Real-Time Forecasts of the Real Price of Oil

We construct a monthly real-time dataset consisting of vintages for 1991.1–2010.12 that is suitable for generating forecasts of the real price of oil from a variety of models. We document that revisions of the data typically represent news, and we introduce backcasting and nowcasting techniques to fill gaps in the real-time data. We show that real-time forecasts of the real price of oil can be more accurate than the no-change forecast at horizons up to 1 year. In some cases, real-time mean squared prediction error (MSPE) reductions may be as high as 25% 1 month ahead and 24% 3 months ahead. This result is in striking contrast to related results in the literature for asset prices. In particular, recursive vector autoregressive (VAR) forecasts based on global oil market variables tend to have lower MSPE at short horizons than forecasts based on oil futures prices, forecasts based on autoregressive (AR) and autoregressive moving average (ARMA) models, and the no-change forecast. In addition, these VAR models have consistently higher directional accuracy.     

Robust estimation of extremes

We consider the problem of making inferences about extreme values from a sample. The underlying model distribution is the generalized extreme-value (GEV) distribution, and our interest is in estimating the parameters and quantiles of the distribution robustly. In doing this we find estimates for the GEV parameters based on that part of the data which is well fitted by a GEV distribution. The robust procedure will assign weights between 0 and 1 to each data point. A weight near 0 indicates that the data point is not well modelled by the GEV distribution which fits the points with weights at or near 1. On the basis of these weights we are able to assess the validity of a GEV model for our data. It is important that the observations with low weights be carefully assessed to determine whether diey are valid observations or not. If they are, we must examine whether our data could be generated by a mixture of GEV distributions or whether some other process is involved in generating the data. This process will require careful consideration of die subject matter area which led to the data. The robust estimation techniques are based on optimal B-robust estimates. Their performance is compared to the probability-weighted moment estimates of Hosking et al. (1985) in both simulated and real data.     

Index to Articles,by Author,Volume 2 (1984)

This article investigates the statistical properties of the U.S. sacrifice ratio—the cumulative output loss arising from a permanent reduction in inflation. We derive estimates of the sacrifice ratio from three structural vector autoregression models and then conduct a series of simulation exercises to analyze their sampling distribution. We obtain point estimates of the sacrifice ratio that are consistent with results reported in earlier studies. However, the estimates are very imprecise, which we suggest reflects the poor quality of instruments used in estimation. We conclude that the estimates provide a very unreliable guide for assessing the output cost of disinflation policy.     

Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations

Summary.  As a part of the EUREDIT project new methods to detect multivariate outliers in incomplete survey data have been developed. These methods are the first to work with sampling weights and to be able to cope with missing values. Two of these methods are presented here. The epidemic algorithm simulates the propagation of a disease through a population and uses extreme infection times to find outlying observations. Transformed rank correlations are robust estimates of the centre and the scatter of the data. They use a geometric transformation that is based on the rank correlation matrix. The estimates are used to define a Mahalanobis distance that reveals outliers. The two methods are applied to a small data set and to one of the evaluation data sets of the EUREDIT project.     

Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty

A popular account for the demise of the U.K.’s monetary targeting regime in the 1980s blames the fluctuating predictive relationships between broad money and inflation and real output growth. Yet ex post policy analysis based on heavily revised data suggests no fluctuations in the predictive content of money. In this paper, we investigate the predictive relationships for inflation and output growth using both real-time and heavily revised data. We consider a large set of recursively estimated vector autoregressive (VAR) and vector error correction models (VECM). These models differ in terms of lag length and the number of cointegrating relationships. We use Bayesian model averaging (BMA) to demonstrate that real-time monetary policymakers faced considerable model uncertainty. The in-sample predictive content of money fluctuated during the 1980s as a result of data revisions in the presence of model uncertainty. This feature is only apparent with real-time data as heavily revised data obscure these fluctuations. Out-of-sample predictive evaluations rarely suggest that money matters for either inflation or real output. We conclude that both data revisions and model uncertainty contributed to the demise of the U.K.’s monetary targeting regime.     

Perceived Inflation Persistence

This article constructs and estimates a measure called perceived inflation persistence that can be used to determine if professional forecasters’ inflation forecasts indicate there has been a change in inflation persistence. This measure is built via the implied autocorrelation function that follows from the estimates obtained using a forecaster-specific state-space model. Findings indicate that U.S. perceived inflation persistence has changed since the mid-1990s with more consensus among forecasters at lower levels of persistence. When compared to the autocorrelation function for actual inflation, forecasters typically react less to shocks to inflation than the actual inflation data would suggest.     

中国通货膨胀、通货膨胀波动与产出增长：基于MGARCH模型分析

通货膨胀、通货膨胀波动和产出增长及其波动之间存在复杂的影响关系。基于一种多元自回归条件异方差模型（MGARCH模型）,采用中国1993—2003年的月度通货膨胀率和产出增长数据检验通货膨胀、通货膨胀波动和产出增长及其波动的关系。结论表明：高通货膨胀水平引起高的通货膨胀波动,而高通货膨胀率和通货膨胀波动导致低的产出增长和产出增长波动,结论的政策含义是价格稳定的货币政策有利于经济健康发展。     

我国通货膨胀率的最优目标区间几何？

 内容提要：本文首先提出了面板数据动态门限回归模型的二阶段合并最小二乘（2SPOLS）估计方法;其次,基于中国29省市自治区1978-2008年的面板数据,对中国通货膨胀和经济增长之间关系的实证分析发现,在一定程度上,我国通货膨胀率对经济增长率的作用存在两个门限值的“双门限效应”,其门限值分别为3.2%和15.7%。所以,通货膨胀率位于（0%,3.2%]时,温和通货膨胀对经济增长率存在“托宾效应”。通货膨胀率超过3.2%时,通货膨胀率对经济增长率存在阻碍经济增长的“反托宾效应”,尤其,通货膨胀率高于15.7%后,恶性通货膨胀严重阻碍经济“软扩张”。因此,我国通货膨胀率的最优目标区间是（0%,3.2%]。     

A two-stage procedure to pool information across quantile levels in linear quantile regression

In linear quantile regression, the regression coefficients for different quantiles are typically estimated separately. Efforts to improve the efficiency of estimators are often based on assumptions of commonality among the slope coefficients. We propose instead a two-stage procedure whereby the regression coefficients are first estimated separately and then smoothed over quantile level. Due to the strong correlation between coefficient estimates at nearby quantile levels, existing bandwidth selectors will pick bandwidths that are too small. To remedy this, we use 10-fold cross-validation to determine a common bandwidth inflation factor for smoothing the intercept as well as slope estimates. Simulation results suggest that the proposed method is effective in pooling information across quantile levels, resulting in estimates that are typically more efficient than the separately obtained estimates and the interquantile shrinkage estimates derived using a fused penalty function. The usefulness of the proposed method is demonstrated in a real data example.     

Macroeconomic Uncertainty Through the Lens of Professional Forecasters

ABSTRACT

We analyze the evolution of macroeconomic uncertainty in the United States, based on the forecast errors of consensus survey forecasts of various economic indicators. Comprehensive information contained in the survey forecasts enables us to capture a real-time measure of uncertainty surrounding subjective forecasts in a simple framework. We jointly model and estimate macroeconomic (common) and indicator-specific uncertainties of four indicators, using a factor stochastic volatility model. Our macroeconomic uncertainty estimates have three major spikes has three major spikes aligned with the 1973–1975, 1980, and 2007–2009 recessions, while other recessions were characterized by increases in indicator-specific uncertainties. We also show that the selection of data vintages affects the estimates and relative size of jumps in estimated uncertainty series. Finally, our macroeconomic uncertainty has a persistent negative impact on real economic activity, rather than producing “wait-and-see” dynamics.     

Estimation and prediction of the Kumaraswamy distribution based on record values and inter-record times

ABSTRACT

The maximum likelihood and Bayesian approaches for estimating the parameters and the prediction of future record values for the Kumaraswamy distribution has been considered when the lower record values along with the number of observations following the record values (inter-record-times) have been observed. The Bayes estimates are obtained based on a joint bivariate prior for the shape parameters. In this case, Bayes estimates of the parameters have been developed by using Lindley's approximation and the Markov Chain Monte Carlo (MCMC) method due to the lack of explicit forms under the squared error and the linear-exponential loss functions. The MCMC method has been also used to construct the highest posterior density credible intervals. The Bayes and the maximum likelihood estimates are compared by using the estimated risk through Monte Carlo simulations. We further consider the non-Bayesian and Bayesian prediction for future lower record values arising from the Kumaraswamy distribution based on record values with their corresponding inter-record times and only record values. The comparison of the derived predictors are carried out by using Monte Carlo simulations. Real data are analysed for an illustration of the findings.     

货币和产出缺口能给通货膨胀提供有用的信息吗？

 ]论文目的是确定货币、产出缺口和国际商品价格指数与我国通货膨胀之间是否有长期的关系,能否给我国通货膨胀预测提供除通货膨胀自身所具有的以外的信息。论文的研究方法主要是自回归分布滞后模型(ARDL),基于自回归分布滞后模型进行通货膨胀与相关经济变量的长期关系检验,并进行预报实验,将预测结果与仅包括通货膨胀自身滞后因子的自回归模型的预测结果比较。实证结果显示：货币供给m2、m1、m0都与通货膨胀有显著的长期关系,但是只有m0含有通货膨胀自身没含有的信息,能给通货膨胀提供额外的信息;产出缺口、国际农产品价格指数分别与通货膨胀有长期的关系,含有通货膨胀自身没含有的信息,能给通货膨胀预测提供额外的信息。最后还提出一些相关建议。     

经济周期中产出波动与通货膨胀不对称动态关系研究

建立不对称动态菲利普斯曲线理论研究经济周期中产出波动与通货膨胀不对称动态关系。该理论蕴含了经济扩张与收缩期中通胀持续性、产出波动对通胀的长短期影响差异特征及相关检验方法。运用该理论对中国相关季度数据进行了实证分析,结果表明:产出波动对通货膨胀短期中具有"顺周期"的正相关性,充当了"晴雨器"作用;长期中具有"逆周期"的负相关性,充当了"稳定器"作用。统计检验表明,经济周期中通货膨胀持续性及产出波动对通货膨胀的长短期影响具有显著不对称性,这种不对称性是中国经济转型期经济运行质量的历史检验,对现阶段追求经济增长质量具有深刻的政策启示。     

Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box–Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically estimated model of data revisions for U.S. output growth is used to investigate small-sample properties.     

A multiple imputation approach to deal with the unity measure error

Unity measure errors (UME) in numerical survey data can determine serious bias in the estimates of interest. In this paper, a finite Gaussian mixture model is used to identify observations affected by UME and to robustly estimate the target parameters in presence of this type of error. In the proposed model, the mixture components are associated to the different error patterns across the variables. We follow a multiple imputation approach in a Bayesian setting that allows us to handle missing values in data. In this framework, the assessment of the uncertainty associated with both errors and missingness is based on repeatedly drawing from the predictive distribution of the true non contaminated data given the observed data. The draws are obtained through a suitable version of the data augmentation algorithm. Applications to both simulated and real data are presented.     

新一轮通胀的特征、成因与二元经济下的通货管理转型

CPI与PPI波动呈分异趋势、PPI对经济增长的敏感性增大、物价上涨的结构性特征显著及通胀效应大等特征说明新一轮通胀有着深层的结构性原因。基于货币供给软约束框架分析了结构性通胀的运行机理。指出M2的过快增长伴随着贷款产值比的下降削弱了稳定通货的基础,输入性通胀因素的不断强化加大了通货管理的难度;因农业与工业劳动生产率的差距过大而形成的农业工资推动的通货膨胀是刘易斯拐点后出现的一种新型通胀,它是"滞胀"的潜形式。推动工资与农产品价格上涨的原因并不是劳动力供给"短缺",而是工资定价机制的转型与劳动生产率差距过大的结合。通货管理应由应急型的以"总量调控"为主的粗放型管理模式,转为更加强调结构性手段运用和绩效的长期稳定的模式。     

Maximum likelihood estimation in a model with interval data

It is suggested that in some situations, observations for random variables should be collected in the form of intervals. In this paper, the unknown parameters in a bivariate normal model are estimated based on a set of point and interval observations via the maximum likelihood approach. The Newton-Raphson algorithm is used to find the estimates, and asymptotic properties of the estimator are provided. Monte Carlo studies are conducted to study the performance of the estimator. An example based on real-life data is presented to demonstrate the practical applicability of the method.     

Mean and cell-mean imputation resulting in the use of a semicontinuous distribution and a mixture of semicontinuous distributions

Zero-inflated models are commonly used for modeling count and continuous data with extra zeros. Inflations at one point or two points apart from zero for modeling continuous data have been discussed less than that of zero inflation. In this article, inflation at an arbitrary point α as a semicontinuous distribution is presented and the mean imputation for a continuous response is discussed as a cause of having semicontinuous data. Also, inflation at two points and generally at k arbitrary points and their relation to cell-mean imputation in the mixture of continuous distributions are studied. To analyze the imputed data, a mixture of semicontinuous distributions is used. The effects of covariates on the dependent variable in a mixture of k semicontinuous distributions with inflation at k points are also investigated. In order to find the parameter estimates, the method of expectation–maximization (EM) algorithm is used. In a real data of Iranian Households Income and Expenditure Survey (IHIES), it is shown how to obtain a proper estimate of the population variance when continuous missing at random responses are mean imputed.     

Pseudo–Modelling of Outliers for Estimation of Regression And Time Series Models

This paper documents situations where the variance inflation model for outliers has undesirable properties. The model is commonly used to accommodate outliers in a Bayesian analysis of regression and time series models. The alternative approach provided here does not suffer from these undesirable properties but gives inferences similar to those of the variance inflation model when this is appropriate. It can be used with regression, time series, and regression with correlated errors in a unified way, and adheres to the scientific principle that inference should be based on the data after obvious outliers have been discarded. Only one parameter is required for outliers; it is interpretable as the a priori willingness to remove observations from the analysis.     

数据修订、实时估计与时变参数货币政策规则抉择

本文拓展构建了后顾、同期和前瞻三种类型的货币政策规则,并基于实时数据和最终数据实证分析数据修订和实时估计对货币政策参数的影响效应。研究结果发现,数据修订对泰勒规则的影响取决于不同模型,而且在三种模型设定中,盯住产出缺口和通胀目标的时变参数均在不同程度上受数据修订的影响。特别是,对于最终数据,采用同期性货币政策规则展开估计最为有效;而对于实时数据,则基于后顾性货币政策规则模型估计是最佳的。最后,本文在数据选择和模型匹配上提出相应的对策建议。