PMIs: Reliable indicators for exports?

Grossmann, Sandra Hanslin; Scheufele, Rolf

doi:10.1111/roie.12395

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…However, studies analyzing German trade forecasts are scarce. The existing studies concentrate on forecasting a single trade aggregate, i.e., German exports (Elstner et al 2013;Grossmann and Scheufele 2019;Jannsen and Richter 2012), or imports (Grimme et al 2019;Hetemäki and Mikkola 2005). However, despite the fact that suboptimal trade forecasts can potentially be costly if, for instance, a protectionist trade policy is pursued due to such forecasts, the evaluation of trade forecasts has not received much attention.…”

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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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%

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

Behrens

2019

Economies

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“…Overall, survey indicators produce lower forecast errors than hard indicators do. Finally, Hanslin Grossmann and Scheufele (2019) show that a weighted Purchasing Manager Index (PMI) from major trading partners improves Swiss exports more than other indicators.…”

Section: Introductionmentioning

confidence: 99%

Forecasting exports across Europe: What are the superior survey indicators?

Lehmann

2020

Empir Econ

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In this study, we systematically evaluate the potential of a bunch of survey-based indicators from different economic branches to forecasting export growth across a multitude of European countries. Our pseudo-out-of-sample analyses reveal that the best performing indicators beat a well-specified benchmark model in terms of forecast accuracy. It turns out that four indicators are superior: the Export Climate, the Production Expectations of domestic manufacturing firms, the Industrial Confidence Indicator, and the Economic Sentiment Indicator. Two robustness checks confirm these results. As exports are highly volatile and turn out to be a large demand-side component of gross domestic product, our results can be used by applied forecasters in order to choose the best performing indicators and thus increasing the accuracy of export forecasts.

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A method of forecasting trade export volume based on back-propagation neural network

Dai

2022

Neural Comput & Applic

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Financial forecasting has been greatly improved in recent years, but at long horizons, forecast accuracy may be low. Foreign trade plays an important role in introducing advanced technology and equipment, expanding employment opportunities, increasing government revenue and promoting economic growth. The main purpose of this paper is to predict the export volume of foreign trade through a back-propagation neural network (BPNN). To shed light on the characteristics of foreign trade and the export volume calculation method, this paper uses BPNN for forecasting. This method has a unique and advanced advantage in solving nonlinear problems and is very suitable for solving forecasting and decision-making problems related to nonlinear financial systems. By establishing multifactor and single-factor export forecasting models, the export volume of a single Chinese city in recent years is forecasted and compared with the actual export volume. The forecasting accuracy of our model is more than 30% higher than that of the traditional forecasting method, and the application is also approximately 15% more accurate than the traditional method, indicating that the method used in this paper is more in line with the growth trend of the actual export data. As a key part of the economic system, foreign trade is an important force driving economic growth. Therefore, developing foreign trade is a suitable path to pursue growth.

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PMIs: Reliable indicators for exports?

Cited by 5 publications

References 34 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

Forecasting exports across Europe: What are the superior survey indicators?

A method of forecasting trade export volume based on back-propagation neural network

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