Official fiscal forecasts in EU member states under the European Semester and Fiscal Compact – An empirical assessment

The efficacy of official forecasts in the EU has been under the spotlight since the introduction of the euro, with biases widely reported prior to the 2008–12 financial and sovereign bond market crisis. Changes to the EU fiscal rules and procedures, in the form of the European Semester and Fiscal Compact, in the early 2010s were adopted to improve forecasting, including through providing a role for independent fiscal institutions. Using data for 22 countries between 2013 and 2019, this paper shows that, despite these changes, biases, of a pessimistic form, remain in forecasts of budget balance and output variables in Stability and Convergence Programmes and the European Commission's Spring Forecasts. Econometric analysis indicates forecast errors in both the headline budget balance and the structural budget balance being explained by forecast errors in output variables and by EU fiscal rule requirements. Member states under an excessive deficit procedure provide optimistic headline budget balance forecasts compared to non-EDP countries, while those that have not met their medium-term objective report smaller forecast errors for the structural budget balance. Independent fiscal institutions are linked to a smaller bias to forecasts of the structural budget balance but have no effect on the forecast errors of the headline budget balance.     

Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?

Structural breaks have been suggested by several economists as a possible explanation for the Meese–Rogoff puzzle, in the sense that an exchange rate model can outperform the random walk in terms of the out-of-sample forecasting error if the period under investigation is free of structural breaks. The results indicate that structural breaks cannot explain the inability of the flexible price monetary model to outperform the random walk. The only plausible explanation for the Meese–Rogoff puzzle is that forecasting accuracy is traditionally assessed by magnitude-only measures. When forecasting accuracy is assessed by alternative measures that do not rely exclusively on the magnitude of error, the monetary model can outperform the random walk regardless of the presence or otherwise of structural breaks.     

Modelling risk premia in CO2 allowances spot and futures prices

This article analyzes the modelling of risk premia in CO₂ allowances spot and futures prices, valid for compliance under the EU Emissions Trading Scheme (EU ETS). Similarly to electricity markets, a salient characteristic of CO₂ allowances is that the theory of storage does not hold, as CO₂ allowances only exist on the balance sheets of companies regulated by the scheme. The main result features positive time-varying risk premia in CO₂ spot and futures prices, which are strictly higher for post-2012 contracts (€6–9/ton of CO₂) than for Phase II contracts (€0–6/ton of CO₂). Contrary to Benth et al.'s (2008) for electricity markets, a positive relationship between risk premia and time-to-maturity is found in the EU ETS. As for relative differences between CO₂ futures and spot prices, CO₂ futures traded between + 1% (December 2008 contract) and + 33% (December 2014 contract) above spot prices during February 2008–April 2009. Contrary to Bessembinder and Lemmon (2002) for the electricity market, a positive relationship between risk premia and the variance/skewness of CO₂ spot prices is found. The futures-spot bias to the EU ETS explains around 1–6% of the variance of CO₂ futures premia.     

声誉如何影响证券分析师的预测行为？

声誉被认为是抑制分析师机会主义行为的有效机制.本文选取2003-2012年分析师盈余预测和新财富最佳分析师榜单数据,采用倍差法研究明星分析师上榜前后预测特征变化.实证结果表明,虽然明星分析师上榜前的预测准确性和一贯性均优于非明星分析师,但是上榜后明星分析师的这两个特征却低于非明星分析师.研究结论说明我国的明星分析师上榜后研究质量下滑.本文对进一步完善最佳分析师评选机制提供了经验证据和理论启示.     

The information content of regional employment data for forecasting aggregate conditions

We consider whether disaggregated data enhance the efficiency of aggregate employment forecasts. We find that incorporating spatial interaction into a disaggregated forecasting model lowers the out-of-sample mean squared error from a univariate aggregate model by 70% at a two-year horizon.     

Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method

Product life cycle (PLC) prediction plays a crucial role in strategic planning and policy definition for high-technology products. Forecast methodologies which can predict PLCs accurately can help to achieve successful strategic decision-making, forecasting, and foresight activities in high-technology firms, research institutes, governments, and universities. Over the past few decades, even though analytic framework strategies have been proposed for production, marketing, R&D (research and development), and finance, aiming at each stage of PLCs, forecast methodologies with which to predict PLCs are few. The purpose of this research is to develop a novel forecast methodology to allow for predictions of product life time (PLT) and the annual shipment of products during the entire PLC of multiple generation products. A novel two-stage fuzzy piecewise regression analysis method is proposed in this paper. In the first stage, the product life-time of the specific generation to be analyzed will be predicted by the fuzzy piecewise regression line that is derived based upon the product life-time of earlier generations. In the second stage of the forecast methodology, the annual shipment of products of the specified generation will be predicted by deriving annual fuzzy regression lines for each generation, based upon the historical data on the earlier generations' products. An empirical study predicting the life-time and the annual shipment of the 16 Mb (Mega bit) DRAM (Dynamic Random Access Memory) PLC is illustrated to validate the analytical process. The results demonstrate that two-stage fuzzy piecewise regression analysis can predict multiple generation PLT and PLC precisely, thereby serving as a foundation for future strategic planning, policy definitions and foresights.     

Equilibrium-correction vs. differencing in macroeconometric forecasting

Recent work by Clements and Hendry elucidate why forecasting systems that are in terms of differences, dVARs, can be more accurate than econometric models that include levels variables, EqCMs. For example, dVAR forecasts are in some cases insulated from parameter non-constancies in the long run mean of the cointegration relationships. In this paper, the practical relevance of these issues are investigated for RIMINI, the quarterly macroeconometric model used in Norges Bank (Central Bank of Norway), an example of an EqCM forecasting model. We develop two dVAR versions of the full RIMINI model and compare EqCM and dVAR forecasts for the period 1992.1–1994.4. We also include forecasts from univariate dVAR type models. The results seem to confirm the relevance of the theoretical results. First, dVAR forecasts appear to provide some immunity against parameter non-constancies that could seriously bias the EqCM forecasts. Second, the misspecification resulting from omitting levels information generates substantial biases in the dVAR forecasts 8 and 12 quarters ahead.     

Where have all the flowers gone? Forecasting green trends in the automobile industry with a choice-based conjoint adoption model

With rising gas prices, global warming, and green thinking, all-electric vehicles are currently considered the automobile technology of the future. However, besides their advantages electric drive trains also exhibit several disadvantages. Moreover, history shows several failed attempts to establish electric vehicles. Thus, a reliable forecasting model is needed that predicts if the current trend is sustainable. We develop and empirically test a choice-based conjoint adoption model that uses individual-level preferences as a basis for prediction. Predictions are mapped to the time of the next planned purchase in order to establish the adoption process. The model extends existing research in several ways. First, no prior information, e.g., historical market data or a functional form of the adoption process, has to be integrated. Second, the model allows dynamic modifications of product specifications or competition at different points in time. Third, a no-choice option can be integrated so that a technology switch is not forced by the model itself and switching costs can be considered. The empirical results reveal different critical factors for the adoption of all-electric vehicles, such as purchase price, range, timing of the market entry, or environmental evolution, which could lead to a solid base of consumers preferring this option.