Bottom-up or direct? Forecasting German GDP in a data-rich environment

Drechsel, Katja; Scheufele, Rolf

doi:10.1007/s00181-016-1218-x

Cited by 25 publications

(27 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, the REER and NEERVOLA do not help much in predicting quarterly exports (see also Lehmann, ). Overall, the results confirm similar findings for GDP forecasting, that is, that surveys (soft data) are particularly helpful in earlier settings when hard data is very incomplete (Giannone, Reichlin, & Simonelli, ; Banbura & Rünstler, ; Heinisch & Scheufele, 2018a).…”

Section: Resultssupporting

confidence: 80%

“… For robustness, we also estimated the MIDAS specification restricting the coefficients by using the Almon lag polynomial β i = ω i ( π ). The Almon specification allows for flexible responses with only a small number of parameters (following Heinisch & Scheufele, 2018a), which can be estimated by restricted least squares. The Almon lag polynomial is given by

ω_{i} false(π false) = \sum_{l = 0}^{s} π_{l} i^{l}

.…”

mentioning

confidence: 99%

See 1 more Smart Citation

PMIs: Reliable indicators for exports?

Grossmann

Scheufele

2019

Rev International Economics

Self Cite

View full text Add to dashboard Cite

Foreign economic activity is a major determinant of export developments. However, foreign GDP figures are published too late to be useful for short‐term forecasting. This paper presents a number of indicators based on the widely available PMI surveys that provide very early signals of foreign activity. Using MIDAS models we analyze the in‐ and out‐of‐sample performance of these and related indicators for two very trade‐exposed countries (Germany and Switzerland). We find that the monthly indicators based on foreign PMIs are strongly correlated with quarterly export growth. The forecast comparison shows that PMI‐based indicators perform very well relative to other benchmark models.

show abstract

Section: Resultssupporting

confidence: 80%

ω_{i} false(π false) = \sum_{l = 0}^{s} π_{l} i^{l}

.…”

mentioning

confidence: 99%

PMIs: Reliable indicators for exports?

Grossmann

Scheufele

2019

Rev International Economics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Table and Figures and show the forecasting performance results obtained using real‐time and final data to predict final GDP growth. In general, the results confirm findings in previous studies that additional information tends to improve forecast accuracy (as shown by Banbura et al ., ; Heinisch and Scheufele, , for typical nowcasting applications). This result is particularly true for hard data.…”

Section: Resultsmentioning

confidence: 99%

“… For Germany, only a small number of papers have explicitly taken into account the mixed‐frequency problem by investigating leading indicators (Mittnik and Zadrozny, ; Schumacher and Breitung, ; Wohlrabe, ; Heinisch and Scheufele, ). …”

mentioning

confidence: 99%

Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

Heinisch

Scheufele

2019

German Economic Review

Self Cite

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The responsibility for discussion papers lies solely with the individual authors. The views expressed herein do not necessarily represent those of the IWH. The papers represent preliminary work and are circulated to encourage discussion with the authors. Citation of the discussion papers should account for their provisional character; a revised version may be available directly from the authors. In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of survey data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information. Terms of use: Documents in

show abstract

“…Additionally, optimal trade forecasts are an essential part of GDP forecasts as macroeconomic forecasters tend to implement a so-called disaggregated approach when forming such forecast. In that case, economic research institutes individually predict all components of the GDP and combine these predictions to a forecast of total output (see, among others, (Angelini et al 2010;Heinisch and Scheufele 2018) for a comparison of direct and disaggregated forecasting approaches). Research on predictions of single GDP components mostly focuses on forecasts of private consumption (see, for instance, Vosen and Schmidt 2011).…”

Section: Introductionmentioning

confidence: 99%

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

Behrens

2019

Economies

View full text Add to dashboard Cite

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.

show abstract

Bottom-up or direct? Forecasting German GDP in a data-rich environment

Cited by 25 publications

References 65 publications

PMIs: Reliable indicators for exports?

PMIs: Reliable indicators for exports?

Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

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

Contact Info

Product

Resources

About