The Role of Survey Data in Nowcasting Euro Area GDP Growth

Girardi, Alessandro; Gayer, Christian; Reuter, Andreas

doi:10.1002/for.2383

Cited by 22 publications

(20 citation statements)

References 45 publications

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“…21 These empirical findings would mean that nowcasts based on the survey data are quite reliable in periods of tranquility. The better nowcasting performance measured by the MSE can be regarded as empirical evidence that the survey data have genuine predictive power beyond their timeliness, as reported in Banbura and Rünstler (2011) and Girardi et al (2015). The nowcasting performance ofĝdp To sum up, our empirical analysis indicates that the survey data have quite considerable predictive power (almost as strong as that of the hard data) in periods of tranquility, but less so in periods of turmoil.…”

Section: Evaluation Of Nowcasting Performancesupporting

confidence: 71%

“…These empirical findings would mean that nowcasts based on the survey data are quite reliable in periods of tranquility. The better nowcasting performance measured by the MSE can be regarded as empirical evidence that the survey data have genuine predictive power beyond their timeliness, as reported in Banbura and Rünstler () and Girardi et al (). The nowcasting performance of

{\overset{gdp}{true}}_{i}^{false(\overset{s}{true} false)}

lies partly somewhere between those of

{\overset{gdp}{true}}_{i}^{false(s false)}

and

{\overset{gdp}{true}}_{_{i}}^{false(s + \overset{s}{true} false)}

for all types of month.…”

Section: Empirical Applicationmentioning

confidence: 80%

“…Our main finding is that the predictive power of survey data depends on the economic circumstances: Survey data are more useful in periods of tranquility, and less so in periods of turmoil. In this respect, our empirical result can be regarded as a supplement for the empirical paper of Banbura and Rünstler (2011) and Girardi et al (2015).…”

Section: Introductionmentioning

confidence: 86%

“…We, however, include them in the nonsurvey data and concentrate on the survey data. Furthermore, Girardi et al (2015) found that in the case of no publication lag the financial group usually has (except during the 2008-9 financial crisis) no impact on the accuracy of GDP nowcasts. 11 The reason for considering the nonsurvey data separately is that it can provide some useful information on the size of the contribution of the hard data alone, and how the predictive power of the hard data improves through the revision.…”

Section: Nowcasting Exercisementioning

confidence: 99%

“…The empirical consensus on this issue is that survey data have useful indicators for GDP nowcasting, but their relevance weakens when hard data are available; see Giannone, Reichlin, and Small (2008), for example. Specifically, Girardi, Gayer, and Reuter (2015) set a hypothetical scenario (where both groups of data are available without any time lags) in order to investigate whether the value of survey data is rooted in their timeliness or in their genuine information content. One of their findings is that survey data have genuine predictive power beyond their timeliness, as also reported in Banbura and Rünstler (2011).…”

Section: Introductionmentioning

confidence: 99%

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A note on the predictive power of survey data in nowcasting euro area GDP

Kurz‐Kim¹

2019

Journal of Forecasting

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This paper investigates the trade‐off between timeliness and quality in nowcasting practices. This trade‐off arises when the frequency of the variable to be nowcast, such as gross domestic product (GDP), is quarterly, while that of the underlying panel data is monthly; and the latter contains both survey and macroeconomic data. These two categories of data have different properties regarding timeliness and quality: the survey data are timely available (but might possess less predictive power), while the macroeconomic data possess more predictive power (but are not timely available because of their publication lags). In our empirical analysis, we use a modified dynamic factor model which takes three refinements for the standard dynamic factor model of Stock and Watson (Journal of Business and Economic Statistics, 2002, 20, 147–162) into account, namely mixed frequency, preselections and cointegration among the economic variables. Our main finding from a historical nowcasting simulation based on euro area GDP is that the predictive power of the survey data depends on the economic circumstances; namely, that survey data are more useful in tranquil times, and less so in times of turmoil.

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Section: Evaluation Of Nowcasting Performancesupporting

confidence: 71%

{\overset{gdp}{true}}_{i}^{false(\overset{s}{true} false)}

lies partly somewhere between those of

{\overset{gdp}{true}}_{i}^{false(s false)}

and

{\overset{gdp}{true}}_{_{i}}^{false(s + \overset{s}{true} false)}

for all types of month.…”

Section: Empirical Applicationmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 86%

Section: Nowcasting Exercisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A note on the predictive power of survey data in nowcasting euro area GDP

Kurz‐Kim¹

2019

Journal of Forecasting

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Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

Çepni

Güney

Swanson

2019

Journal of Forecasting

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In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.

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The role of indicator selection in nowcasting euro-area GDP in pseudo-real time

Girardi

Golinelli

Pappalardo³

2016

Empir Econ

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Building on the literature on regularization and dimension reduction methods, we have developed a quarterly forecasting model for euro area GDP. This method consists in bridging quarterly national accounts data using factors extracted from a large panel of monthly and quarterly series including business surveys and financial indicators. The pseudo real-time nature of the information set is accounted for as the pattern of publication lags is considered. Forecast evaluation exercises show that predictions obtained through various dimension reduction methods outperform both the benchmark AR and the diffusion index model without pre-selected indicators. Moreover, forecast combination significantly reduces forecast error.

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The Role of Survey Data in Nowcasting Euro Area GDP Growth

Cited by 22 publications

References 45 publications

A note on the predictive power of survey data in nowcasting euro area GDP

A note on the predictive power of survey data in nowcasting euro area GDP

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

The role of indicator selection in nowcasting euro-area GDP in pseudo-real time

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