ifo Economic Forecast
The ifo Institute publishes an economic forecast four times per year. The forecast focuses on developments in the German economy for a period of up to two years. In view of the close links between the German economy and the world economy, and particularly the European economy, ifo also forecasts developments in the European Union and other important countries. The forecast is based on an in-depth analysis of the economic situation.
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Economic forecasts are estimates of future economic activity in the overall economy, with a focus on the slowing or speeding up as well as the turning points of economic variables in the course of the business cycle. Economic fluctuations are expressed in terms of the development in real quarterly GDP.
The ifo Institute forecasts real and nominal GDP in Germany in terms of the demand components, because in matters of economic activity it is not an issue of trend growth, which requires a supply-side explanation, but of the variations around the trend. The forecast distinguishes between the demand of private households, the state, investors (equipment and buildings) and other countries (exports) and takes into account that these demand components are also affected in part by imports. Taking into account the sectoral business surveys (ifo KT) the forecast is broken down to the major sectors of the economy (IFOCAST). Every GDP forecast is supplemented by an estimate of primary distribution (employee compensation, corporate and investment income) as well as income redistribution by the state. In addition the government account (revenue, expenditure and net lending/net borrowing) is quantified and the foreseeable development on the labour market (number of employees, number of unemployed) is forecast in conjunction with the expected state of economic activity.
Partial forecasts are presented by using the system of national accounts and converted to half-year or yearly values for publication. In a multi-phased, iterative process the partial forecasts of GDP, the labour market and government accounts are tested for their economic plausibility and synchronized with each other until a computationally coherent forecast with the greatest subjective probability is achieved.
The ifo Institute employs a number of modern methods for forecasting economic activity such as vector autoregressive approaches (VAR models), econometric structural models and trend-cycle decomposition. This ensures that all information efficiently flows into the forecast and that the expert knowledge of the forecaster is adequately incorporated. As a long-standing practice, the ifo Institute regularly evaluates the quality of its own forecasts of economic activity. It comments on important conceptual changes in the system of national accounts, in which accounting system the Ifo economic forecasts are embedded.
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Accurate economic forecasts are desirable, but challenging. Like all forecasts, the ifo Institute's economic forecasts deviate to a certain extent from the official numbers published later. In order to ensure maximum transparency, the ifo Institute's economic forecasts, which have been produced in June and December since 1991, are documented and evaluated on an ongoing basis. The following analysis focuses on the forecast of the annual rate of change of the price-adjusted gross domestic product (GDP) for the current and the coming year.
Since the length of the forecast horizon has a decisive influence on the accuracy of the forecast, in the following the forecasts are considered separately according to the respective forecast horizon. In order to obtain the GDP growth rate for the current year in December, only GDP in the fourth quarter needs to be forecast, since three quarterly GDP values in the current year are already known at this point. For the forecast of the GDP growth rate in the coming year, five quarters have to be forecast at this point: the GDP in the fourth quarter of the current year as well as the four quarters of the coming year. In the June forecast for the current year, three quarters of the current year still need to be forecast, and to obtain the value for the coming year, the forecast horizon is seven quarters.
The following figure shows the forecast error for different forecast horizons in quarters, calculated as the forecast value of the annual rate of change in GDP for a year minus the value for this rate published by the Federal Statistical Office at the beginning of the following year. If the forecast error is positive, the ifo forecasts were too optimistic; if it is negative, they were too pessimistic. The largest errors always were made in forecasts with a longer horizon, when singular and crisis-like events occurred. These include German reunification in the early 1990s, the bursting of the New Economy bubble in the early 2000s, the world financial and euro crises in the years after 2008, and the Covid and energy crisis in the early 2020s.
The average forecast error shows whether the forecaster makes systematic errors. Ideally, the average forecast error should be zero. In such a case, the errors occur purely at random and balance out on average over a longer period of time: the forecasts are unbiased and the forecaster is neither too optimistic nor too pessimistic. Planning based on such unbiased forecasts is thus always correct on average over many years.
The following figure shows that the ifo economic forecasts are unbiased at short forecast horizons. While the average forecast error is actually zero for a forecast horizon of one quarter, it is -0.06 and +0.49 percentage points for horizons of three and five quarters. Statistically, however, these values are not significantly different from zero, as they lie within the respective 95% confidence intervals. As the forecast horizon increases, the ifo business cycle forecasts tend to become too optimistic. This reflects in particular the mostly abrupt economic contractions associated with the crises, which cannot be anticipated a year in advance or even earlier.
In addition to unbiasedness, accuracy is another important criterion in the evaluation of forecasts. Since positive and negative deviations may cancel each other out when calculating the average forecast error, the mean absolute forecast error (MAE) is used to determine the forecast accuracy. Here, the forecast errors are included without a sign, so that the value can be interpreted as the average deviation of the forecast from the actual value (whether upwards or downwards) and thus as a measure of the accuracy.
Obviously, the forecast accuracy depends on the level of information at the time of forecasting. The following figure shows that the MAE is 0.07 percentage points if only one quarter has to be forecast. Thus, the ifo economic forecasts in December deviate on average by 0.07 percentage points from the annual GDP growth rate published by the Federal Statistical Office in the following January. As the number of quarters to be forecast increases, so does the uncertainty. With seven quarters to forecast, the MAE of the ifo economic forecast is 1.72 percentage points.
In order to form a final judgement on the value of the ifo economic forecasts, their quality must be put in relation to another forecast. For this purpose, it is useful to refer to the so-called Consensus forecasts, which were published at the same time as the ifo economic forecasts. Since 1989, Consensus Economics regularly asks about 30 experts from banks, investment trusts and economic research institutes (including the ifo Institute) about their forecast for the annual rate of change of important macroeconomic variables, such as price-adjusted GDP. The consensus forecast is usually understood as the average of the forecast values given by the individual experts. The following figure shows that the ifo economic researchers clearly beat the consensus forecasts for shorter forecast horizons. For example, the MAE of the ifo December forecasts for the current year is only about half as large as that of the consensus forecasts. With three and five quarters to be forecast, the improvement in the MAE is still a good 20 percent and just under 10 percent respectively. Only with a forecast horizon of seven quarters are the consensus forecasts superior. Overall, it can be concluded that the ifo economic forecasts make a measurable contribution to reducing uncertainty about the future development of the economy.
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The Main National Accounts Data (Archive since 1974) (in German)
- Tabellenanhang der ifo Konjunkturprognosen 1974-1979 (PDF)
- Tabellenanhang der ifo Konjunkturprognosen 1980-1989 (PDF)
- Tabellenanhang der ifo Konjunkturprognosen 1990-1999 (PDF)
- Tabellenanhang der ifo Konjunkturprognosen 2000-2009 (PDF)
- Tabellenanhang der ifo Konjunkturprognosen 2010-2019 (PDF)
- Tabellenanhang der ifo Konjunkturprognosen 2020-2023 (PDF)
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Quarterly Components of Real Gross Domestic Product by Expenditure – Archive since Winter 2007 (in German)
Publications
The Forecasting Power of the ifo Business Survey
2023
Journal of Business Cycle Research 19 (1), 43–94
Einblicke in die Methodik der ifo Konjunkturprognose
2021
Unterricht Wirtschaft + Politik Nr. 1 (11), 02-06
ifo Konjunkturumfragen und Konjunkturanalyse: Band II
ifo Institut, München, 2016
ifo Forschungsberichte / 72
IFOCAST: Methods of the Ifo short-term forecast
ifo Institut für Wirtschaftsforschung, München, 2009
in: ifo Schnelldienst, 2009, 62, Nr. 23, 15-28
ifo Konjunkturumfragen und Konjunkturanalyse : ausgewählte methodische Aufsätze aus dem ifo Schnelldienst
ifo Institut für Wirtschaftsforschung, München, 2007
ifo Forschungsberichte / 33
Methods of business cycle forecasting
ifo Institut für Wirtschaftsforschung, München, 2003
in: ifo Schnelldienst, 2003, 56, Nr. 04, 7-23
Eine Analyse der Konjunkturzyklen für die deutschen Bundesländer
ifo Institut, Dresden, 2023
ifo Dresden berichtet, 2023, 30, Nr. 2, 15-21
Forecasting Regional Industrial Production with High-Frequency Electricity Consumption Data
CESifo, Munich, 2022
CESifo Working Paper No. 9917
Gesamtwirtschafliche ifo Kapazitätsauslastungen für die deutschen Bundesländer
ifo Institut, Dresden, 2022
ifo Dresden berichtet, 2022, 29, Nr. 3, 19-25
Economic and Business Cycle Analyses with Electricity Consumption Data
ifo Institut, München, 2022
ifo Forschungsberichte / 129
The Forecasting Power of the ifo Business Survey
CESifo, Munich, 2020
CESifo Working Paper No. 8291
The Macroeconomic Projections of the German Government: A Comparison to an Independent Forecasting Institution
2020
German Economic Review
Forecasting GDP all over the World Using Leading Indicators based on Comprehensive Survey Data
2019
Applied Economics 51(54), 5802–5816
Mit den ifo-Umfragen regionale Konjunktur verstehen
ifo Institut, München, 2019
ifo Schnelldienst, 2019, 72, Nr. 09, 45-49
Risk of recession for the German economy significantly higher
ifo Institut, München, 2019
ifo Schnelldienst, 2019, 72, Nr. 05, 28-31
On the obstruction of production by enterprises in the business cycle: A Compositional Data Analysis
ifo Institut, München, 2019
ifo Schnelldienst, 2019, 72, Nr. 05, 21-27
Forecasting Imports with Information from Abroad
ifo Institute, Munich, 2019
ifo Working Paper No. 294
Electric Motors, Energy Supply and Education: the Quality of Ifo’s Production-Side Short-Term Forecast
ifo Institut, München, 2016
ifo Schnelldienst, 2016, 69, Nr. 12, 58-63
ifo Konjunkturumfragen und Konjunkturanalyse: Band II
ifo Institut, München, 2016
ifo Forschungsberichte / 72
Forecast for the Services Sector in Germany
ifo Institut, München, 2012
ifo Schnelldienst, 2012, 65, Nr. 01, 31-39
Sector-based Forecasts in Manufacturing
ifo Institut, München, 2011
ifo Schnelldienst, 2011, 64, Nr. 22, 27-35
Current Economic Developments in View of the Ifo Economic Traffic Light
ifo Institut, München, 2011
ifo Schnelldienst, 2011, 64, Nr. 22, 36-38
Die ifo Konjunkturuhr: Zirkulare Korrelation mit dem realen Bruttoinlandsprodukt
2011
in: Wirtschafts- und Sozialwissenschaftliches Archiv (WiSoStA) 5 (3), 179-201
Markov-Switching and the Ifo Business Climate: The Ifo Business Cycle Traffic Lights
2010
in: Journal of Business Cycle Measurement and Analysis 7 (2), 1–13
Macroeconomic forecasting with mixed frequencies
ifo Institut für Wirtschaftsforschung, München, 2009
in: ifo Schnelldienst, 2009, 62, Nr. 21, 22-33
Ursachen des Rohölpreisanstiegs seit 2016
ZBW – Leibniz-Informationszentrum Wirtschaft, Hamburg, 2018
Wirtschaftsdienst 96 (8), 605-607
ifo Import Climate – a First Lead Indicator for Forecasting German Imports
ifo Institut, München, 2018
ifo Schnelldienst, 2018, 71, Nr. 12, 27-32
The Influence of Investment Income over the German Current Account Balance
ifo Institut, München, 2017
ifo Schnelldienst, 2017, 70, Nr. 22, 38-44
Measuring Uncertainty with Survey Data
ifo Institut, München, 2017
ifo Schnelldienst, 2017, 70, Nr. 16, 25-29
Measurement of Corporate Uncertainty in Germany – the ifo Dispersion Measure
ifo Institut, München, 2017
ifo Schnelldienst, 2017, 70, Nr. 15, 19-25
The Impact of Changes in Commodity Prices on the Current Account Balance
ifo Institut, München, 2017
ifo Schnelldienst, 2017, 70, Nr. 14, 44-46
Macoeconomic Uncertainty in Germany
ifo Institut, München, 2017
ifo Schnelldienst, 2017, 70, Nr. 06, 41-50
Inflation is Returning! More and More Firms in Germany Plan to Increase Prices
ifo Institut, München, 2017
ifo Schnelldienst, 2017, 70, Nr. 05, 16-21
Zur Prognosegüte der gesamtwirtschaftlichen Stundenproduktivität
ifo Institut, München, 2016
ifo Schnelldienst, 2016, 69, Nr. 22, 57-61
Deutschlands Position in der Weltwirtschaft
2016
Wirtschaftsdienst 96 (11), 806-810
A Flash Estimate of Private Consumption in Germany
ifo Institut, München, 2016
ifo Schnelldienst, 2016, 69, Nr. 21, 36-41
ifo Konjunkturumfragen und Konjunkturanalyse: Band II
ifo Institut, München, 2016
ifo Forschungsberichte / 72
Boosting and Forecasting German Industrial Output: What Does a Closer Look at the Details Tell Us?
ifo Institut, München, 2016
ifo Schnelldienst, 2016, 69, Nr. 03, 30-33
The Impact of Uncertainty on the German and Austrian Business Cycle
2015
Wirtschaftspolitische Blätter 62 (4), 655-667
Inventory Investment as Reflected in Statistics
ifo Institut, München, 2015
ifo Schnelldienst, 2015, 68, Nr. 15, 33-37
ifo Konjunkturampel Revisited
ifo Institut, München, 2015
ifo Schnelldienst, 2015, 68, Nr. 05, 27-32
Forecasting Imports with Information from Abroad
2021
Economic Modelling 98, 109 – 117
The ifo Export Climate – A Leading Indicator to Forecast German Export Growth
ifo Institute, Munich, 2019
CESifo Forum 20 (4), 36-42
Weltweite Prognosen des Bruttoinlandsprodukts mit Hilfe der Indikatoren des ifo World Economic Survey
ifo Institut, München, 2019
ifo Schnelldienst, 2019, 72, Nr. 15, 36-39
Forecasting GDP all over the World Using Leading Indicators based on Comprehensive Survey Data
2019
Applied Economics 51(54), 5802–5816
Experts, firms, consumers or even hard data? Forecasting employment in Germany
2017
Applied Economics Letters 24 (4), 279-283
Boosting and Forecasting German Industrial Output: What Does a Closer Look at the Details Tell Us?
ifo Institut, München, 2016
ifo Schnelldienst, 2016, 69, Nr. 03, 30-33
Looking into the black box of boosting: the case of Germany
2016
Applied Economics Letters 23 (17), 1229-1233
Evaluation of Ifo Economic Forecasts – a Comparison with Forecasts by Consensus Economics
ifo Institut, München, 2015
ifo Schnelldienst, 2015, 68, Nr. 22, 26-28
The Impact of Exchange Rates on German Exports – Simulations with Error Correction Models
ifo Institut, München, 2015
ifo Schnelldienst, 2015, 68, Nr. 20, 35-38
Ifo Export Expectations – a New Indicator on the Export Industry in Germany
ifo Institut, München, 2014
ifo Schnelldienst, 2014, 67, Nr. 23, 64-65
Forecasting business-cycle turning points: the three-times-in-succession rule vs. Markov switching
ifo Institut, München, 2014
ifo Schnelldienst, 2014, 67, Nr. 16, 21-25
On the Introduction of ESA 2010: Impact on Gross Domestic Product
ifo Institut, München, 2014
ifo Schnelldienst, 2014, 67, Nr. 05, 45-48
The Ifo Export Climate – an Early Indicator for the German Export Forecast
ifo Institut, München, 2013
ifo Schnelldienst, 2013, 66, Nr. 04, 36-43
Economic Growth in Germany's Quarterly National Accounts: Previous Year's Price Basis Revisited
ifo Institut, München, 2013
ifo Schnelldienst, 2013, 66, Nr. 03, 29-36
Economic Forecasts Today– Possibilities and Problems
ifo Institut, München, 2013
ifo Schnelldienst, 2013, 66, Nr. 01, 25-32
Forecasting Imports with Information from Abroad
2021
Economic Modelling 98, 109 – 117
Regional business cycles in Germany – convergence
ifo Institut für Wirtschaftsforschung, München, 2009
in: ifo Schnelldienst, 2009, 62, Nr. 15, 23-32
Regional business cycles in Germany - the dating problem
ifo Institut für Wirtschaftsforschung, München, 2009
in: ifo Schnelldienst, 2009, 62, Nr. 14, 24-31
Regional business cycles in Germany - Part 1: The data situation
ifo Institut für Wirtschaftsforschung, München, 2009
in: ifo Schnelldienst, 2009, 62, Nr. 13, 18-24
The Virtues of VAR Forecast Pooling – A DSGE Model Based Monte Carlo Study
ifo Institut für Wirtschaftsforschung, München, 2009
Ifo Working Paper No. 65
Do we need a moratorium on forecasts?
ifo Institut für Wirtschaftsforschung, München, 2008
ifo Schnelldienst, 2008, 61, Nr. 24, 83
Forecasting Euro Area Real GDP: Optimal Pooling of Information
CESifo, Munich, 2008
CESifo Working Paper No. 2371
What is a recession?
ifo Institut für Wirtschaftsforschung, München, 2008
ifo Schnelldienst, 2008, 61, Nr. 14, 44-45
Price-adjusted GDP and the practice followed by the Institutes in the Joint Economic Analysis
ifo Institut für Wirtschaftsforschung, München, 2008
ifo Schnelldienst, 2008, 61, Nr. 09, 15-18
VAR Model Averaging for Multi-Step Forecasting
ifo Institut für Wirtschaftsforschung, München, 2007
Ifo Working Paper No. 48
Medium-term inflation forecast: The dilemma of the two pillar strategy of the European Central Bank
ifo Institut für Wirtschaftsforschung, München, 2007
ifo Schnelldienst, 2007, 60, Nr. 11, 16-24
Methods of economic forecasting and business cycle-indicators
Elgar, Cheltenham, 2007
in: Goldrian, Georg: Handbook of survey-based business cycle analysis, 2007, S.117-142
ifo Konjunkturumfragen und Konjunkturanalyse : ausgewählte methodische Aufsätze aus dem ifo Schnelldienst
ifo Institut für Wirtschaftsforschung, München, 2007
ifo Forschungsberichte / 33
Assessing the Forecast Properties of the CESifo World Economic Climate Indicator: Evidence for the Euro Area
ifo Institut für Wirtschaftsforschung, München, 2007
Ifo Working Paper No. 46
On the evaluation of VAR forecasts
ifo Institut für Wirtschaftsforschung, München, 2007
ifo Schnelldienst, 2007, 60, Nr. 07, 19-25
Log versus level in VAR forecasting: 16 Million empirical answers - expect the unexpected
ifo Institut für Wirtschaftsforschung, München, 2007
Ifo Working Paper No. 42
Methoden der Wirtschaftsprognose und Konjunkturindikatoren : Aussagekraft der Befragungsergebnisse
ifo Institut, München, 2004
in: Goldrian, Georg (Hrsg.): Handbuch der umfragebasierten Konjunkturforschung, 2004, S.273-301
Previous Year’s Prices: Aggregation and Growth Contributions
ifo Institut, München, 2016
ifo Schnelldienst, 2016, 69, Nr. 11, 39-45
Previous year price basis: Aggregation and linked difference
ifo Institut für Wirtschaftsforschung, München, 2007
ifo Schnelldienst, 2007, 60, Nr. 06, 29-33
Aggregate production and price level: Volume calculations in comparison
ifo Institut für Wirtschaftsforschung, München, 2006
ifo Schnelldienst, 2006, 59, Nr. 14, 13-18
Previous-year price basis: Calculating rules for aggregation
ifo Institut für Wirtschaftsforschung, München, 2005
ifo Schnelldienst, 2005, 58, Nr. 22, 12-16
Previous year price basis and chain linking in the National Accounts: the most important features of the new volume calculation
ifo Institut für Wirtschaftsforschung, München, 2005
ifo Schnelldienst, 2005, 58, Nr. 15, 29-35
The introduction of the previous-year price basis in German National Accounts: Consequences for business-cycle analysis
ifo Institut für Wirtschaftsforschung, München, 2005
in: ifo Schnelldienst, 2005, 58, Nr. 05, 19-27
Zur Einführung der Vorjahrespreisbasis in der deutschen Statistik: Besonderheiten der Quartalsrechnung
ifo Institut für Wirtschaftsforschung, München, 2004
in: ifo Schnelldienst, 2004, 57, Nr. 15, 14-21
Economic growth in the system of national accounts: the introduction of the previous-year price base in the German statistics
ifo Institut für Wirtschaftsforschung, München, 2004
in: ifo Schnelldienst, 2004, 57, Nr. 05, 28-34
Economic growth in national accounting: a comparison between Germany and the U.S.
ifo Institut für Wirtschaftsforschung, München, 2001
in: ifo Schnelldienst, 2001, 54, Nr. 03, 41-51
Real income in the new European System of Accounts
ifo Institut für Wirtschaftsforschung, München, 2000
in: ifo Schnelldienst, 2000, 53, Nr. 04, 07-13
Aus dem Instrumentenkasten der Konjunkturanalyse : Veränderungen im Vergleich
ifo Institut für Wirtschaftsforschung, München, 1999
in: ifo Schnelldienst, 1999, 52, Nr. 27, 11-19
Economic Business Cycle 2019: Forecast and Reality
ifo Institut, München, 2020
ifo Schnelldienst, 2020, 73, Nr. 01, 51-57
Business Cycle 2018: Forecast and Reality
ifo Institut, München, 2019
ifo Schnelldienst, 2019, 72, Nr. 03, 22-29
Economic Situation 2017: Forecast and Reality
ifo Institut, München, 2018
ifo Schnelldienst, 2018, 71, Nr. 03, 35-42
Economic Activity in 2016: Forecast and Reality
ifo Institut, München, 2017
ifo Schnelldienst, 2017, 70, Nr. 02, 72-78
Business Cycle 2015: Forecast and Reality
ifo Institut, München, 2016
ifo Schnelldienst, 2016, 69, Nr. 03, 34-40
Evaluation of Ifo Economic Forecasts – a Comparison with Forecasts by Consensus Economics
ifo Institut, München, 2015
ifo Schnelldienst, 2015, 68, Nr. 22, 26-28
Economic Situation in 2014: Forecast and Reality
ifo Institut, München, 2015
ifo Schnelldienst, 2015, 68, Nr. 02, 43-49
Evaluation der ifo Konjunkturprognosen
ifo Institut, München, 2014
ifo Schnelldienst, 2014, 67, Nr. 17, 43-45
Predicting the German Economy: Headline Survey Indices under Test
2021
Journal of Business Cycle Research 17 (2), 215–232
On the Plausibility of Consumption Forecasts by Means of Input-Output Accounting
ifo Institut, München, 2021
ifo Schnelldienst, 2021, 74, Nr. 04, 54-56
Forecasting Imports with Information from Abroad
ifo Institute, Munich, 2019
ifo Working Paper No. 294
Business Cycle 2013: Forecast and Reality
ifo Institut, München, 2014
ifo Schnelldienst, 2014, 67, Nr. 02, 41-46
Economic activity 2012: Forecasts and Reality
ifo Institut, München, 2013
ifo Schnelldienst, 2013, 66, Nr. 02, 30-33
Economic Situation 2011: Forecast and Reality
ifo Institut, München, 2012
ifo Schnelldienst, 2012, 65, Nr. 02, 22-27
Economic activity in 2010: Forecasts and reality
ifo Institut für Wirtschaftsforschung, München, 2011
ifo Schnelldienst, 2011, 64, Nr. 02, 22-25
Economy activity 2009: forecasts and reality
ifo Institut für Wirtschaftsforschung, München, 2010
ifo Schnelldienst, 2010, 63, Nr. 02, 30-33
Economic activity 2008: Forecasting and reality
ifo Institut für Wirtschaftsforschung, München, 2009
ifo Schnelldienst, 2009, 62, Nr. 03, 21-25
Economic activity in 2007: forecasts and reality
ifo Institut für Wirtschaftsforschung, München, 2008
ifo Schnelldienst, 2008, 61, Nr. 03, 21-26
Economy economic activity 2006: Forecasting and reality
ifo Institut für Wirtschaftsforschung, München, 2007
ifo Schnelldienst, 2007, 60, Nr. 02, 23-28
Economic activity 2005: forecasting and reality
ifo Institut für Wirtschaftsforschung, München, 2006
ifo Schnelldienst, 2006, 59, Nr. 02, 37-43
Economic activity 2004: Forecasting and reality
ifo Institut für Wirtschaftsforschung, München, 2005
in: ifo Schnelldienst, 2005, 58, Nr. 03, 26-30
Economy economic activity 2003: Forecasting and reality
ifo Institut für Wirtschaftsforschung, München, 2004
in: ifo Schnelldienst, 2003, 57, Nr. 03, 26-29
Economy economic activity 2002: Forecasting and reality
ifo Institut für Wirtschaftsforschung, München, 2003
in: ifo Schnelldienst, 2003, 56, Nr. 02, 20-23
German economy 2001: Forecast and reality
ifo Institut für Wirtschaftsforschung, München, 2002
in: ifo Schnelldienst, 2002, 55, Nr. 02, 32-34
Business forecasts and forecasting risks
ifo Institut für Wirtschaftsforschung, München, 2001
in: ifo Schnelldienst, 2001, 54, Nr. 16, 17-21