Density forecasting through disaggregation

In this paper, the revised expectations model (REM) is developed to incorporate economic agents’ price expectation formation effects. With this incorporation, two models, an aggregate one sector model and a disaggregated multi-sector model, are estimated and used in density forecasting of the US real GDP growth rate. The experiment shows that use of the disaggregated version of the model, which incorporates price expectation effects along with modern Bayesian MCMC estimation and prediction techniques, produces more precise density forecasts than those yielded by either an aggregate version or benchmark forecasting models.     

A new structural break model,with an application to Canadian inflation forecasting

This paper develops an efficient approach to modelling and forecasting time series data with an unknown number of change-points. Using a conjugate prior and conditioning on time-invariant parameters, the predictive density and the posterior distribution of the change-points have closed forms. Furthermore, the conjugate prior is modeled as hierarchical in order to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients, or both. The regime duration can be modelled as a Poisson distribution. A new, efficient Markov chain Monte Carlo sampler draws the parameters from the posterior distribution as one block. An application to a Canadian inflation series shows the gains in forecasting precision that our model provides.     

Methods for Default and Robust Bayesian Model Comparison: the Fractional Bayes Factor Approach

In the Bayesian approach to model selection and hypothesis testing, the Bayes factor plays a central role. However, the Bayes factor is very sensitive to prior distributions of parameters. This is a problem especially in the presence of weak prior information on the parameters of the models. The most radical consequence of this fact is that the Bayes factor is undetermined when improper priors are used. Nonetheless, extending the non-informative approach of Bayesian analysis to model selection/testing procedures is important both from a theoretical and an applied viewpoint. The need to develop automatic and robust methods for model comparison has led to the introduction of several alternative Bayes factors. In this paper we review one of these methods: the fractional Bayes factor (O'Hagan, 1995). We discuss general properties of the method, such as consistency and coherence. Furthermore, in addition to the original, essentially asymptotic justifications of the fractional Bayes factor, we provide further finite-sample motivations for its use. Connections and comparisons to other automatic methods are discussed and several issues of robustness with respect to priors and data are considered. Finally, we focus on some open problems in the fractional Bayes factor approach, and outline some possible answers and directions for future research.     

Comparing the accuracy of multivariate density forecasts in selected regions of the copula support

This paper develops a testing framework for comparing the predictive accuracy of competing multivariate density forecasts with different predictive copulas, focusing on specific parts of the copula support. The tests are framed in the context of the Kullback–Leibler Information Criterion, using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on the region of interest. Monte Carlo simulations document that the resulting test statistics have satisfactory size and power properties for realistic sample sizes. In an empirical application to daily changes of yields on government bonds of the G7 countries we obtain insights into why the Student-t and Clayton mixture copula outperforms the other copulas considered; mixing in the Clayton copula with the t-copula is of particular importance to obtain high forecast accuracy in periods of jointly falling yields.     

非平稳条件下的市场可预测性问题研究

本文研究了非平稳对线性、非线性可预测性检验的影响,从非平稳角度解释了"非线性可预测性难题"。在此基础上对传统的Box-Pierce-Ljung线性检验法和广义谱密度非线性检验法进行了修正,并运用严格的理论证明和蒙特卡罗模拟证实了它们在大样本和有限样本情况下的非平稳稳健性。最后,运用修正的检验方法对我国第一只股指期货标的沪深300统一指数的可预测性进行了分析。本文结论对于我国金融市场实证研究具有重要的指导意义,也反映了我国新兴金融市场的独有特性以及成熟统计方法在我国的实用性。     

A comparison of financial duration models via density forecasts

Using density forecast evaluation techniques, we compare the predictive performance of econometric specifications that have been developed for modeling duration processes in intra-day financial markets. The model portfolio encompasses various variants of the Autoregressive Conditional Duration (ACD) model and recently proposed dynamic factor models. The evaluation is conducted on time series of trade, price and volume durations computed from transaction data of NYSE listed stocks. The results show that simpler approaches perform at least as well as more complex methods. With respect to modeling trade duration processes, standard ACD models successfully account for duration dynamics while none of the models provides an acceptable specification for the conditional duration distribution. We find that the Logarithmic ACD, if based on a flexible innovation distribution, provides a quite robust and useful framework for the modeling of price and volume duration processes.     

负荷预测准确度对电厂经济效益的影响

电力系统的高效运行与负荷预测的准确度密切相关,本文首先分析了电厂经济效益与负荷预测准确度是如何影响电厂的经济效益的,然后列举了负荷预测的相关影响因素,最后总结了通过改善预测方法和提高调度人员的技术水平来增加电厂的经济效益.     

Does the Phillips curve help to forecast euro area inflation?

We find that it does, but choosing the right specification is not trivial. Based on an extensive forecast evaluation we document notable forecast instabilities for most simple Phillips curves. Euro area inflation was particularly hard to forecast in the run-up to the Economic and Monetary Union and after the sovereign debt crisis, when the trends—and, for the latter period, also the amount of slack—were harder to pin down. Yet, some specifications outperform a univariate benchmark and point to the following lessons: (i) the key type of time variation to consider is an inflation trend; (ii) a simple filter-based output gap works well, but after the Great Recession it is outperformed by endogenously estimated slack or by “institutional” estimates; (iii) external variables do not bring forecast gains; (iv) newer-generation Phillips curve models with several time-varying features are a promising avenue for forecasting; and (v) averaging over a wide range of modelling choices helps.     

Copula-based multivariate GARCH model with uncorrelated dependent errors

Multivariate GARCH (MGARCH) models are usually estimated under multivariate normality. In this paper, for non-elliptically distributed financial returns, we propose copula-based multivariate GARCH (C-MGARCH) model with uncorrelated dependent errors, which are generated through a linear combination of dependent random variables. The dependence structure is controlled by a copula function. Our new C-MGARCH model nests a conventional MGARCH model as a special case. The aim of this paper is to model MGARCH for non-normal multivariate distributions using copulas. We model the conditional correlation (by MGARCH) and the remaining dependence (by a copula) separately and simultaneously. We apply this idea to three MGARCH models, namely, the dynamic conditional correlation (DCC) model of Engle [Engle, R.F., 2002. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics 20, 339–350], the varying correlation (VC) model of Tse and Tsui [Tse, Y.K., Tsui, A.K., 2002. A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20, 351–362], and the BEKK model of Engle and Kroner [Engle, R.F., Kroner, K.F., 1995. Multivariate simultaneous generalized ARCH. Econometric Theory 11, 122–150]. Empirical analysis with three foreign exchange rates indicates that the C-MGARCH models outperform DCC, VC, and BEKK in terms of in-sample model selection and out-of-sample multivariate density forecast, and in terms of these criteria the choice of copula functions is more important than the choice of the volatility models.     

Bayesian model selection using encompassing priors

This paper deals with Bayesian selection of models that can be specified using inequality constraints among the model parameters. The concept of encompassing priors is introduced, that is, a prior distribution for an unconstrained model from which the prior distributions of the constrained models can be derived. It is shown that the Bayes factor for the encompassing and a constrained model has a very nice interpretation: it is the ratio of the proportion of the prior and posterior distribution of the encompassing model in agreement with the constrained model. It is also shown that, for a specific class of models, selection based on encompassing priors will render a virtually objective selection procedure. The paper concludes with three illustrative examples: an analysis of variance with ordered means; a contingency table analysis with ordered odds-ratios; and a multilevel model with ordered slopes.     

基于广义交叉谱密度方法的线性和非线性格兰杰因果关系检验

鉴于现行的均值格兰杰因果关系检验或者无法检验非线性的格兰杰因果关系,或者存在“维数灾难”问题,我们利用Chung 和 Hong (2007) 的广义交叉谱方法提出了一个能统一检验线性和非线性均值格兰杰因果关系的检验统计量。我们的广义交叉谱检验统计量渐近服从一个标准正态分布,它不但能考虑所有滞后阶的信息,而且避免了“维数灾难”问题。蒙特卡罗试验结果表明广义交叉谱检验具有良好的有限样本表现。     

Benchmark priors for Bayesian model averaging

In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, ‘diffuse’ priors on model-specific parameters can lead to quite unexpected consequences. Here we focus on the practically relevant situation where we need to entertain a (large) number of sampling models and we have (or wish to use) little or no subjective prior information. We aim at providing an ‘automatic’ or ‘benchmark’ prior structure that can be used in such cases. We focus on the normal linear regression model with uncertainty in the choice of regressors. We propose a partly non-informative prior structure related to a natural conjugate g-prior specification, where the amount of subjective information requested from the user is limited to the choice of a single scalar hyperparameter g_0j. The consequences of different choices for g_0j are examined. We investigate theoretical properties, such as consistency of the implied Bayesian procedure. Links with classical information criteria are provided. More importantly, we examine the finite sample implications of several choices of g_0j in a simulation study. The use of the MC³ algorithm of Madigan and York (Int. Stat. Rev. 63 (1995) 215), combined with efficient coding in Fortran, makes it feasible to conduct large simulations. In addition to posterior criteria, we shall also compare the predictive performance of different priors. A classic example concerning the economics of crime will also be provided and contrasted with results in the literature. The main findings of the paper will lead us to propose a ‘benchmark’ prior specification in a linear regression context with model uncertainty.     

Forecasting macroeconomic risks

We construct risks around consensus forecasts of real GDP growth, unemployment, and inflation. We find that risks are time-varying, asymmetric, and partly predictable. Tight financial conditions forecast downside growth risk, upside unemployment risk, and increased uncertainty around the inflation forecast. Growth vulnerability arises as the conditional mean and conditional variance of GDP growth are negatively correlated: downside risks are driven by lower mean and higher variance when financial conditions tighten. Similarly, employment vulnerability arises as the conditional mean and conditional variance of unemployment are positively correlated, with tighter financial conditions corresponding to higher forecasted unemployment and higher variance around the consensus forecast.