CDF_CGE模型基本结构及应用

本文建立了基于1997年数据的中国动态金融CGE模型（China’s Dynamic Financial CGE Model，CDF_CGE），文章的内容包括金融社会核算矩阵（Financial Social Accounting Matrix，FSAM）的结构、模型的总体结构以及模型对我国经济2001-2010间的中期预测。     

Bayesian time series analysis of periodic behaviour and spectral structure

Analysis, model selection and forecasting in univariate time series models can be routinely carried out for models in which the model order is relatively small. Under an ARMA assumption, classical estimation, model selection and forecasting can be routinely implemented with the Box–Jenkins time domain representation. However, this approach becomes at best prohibitive and at worst impossible when the model order is high. In particular, the standard assumption of stationarity imposes constraints on the parameter space that are increasingly complex. One solution within the pure AR domain is the latent root factorization in which the characteristic polynomial of the AR model is factorized in the complex domain, and where inference questions of interest and their solution are expressed in terms of the implied (reciprocal) complex roots; by allowing for unit roots, this factorization can identify any sustained periodic components. In this paper, as an alternative to identifying periodic behaviour, we concentrate on frequency domain inference and parameterize the spectrum in terms of the reciprocal roots, and, in addition, incorporate Gegenbauer components. We discuss a Bayesian solution to the various inference problems associated with model selection involving a Markov chain Monte Carlo (MCMC) analysis. One key development presented is a new approach to forecasting that utilizes a Metropolis step to obtain predictions in the time domain even though inference is being carried out in the frequency domain. This approach provides a more complete Bayesian solution to forecasting for ARMA models than the traditional approach that truncates the infinite AR representation, and extends naturally to Gegenbauer ARMA and fractionally differenced models.     

引入土地流转因素的新型农村社会养老保险基金预测

本文以政策外部性关联与协同效应为切入点,通过陕西省西安市新农保试点区县农民问卷调查,对引入土地流转因素的新型农村社会养老保险基金预测进行精算建模和政策仿真研究。结果表明：土地流转因素对新农保参保率、缴费率具有显著影响,基金收支呈现由盈余转向缺口、并逐年扩大的趋势,基金、制度存在双重失衡风险。通过关键制度因素的单一、组合调整结果分析对比,提出促进土地流转与新农保制度良性融合与可持续发展的对策建议。     

颠覆性技术识别与预测方法研究进展

为了把握颠覆性技术识别与预测方法研究进展,首先对颠覆性的内涵、特征和运行机制进行论述;然后,通过对已有文献进行系统梳理,将识别与预测方法分为5类,分别展开详述;最后,对各类方法进行比较分析。研究发现,已有颠覆性技术识别与预测方法仍处于发展的初期阶段,所有识别研究方法均围绕颠覆性技术的外部影响或内在特征中的某一方面展开,并未将二者有机结合起来。未来,应综合把握颠覆性技术内外部特征,构建全面、系统的识别与预测框架。     

中国宏观经济总量的实时预报与短期预测——基于混频数据预测模型的实证研究

季度GDP的走势与波动不仅会影响政府的财政收支、企业的盈利和财务状况,甚至还会影响家庭和个人的收入与支出,是宏观经济总量预报、预测与分析的重中之重。传统的宏观经济总量预测模型是基于同频数据进行的,高频和超高频数据必需处理为低频数据,这不仅忽略了高频数据信息的变化,还影响了模型预报和预测的及时性,降低了模型的预测精度。本文将混合数据抽样模型(MIDAS)用于中国季度GDP的预报和预测,实证研究表明,出口是造成我国金融危机时期经济增长减速的主要因素,MIDAS模型在中国宏观经济总量的短期预测方面具有精确性的比较优势,在实时预报方面具有显著的可行性和时效性。     

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.     

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.