataset for one cycle of selection and in a simulated selection program. The performance of each univariate loss function was compared with the standard method of selection (Std) that does not use loss functions. We compared the performance in terms of the selection response and the decrease in the population’s genetic variance during recurrent breeding cycles. Results suggest that it is possible to obtain better performance in a long-term breeding program using the single-trait scheme by selecting 30% of the best individuals in each cycle but not by selecting 10% of the best individuals. For the multi-trait approach, results show that the population mean for all traits under consideration had positive gains, even though two of the traits were negatively correlated. The corresponding population variances were not statistically different from the different loss function during the 10th selection cycle. Using the loss function should be a useful criterion when selecting the candidates for selection for the next breeding cycle.

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“…The Multivariate Asymmetric Loss Function (MALF) (Komunjer and Owyang 2011) is the generalization of LinLin LF. For GS purposes, it is expressed as

L_{2} (F_{bold-italicY}_{o}, {bold-italicμ}_{s}, τ) = (∥ e ∥_{2} + {bold-italicτ}^{bold-italic'} e) ∥ e ∥_{2}

where

∥ e ∥_{2} = {(e_{1}^{2} + e_{2}^{2} + \dots + e_{t}^{2})}^{\frac{1}{2}}

is the Euclidean norm and

bold-italicτ

controls the degree of asymmetry.…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

A Bayesian Decision Theory Approach for Genomic Selection

Villar-Hernández

Pérez-Elizalde

Crossa

et al. 2018

G3 Genes|Genomes|Genetics

View full text Add to dashboard Cite

show abstract

“…Even though we only have forecasts for one year, we illustrate this issue with the simplest univariate case. In addition, Komunjer and Owyang (2012) evaluate forecasts in a multivariate framework by using forecast errors to derive the weights of a utility function with the objective of determining whether forecasts are rationalizable. These forecasts have been made T times.…”

Section: Evaluating Multivariate Forecastsmentioning

confidence: 99%

Evaluating Forecasts of a Vector of Variables: A German Forecasting Competition

Sinclair

Stekler

Müller-Dröge

2016

Journal of Forecasting

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In this paper we explore methodologies appropriate for evaluating a forecasting competition when the participants predict a number of variables that may be related to each other and are judged for a single period. Typically, forecasting competitions are judged on a variable-by-variable basis, but a multivariate analysis is required to determine how each competitor performed overall. We use three different multivariate tests to determine an overall winner for a forecasting competition for the German economy across 25 different institutions for a single time period using a vector of eight key economic variables. We find that neglecting the cross-variable relationships greatly alters the outcome of the forecasting competition. Davies et al. (2011) provide a useful summary of the framework used in these papers. 6 Clements and Hendry (1993) did consider the methodology of evaluating forecasts from dynamic systems. 494 T. Sinclair et al.

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“…We assess the finite‐sample out‐of‐sample performance of the prediction regions considered and compare their performance according to several metrics. There is a rather extensive literature on the evaluation of point multivariate forecasts; see, for example, Komunjer and Owyang (). Also, recently, several authors have proposed different evaluation criteria for multivariate forecast densities; see Diebold, Han, and Tay (), Clements and Smith (), Gneiting et al (), González‐Rivera and Yoldas (), and González‐Rivera and Sun ().…”

Section: Introductionmentioning

confidence: 99%

Prediction regions for interval‐valued time series

González‐Rivera

Luo

Ruiz

2020

J of Applied Econometrics

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We approximate probabilistic forecasts for interval-valued time series by offering alternative approaches. After fitting a possibly non-Gaussian bivariate vector autoregression (VAR) model to the center/log-range system, we transform prediction regions (analytical and bootstrap) for this system into regions for center/range and upper/lower bounds systems. Monte Carlo simulations show that bootstrap methods are preferred according to several new metrics. For daily S&P 500 low/high returns, we build joint conditional prediction regions of the return level and volatility. We illustrate the usefulness of obtaining bootstrap forecasts regions for low/high returns by developing a trading strategy and showing its profitability when compared to using point forecasts.

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Multivariate Forecast Evaluation and Rationality Testing

Cited by 49 publications

References 35 publications

A Bayesian Decision Theory Approach for Genomic Selection

A Bayesian Decision Theory Approach for Genomic Selection

Evaluating Forecasts of a Vector of Variables: A German Forecasting Competition

Prediction regions for interval‐valued time series

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