Testing the optimality of inflation forecasts under flexible loss with random forests

Behrens, Christoph; Pierdzioch, Christian; Risse, Marian

doi:10.1016/j.econmod.2018.02.004

Cited by 28 publications

(23 citation statements)

References 37 publications

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“…When analyzing the optimality of forecasts, it can be crucial to consider a flexible loss function as results can be misleading if a researcher assumes a certain type of loss function, which, in fact, is misspecified Pierdzioch and Rülke (2013). I, therefore, extend the Behrens et al (2018c) approach to testing forecast optimality by estimating random regression forests with a quadratic, i.e., symmetric, loss function as well as random classification forests with a flexible loss function to analyze whether or not German trade forecasts are best described by a symmetric loss function. Tree-based models are powerful nonparametric modeling instruments, for which no model specifications regarding the linkage of predictor and response variables need to be imposed a priori and that can deal with datasets with a small number of observations relative to the number of predictor variables.…”

Section: Introductionmentioning

confidence: 99%

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A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts Under Flexible Loss

Behrens

2019

Economies

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This study contributes to research on the nonparametric evaluation of German trade forecasts. To this end, I compute random classification and regression forests to analyze the optimality of annual German export and import growth forecasts from 1970 to 2017. A forecast is considered as optimal if a set of predictors, which models the information set of a forecaster at the time of forecast formation, has no explanatory power for the corresponding (sign of the) forecast error. I analyze trade forecasts of four major German economic research institutes, a collaboration of German economic research institutes, and one international forecaster. For trade forecasts with a horizon of half-a-year, I cannot reject forecast optimality for all but one forecaster. In the case of a forecast horizon of one year, forecast optimality is rejected in more cases if the underlying loss function is assumed to be quadratic. Allowing for a flexible loss function results in more favorable assessment of forecast optimality.

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Section: Introductionmentioning

confidence: 99%

“…I describe random forests and my adaption of the Behrens et al (2018c) approach to testing forecast optimality in Section 2. In Section 3, I present my data and I summarize my empirical results in Section 4.…”

Section: Introductionmentioning

confidence: 99%

A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts Under Flexible Loss

Behrens

2019

Economies

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“…Our choice of predictors follows Behrens et al (2018aBehrens et al ( , 2018b and Döpke, Fritsche, and Pierdzioch (2017). As financial predictors, we use the US federal funds rate, German money market rate (3 months), the term spread (the difference between the monthly averages of the yield on debt securities with a maturity of more than 3 years and the monthly average money market rate), and the continuously compounded year-on-year returns on the OECD share price index for Germany.…”

Section: The Datamentioning

confidence: 99%

“…In a univariate setting, Behrens, Pierdzioch, and Risse (2018a) use Bayesian regression trees to analyze the efficiency of growth and inflation forecasts. Behrens, Pierdzioch, and Risse (2018b) use univariate random forests to study the optimality of inflation forecasts in a univariate setting under flexible loss. Behrens, Pierdzioch, and Risse (2018b) use univariate random forests to study the optimality of inflation forecasts in a univariate setting under flexible loss.…”

Section: Introductionmentioning

confidence: 99%

“…They reject strong efficiency of forecasts and find evidence against weak efficiency for longer-term growth and inflation forecasts. Behrens, Pierdzioch, and Risse (2018b) use univariate random forests to study the optimality of inflation forecasts in a univariate setting under flexible loss. They cannot reject optimality of longer-term inflation forecasts.…”

Section: Introductionmentioning

confidence: 99%

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A test of the joint efficiency of macroeconomic forecasts using multivariate random forests

Behrens

Pierdzioch

Risse

2018

Journal of Forecasting

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We contribute to recent research on the joint evaluation of the properties of macroeconomic forecasts in a multivariate setting. The specific property of forecasts that we are interested in is their joint efficiency. We study the joint efficiency of forecasts by means of multivariate random forests, which we use to model the links between forecast errors and predictor variables in a forecaster's information set. We then use permutation tests to study whether the Mahalanobis distance between the predicted forecast errors for the growth and inflation forecasts of four leading German economic research institutes and actual forecast errors is significantly smaller than under the null hypothesis of forecast efficiency. We reject joint efficiency in several cases, but also document heterogeneity across research institutes with regard to the joint efficiency of their forecasts.

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Testing the optimality of USDA's WASDE forecasts under unknown loss

Ding,

Katchova

2023

Agribusiness

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Motivated by the long‐lasting debate on whether the United States Department of Agriculture's (USDA's) World Agricultural Supply and Demand Estimates (WASDE) forecasts are optimal, we employ an unknown loss method for ex post evaluation which assumes that the USDA forecasters' loss function is unknown. We conduct optimality tests of the WASDE forecasts for corn, soybeans, and wheat published during 1988–2019. Our results suggest that USDA forecasters generally realize optimality during the data‐generating process. Our findings are consistent with previous studies when narrowing down the more general unknown loss function to the symmetric or asymmetric loss function which assumes a specific shape for the loss function. This study provides implications based on the unknown loss function that the USDA forecasters can boost their information set as an alternative way to improve the WASDE forecasts. [EconLit Citations: D84, E37, Q13, Q14].

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Testing the optimality of inflation forecasts under flexible loss with random forests

Cited by 28 publications

References 37 publications

A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts Under Flexible Loss

A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts Under Flexible Loss

A test of the joint efficiency of macroeconomic forecasts using multivariate random forests

Testing the optimality of USDA's WASDE forecasts under unknown loss

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