25 Years IWH

Econometric Tools for Macroeconomic Forecasting

This research group belongs to the IWH Research Cluster Macroeconomic Dynamics and Stability. The aim of this group is to enhance research on, and development, implementation, evaluation, and application of quantitative macroeconometric models for forecasting and analysing aggregate economic fluctuations and developments. Research in this group contributes to the econometric foundation and the methodological improvements of the IWH forecasts. During the last years, the IWH has highly specialised in macroeconomic modelling, both for flash estimates and medium-term projections. Furthermore, this group conducts comprehensive empirical analysis and develops econometric tools that are used for third-party funded projects. In the last years, particular models have been developed for e.g. Volkswagen Financial Services AG and for GIZ. The research group contributed in particular on macroeconomic modelling for ministries in Kyrgyzstan and Tajikistan as well as for the institute of forecasting and macroeconomic research (IFMR) Uzbekistan.

IWH Data Project: IWH Real-time Database

Research Cluster
Macroeconomic Dynamics and Stability

Your contact

Dr Katja Heinisch
Dr Katja Heinisch
Mitglied - Department Macroeconomics
Send Message +49 345 7753-836

EXTERNAL FUNDING

11.2015 ‐ 12.2016

Employment and Development in the Republic of Uzbekistan

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Support to sustainable economic development in selected regions of Uzbekistan

Dr Katja Heinisch

05.2016 ‐ 05.2016

Framework and Finance for Private Sector Development in Tajikistan

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Dr Katja Heinisch

07.2016 ‐ 12.2017

Climate Protection and Coal Phaseout: Political Strategies and Measures up to 2030 and beyond

Umweltbundesamt (UBA)

Dr Katja Heinisch

02.2016 ‐ 04.2016

Macroeconomic Reforms and Green Growth - Assessment of economic modelling capacity in Vietnam

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Dr Katja Heinisch

Refereed Publications

Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment

Katja Heinisch Rolf Scheufele

in: Empirical Economics , forthcoming

Abstract

In this paper, we investigate whether there are benefits in disaggregating GDP into its components when nowcasting GDP. To answer this question, we conduct a realistic out-of-sample experiment that deals with the most prominent problems in short-term forecasting: mixed frequencies, ragged-edge data, asynchronous data releases and a large set of potential information. We compare a direct leading indicator-based GDP forecast with two bottom-up procedures—that is, forecasting GDP components from the production side or from the demand side. Generally, we find that the direct forecast performs relatively well. Among the disaggregated procedures, the production side seems to be better suited than the demand side to form a disaggregated GDP nowcast.

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The European Refugee Crisis and the Natural Rate of Output

Katja Heinisch Klaus Wohlrabe

in: Applied Economics Letters , forthcoming

Abstract

The European Commission follows a harmonized approach for calculating structural (potential) output for EU member states that takes into account labour as an important ingredient. This article shows how the recent huge migrants’ inflow to Europe affects trend output. Due to the fact that the immigrants immediately increase the working population but effectively do not enter the labour market, we illustrate that the potential output is potentially upward biased without any corrections. Taking Germany as an example, we find that the average medium-term potential growth rate is lower if the migration flow is modelled adequately compared to results based on the unadjusted European Commission procedure.

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Flow of Conjunctural Information and Forecast of Euro Area Economic Activity

Katja Drechsel L. Maurin

in: Journal of Forecasting , No. 3, 2011

Abstract

Combining forecasts, we analyse the role of information flow in computing short-term forecasts up to one quarter ahead for the euro area GDP and its main components. A dataset of 114 monthly indicators is set up and simple bridge equations are estimated. The individual forecasts are then pooled, using different weighting schemes. To take into consideration the release calendar of each indicator, six forecasts are compiled successively during the quarter. We found that the sequencing of information determines the weight allocated to each block of indicators, especially when the first month of hard data becomes available. This conclusion extends the findings of the recent literature. Moreover, when combining forecasts, two weighting schemes are found to outperform the equal weighting scheme in almost all cases. Compared to an AR forecast, these improve by more than 40% the forecast performance for GDP in the current and next quarter.

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The Financial Crisis from a Forecaster's Perspective

Katja Drechsel Rolf Scheufele

in: Kredit und Kapital , No. 1, 2012

Abstract

This paper analyses the recession in 2008/2009 in Germany. This recession is very different from previous recessions in particular regarding their causes and magnitude. We show to what extent forecasters and forecasts based on leading indicators fail to detect the timing and the magnitude of the recession. This study shows that large forecast errors for both expert forecasts and forecasts based on leading indicators resulted during this recession which implies that the recession was very difficult to forecast. However, some leading indicators (survey data, risk spreads, stock prices) have indicated an economic downturn and hence, beat univariate time series models. Although the combination of individual forecasts provides an improvement compared to the benchmark model, the combined forecasts are worse than several individual models. A comparison of expert forecasts withthe best forecasts based on leading indicators shows only minor deviations. Overall, the range for an improvement of expert forecasts in the crisis compared to indicator forecasts is small.

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The Performance of Short-term Forecasts of the German Economy before and during the 2008/2009 Recession

Katja Drechsel Rolf Scheufele

in: International Journal of Forecasting , No. 2, 2012

Abstract

The paper analyzes the forecasting performance of leading indicators for industrial production in Germany. We focus on single and pooled leading indicator models both before and during the financial crisis. Pairwise and joint significant tests are used to evaluate single indicator models as well as forecast combination methods. In addition, we investigate the stability of forecasting models during the most recent financial crisis.

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Working Papers

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Is East Germany Catching Up? A Time Series Perspective

Bernd Aumann Rolf Scheufele

in: IWH Discussion Papers , No. 14, 2009

Abstract

This paper assesses whether the economy of East Germany is catching up with the West German region in terms of welfare. While the primary measure for convergence and catching up is per capita output, we also look at other macroeconomic indicators such as unemployment rates, wage rates, and production levels in the manufacturingsector. In contrast to existing studies of convergence between regions of reunified Germany, our approach is purely based upon the time series dimension and is thus directly focused on the catching up process in East Germany as a region. Our testing setup includes standard ADF unit root tests as well as unit root tests that endogenously allow for a break in the deterministic component of the process. In our analysis, we find evidence of catching up for East Germany for most of the indicators. However, convergence speed is slow, and thus it can be expected that the catching up process will take further decades until the regional gap is closed.

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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment

Katja Drechsel Rolf Scheufele

in: IWH Discussion Papers , No. 7, 2013

Abstract

This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.

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Outperforming IMF Forecasts by the Use of Leading Indicators

Katja Drechsel Sebastian Giesen Axel Lindner

in: IWH Discussion Papers , No. 4, 2014

Abstract

This study analyzes the performance of the IMF World Economic Outlook forecasts for world output and the aggregates of both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current and the next year, we examine whether IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time dataset for GDP and for the indicators we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts if the publication of the Outlook is only a few months old.

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Should We Trust in Leading Indicators? Evidence from the Recent Recession

Katja Drechsel Rolf Scheufele

in: IWH Discussion Papers , No. 10, 2010

Abstract

The paper analyzes leading indicators for GDP and industrial production in Germany. We focus on the performance of single and pooled leading indicators during the pre-crisis and crisis period using various weighting schemes. Pairwise and joint significant tests are used to evaluate single indicator as well as forecast combination methods. In addition, we use an end-of-sample instability test to investigate the stability of forecasting models during the recent financial crisis. We find in general that only a small number of single indicator models were performing well before the crisis. Pooling can substantially increase the reliability of leading indicator forecasts. During the crisis the relative performance of many leading indicator models increased. At short horizons, survey indicators perform best, while at longer horizons financial indicators, such as term spreads and risk spreads, improve relative to the benchmark.

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The Financial Crisis from a Forecaster’s Perspective

Katja Drechsel Rolf Scheufele

in: IWH Discussion Papers , No. 5, 2011

Abstract

This paper analyses the recession in 2008/2009 in Germany, which is very different from previous recessions, in particular regarding its cause and magnitude. We show to what extent forecasters and forecasts based on leading indicators fail to detect the timing and the magnitude of the recession. This study shows that large forecast errors for both expert forecasts and forecasts based on leading indicators resulted during this recession which implies that the recession was very difficult to forecast. However, some leading indicators (survey data, risk spreads, stock prices) have indicated an economic downturn and hence, beat univariate time series models. Although the combination of individual forecasts provides an improvement compared to the benchmark model, the combined forecasts are worse than several individual models. A comparison of expert forecasts with the best forecasts based on leading indicators shows only minor deviations. Overall, the range for an improvement of expert forecasts during the crisis compared to indicator forecasts is relatively small.

read publication
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