Econometric Tools for Macroeconomic Forecasting and Simulation

The aim of this research 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

01.2016 ‐ 12.2017

Development of analytical tools based on Input-Output table

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

The aim of the project was the development of an analytical tool to assess the gains and losses of possible state programs supporting the development of the private sector of the Tajik economy.

Dr Katja Heinisch

01.2017 ‐ 12.2017

Support to Sustainable Economic Development in Selected Regions of Uzbekistan

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Dr Andrej Drygalla

01.2017 ‐ 12.2017

Short-term Macroeconomic Forecasting Model in Ministry of Economic Development and Trade of Ukraine

Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Dr Andrej Drygalla

01.2018 ‐ 12.2023

EuropeAid (EU Framework Contract)

European Commission

Professor Dr Oliver Holtemöller

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 ‐ 12.2018

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

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An Evolutionary Algorithm for the Estimation of Threshold Vector Error Correction Models

Makram El-Shagi

in: International Economics and Economic Policy, No. 4, 2011

Abstract

We develop an evolutionary algorithm to estimate Threshold Vector Error Correction models (TVECM) with more than two cointegrated variables. Since disregarding a threshold in cointegration models renders standard approaches to the estimation of the cointegration vectors inefficient, TVECM necessitate a simultaneous estimation of the cointegration vector(s) and the threshold. As far as two cointegrated variables are considered, this is commonly achieved by a grid search. However, grid search quickly becomes computationally unfeasible if more than two variables are cointegrated. Therefore, the likelihood function has to be maximized using heuristic approaches. Depending on the precise problem structure the evolutionary approach developed in the present paper for this purpose saves 90 to 99 per cent of the computation time of a grid search.

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Inflation Expectations: Does the Market Beat Professional Forecasts?

Makram El-Shagi

in: The North American Journal of Economics and Finance, No. 3, 2011

Abstract

The present paper compares expected inflation to (econometric) inflation forecasts based on a number of forecasting techniques from the literature using a panel of ten industrialized countries during the period of 1988 to 2007. To capture expected inflation, we develop a recursive filtering algorithm which extracts unexpected inflation from real interest rate data, even in the presence of diverse risks and a potential Mundell-Tobin-effect. The extracted unexpected inflation is compared to the forecasting errors of ten econometric forecasts. Beside the standard AR(p) and ARMA(1,1) models, which are known to perform best on average, we also employ several Phillips curve based approaches, VAR, dynamic factor models and two simple model avering approaches.

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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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Working Papers

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How Forecast Accuracy Depends on Conditioning Assumptions

Carola Engelke Katja Heinisch Christoph Schult

in: IWH Discussion Papers, No. 18, 2019

Abstract

This paper examines the extent to which errors in economic forecasts are driven by initial assumptions that prove to be incorrect ex post. Therefore, we construct a new data set comprising an unbalanced panel of annual forecasts from different institutions forecasting German GDP and the underlying assumptions. We explicitly control for different forecast horizons to proxy the information available at the release date. Over 75% of squared errors of the GDP forecast comove with the squared errors in their underlying assumptions. The root mean squared forecast error for GDP in our regression sample of 1.52% could be reduced to 1.13% by setting all assumption errors to zero. This implies that the accuracy of the assumptions is of great importance and that forecasters should reveal the framework of their assumptions in order to obtain useful policy recommendations based on economic forecasts.

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Power Generation and Structural Change: Quantifying Economic Effects of the Coal Phase-out in Germany

Christoph Schult Katja Heinisch Oliver Holtemöller

in: IWH Discussion Papers, No. 16, 2019

Abstract

In the fight against global warming, the reduction of greenhouse gas emissions is a major objective. In particular, a decrease in electricity generation by coal could contribute to reducing CO2 emissions. Using a multi-region dynamic general equilibrium model, this paper studies potential economic consequences of a coal phase-out in Germany. Different regional phase-out scenarios are simulated with varying timing structures. We find that a politically induced coal phase-out would lead to an increase in the national unemployment rate by about 0.10 percentage points from 2020 to 2040, depending on the specific scenario. The effect on regional unemployment rates varies between 0.18 to 1.07 percentage points in the lignite regions. However, a faster coal phase-out can lead to a faster recovery. The coal phase-out leads to migration from German lignite regions to German non-lignite regions and reduces the labour force in the lignite regions by 10,000 people by 2040.

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(Since When) Are East and West German Business Cycles Synchronised?

Stefan Gießler Katja Heinisch Oliver Holtemöller

in: IWH Discussion Papers, No. 7, 2019

Abstract

This paper analyses whether and since when East and West German business cycles are synchronised. We investigate real GDP, unemployment rates and survey data as business cycle indicators and employ several empirical methods. Overall, we find that the regional business cycles have synchronised over time. GDP-based indicators and survey data show a higher degree of synchronisation than the indicators based on unemployment rates. However, recently synchronisation among East and West German business cycles seems to become weaker, in line with international evidence.

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Progressive Tax-like Effects of Inflation: Fact or Myth? The U.S. Post-war Experience

Matthias Wieschemeyer Bernd Süssmuth

in: IWH Discussion Papers, No. 33, 2017

Abstract

Inflation and earnings growth can push some tax payers into higher brackets in the absence of inflation-indexed schedules. Moreover, inflation may affect the composition of individuals’ income sources. As a result, depending on the relative tax burden of labour and capital, inflation may decrease or increase the difference between before-tax and after-tax income. However, whether some and if so which percentiles of the income distribution net benefit from inflation via taxation is a widely unexplored question. We make use of a novel dataset on U.S. pre-tax and post-tax income distribution series provided by Pike ty et al. (2018) for the years 1962 to 2014 to answer this question. To this end, we estimate local projections to quantify dynamic effects. We find that inflation shocks increase progressivity of taxation not only contemporaneously but also with some repercussion of several years after the shock. While particularly the bottom two quintiles gain in share, it is not the top but the fourth quintile that lastingly loses.

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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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