Using forecast evaluation to improve the accuracy of the Greenbook forecast

Recently, Patton and Timmermann (2012) proposed a more powerful kind of forecast efficiency regression at multiple horizons, and showed that it provides evidence against the efficiency of the Fed’s Greenbook forecasts. I use their forecast efficiency evaluation to propose a method for adjusting the Greenbook forecasts. Using this method in a real-time out-of-sample forecasting exercise, I find that it provides modest improvements in the accuracies of the forecasts for the GDP deflator and CPI, but not for other variables. The improvements are statistically significant in some cases, with magnitudes of up to 18% in root mean square prediction error.     

Evaluating a three-dimensional panel of point forecasts: The Bank of England Survey of External Forecasters

This article provides a first analysis of the forecasts of inflation and GDP growth obtained from the Bank of England's Survey of External Forecasters, considering both the survey average forecasts published in the quarterly Inflation Report, and the individual survey responses, recently made available by the Bank. These comprise a conventional incomplete panel dataset, with an additional dimension arising from the collection of forecasts at several horizons; both point forecasts and density forecasts are collected. The inflation forecasts show good performance in tests of unbiasedness and efficiency, albeit over a relatively calm period for the UK economy, and there is considerable individual heterogeneity. For GDP growth, inaccurate real-time data and their subsequent revisions are seen to cause serious difficulties for forecast construction and evaluation, although the forecasts are again unbiased. There is evidence that some forecasters have asymmetric loss functions.     

A new method to assess the degree of information rigidity using fixed-event forecasts

We propose a new method to explore the information content of fixed-event forecasts and estimate structural parameters that are keys to sticky and noisy information models. Estimation follows a regression-based framework in which estimated coefficients map one-to-one with parameters that measure the degree of information rigidity. The statistical characterization of regression errors explores the laws that govern expectation formation under sticky and noisy information, that is, they are coherent with the theory. This strategy is still unexplored in the literature and potentially enhances the reliability of inference results. The method also allows linking estimation results to the signal-to-noise ratio, an important parameter of noisy information models. This task cannot be accomplished if one adopts an “agnostic” characterization of regression errors. With regard to empirical results, they show a substantial degree of information rigidity in the countries studied. They also suggest that the theoretical characterization of regression errors yields a more conservative picture of the uncertainty surrounding parameter estimates.     

Forecasting and forecast narratives: The Bank of England Inflation Reports

We analyze the narratives that accompany the numerical forecasts in the Bank of England’s Quarterly Inflation Reports, 1997–2018. We focus on whether the narratives contain useful information about the future course of key macro variables over and above the point predictions, in terms of whether the narratives can be used to enhance the accuracy of the numerical forecasts. We also consider whether the narratives are able to predict future changes in the numerical forecasts. We find that a measure of sentiment derived from the narratives can predict the errors in the numerical forecasts of output growth, but not of inflation. We find no evidence that past changes in sentiment predict subsequent changes in the point forecasts of output growth or of inflation, but do find that the adjustments to the numerical output growth forecasts have a systematic element.     

The financial content of inflation risks in the euro area

Recent studies have emphasized that survey-based inflation risk measures are informative about future inflation, and thus are useful for monetary authorities. However, these data are typically only available at a quarterly frequency, whereas monetary policy decisions require a more frequent monitoring of such risks. Using the ECB Survey of Professional Forecasters, we show that high-frequency financial market data have predictive power for the low-frequency survey-based inflation risk indicators observed at the end of a quarter. We rely on MIDAS regressions for handling the problem of mixing data with different frequencies that such an analysis implies. We also illustrate that upside and downside risks react differently to financial indicators.     

Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set

We evaluate conditional predictive densities for US output growth and inflation using a number of commonly-used forecasting models that rely on large numbers of macroeconomic predictors. More specifically, we evaluate how well conditional predictive densities based on the commonly-used normality assumption fit actual realizations out-of-sample. Our focus on predictive densities acknowledges the possibility that, although some predictors can cause point forecasts to either improve or deteriorate, they might have the opposite effect on higher moments. We find that normality is rejected for most models in some dimension according to at least one of the tests we use. Interestingly, however, combinations of predictive densities appear to be approximated correctly by a normal density: the simple, equal average when predicting output growth, and the Bayesian model average when predicting inflation.     

Testing forecast accuracy of expectiles and quantiles with the extremal consistent loss functions

Forecast evaluations aim to choose an accurate forecast for making decisions by using loss functions. However, different loss functions often generate different ranking results for forecasts, which complicates the task of comparisons. In this paper, we develop statistical tests for comparing performances of forecasting expectiles and quantiles of a random variable under consistent loss functions. The test statistics are constructed with the extremal consistent loss functions of Ehm et al. (2016). The null hypothesis of the tests is that a benchmark forecast at least performs equally well as a competing one under all extremal consistent loss functions. It can be shown that if such a null holds, the benchmark will also perform at least equally well as the competitor under all consistent loss functions. Thus under the null, when different consistent loss functions are used, the result that the competitor does not outperform the benchmark will not be altered. We establish asymptotic properties of the proposed test statistics and propose to use the re-centered bootstrap to construct their empirical distributions. Through simulations, we show that the proposed test statistics perform reasonably well. We then apply the proposed method to evaluations of several different forecast methods.     

Reassessing the Relative Power of the Yield Spread in Forecasting Recessions

In this paper, we replicate the main results of previous research showing that the use of the yield spread in a probit model can predict recessions better than the Survey of Professional Forecasters. We investigate the robustness of their results in several ways: extending the sample to include the 2007‐09 recession, changing the starting date of the sample, using rolling windows of data instead of just an expanding sample, and using alternative measures of the “actual” value of real output. Our results show that the Rudebusch‐Williams findings are robust in all dimensions. Copyright © 2015 John Wiley & Sons, Ltd.     

Money demand and the role of monetary indicators in forecasting euro area inflation

This paper examines the stability of money demand and the forecasting performances of a broad monetary aggregate (M3), excess liquidity and excess inflation in predicting euro area inflation. The out-of sample forecasting performances are compared to a widely used alternative, the spread of interest rates. The results indicate that the evolution of M3 is still in line with money demand, even when observations from the economic and financial crisis are included. Both excess measures and the spread are useful for predicting inflation.     

Asymmetric loss functions and the rationality of expected stock returns

We combine the innovative approaches of Elliott, Komunjer, and Timmermann (2005) and Patton and Timmermann (2007) with a block bootstrap to analyze whether asymmetric loss functions can rationalize the S&P 500 return expectations of individual forecasters from the Livingston Surveys. Although the rationality of these forecasts has often been rejected, earlier studies have relied on the assumption that positive and negative forecast errors of identical magnitudes are equally important to forecasters. Allowing for homogenous asymmetric loss, our evidence still strongly rejects forecast rationality. However, if we allow for variation in asymmetric loss functions across forecasters, not only do we find significant differences in preferences, but also we can often no longer reject forecast rationality. Our conclusions raise serious doubts about the homogeneous expectations assumption often made in asset pricing, portfolio construction and corporate finance models.     

Evolving macroeconomic perceptions and the term structure of interest rates

We explore the role of evolving beliefs regarding the structure of the macroeconomy in improving our understanding of the term structure of interest rates within the context of a simple macro-finance model. Using quarterly vintages of real-time data and survey forecasts for the United States over the past 40 years, we show that a recursively estimated VAR on real GDP growth, inflation and the nominal short-term interest rate generates predictions that are more consistent with survey forecasts than a benchmark fixed-coefficient counterpart. We then estimate a simple term structure model under the assumption that investor risk attitude is driven by near-term expectations of the three state variables. When we allow for evolving beliefs about the macroeconomy, the resulting term structure model provides a better fit to the cross section of yields than the benchmark model, especially at longer maturities, and exhibits better performance in out-of-sample predictions of future yield movements.     

福建省三次产业对经济增长的贡献分析

本文利用1979-2008年福建省的地区生产总值及第一、二、三产业增加值的时间序列数据,测算了改革开放三十多年来各产业增长对经济增长的贡献。结果表明福建省经济的增长主要是由第二产业拉动的,其次是第三产业,最后是第一产业,其中第二产业对经济增长的贡献率达到了惊人的52.54%。在第二产业内部结构中,工业对经济增长的贡献率是建筑业的30倍。本文据此提出相关建议。     

经济低梯度区居民住房福利受损原因及保障机制

资本与劳动力要素之间在流动性方面的差异使得经济低梯度地区居民的住房福利受到了很大损失,因此,对外来投资性需求资本进行限制存在着一定的合理性.但是,在具体实现机制上应当对外来资本区别对待,并对本地居民构成的合理内涵加以科学的界定,同时,税收较其他手段而言,在限制外来投资性需求影响和保障本地居民住房福利方面更能够起到积极的作用.     

我国经济发展与离婚率关系的计量分析——基于VAR方法

改革开放以来,随着经济的发展,我国的离婚率持高不下。目前,越来越多的人关心中国离婚率的相关问题。文章基于VAR模型,采用协整分析,格兰杰因果分析,脉冲响应函数和方差分析法,发现离婚率与GDP增长率存在长期的正向协整关系,其长期弹性为0.11,GDP增长率是离婚率的单向格兰杰原因。脉冲响应和方差分解表明,GDP增长率对离婚率有影响但影响不太大,而离婚率对GDP增长率基本无影响。     

Measuring the Impact of Direct and Indirect R&D on the Productivity Growth of Industries: Using the Yale Technology Concordance

This paper investigates the impact of direct and indirect R&D on the productivity growth of 30 French industries during the period 1978–92. The main aim of this paper is to assess the appropriateness of the Yale Technology Concordance (YTC) as a proxy for indirect R&D flows. We compare the effect on productivity growth of the Yale proxy with the traditional proxy based on goods input–output flows, and assess their suitability for different groups of industries. Results indicate that the indirect YTC proxy performs slightly better than the traditional proxy, as a result of superior performance in the high-tech and service sectors in particular.     

炼厂加热炉的用分析及节能方法

介绍了火用能分析方法,火用的概念及火用分析基本原理。并且运用火用分析方法对炼厂中的加热炉进行分析,然后提出改进措施。     

南京市科技进步贡献率测算研究

科技进步贡献率对分析城市经济增长有重要意义。本文依据南京市1993年～2007年统计数据,以柯布-道格拉斯生产函数为基础,运用索洛余值法对南京市经济增长中的科技进步贡献率进行测算,根据测算结果分析出南京市科技贡献率对经济的实际影响并提供政策建议。