Assumptions on Fertility in Stochastic Population Forecasts*

In recent years Statistics Netherlands has published several stochastic population forecasts. The degree of uncertainty of the future population is assessed on the basis of assumptions about the probability distribution of future fertility, mortality and migration. The assumptions on fertility are based on an analysis of historic forecasts of the total fertility rate (TFR), on time‐series models of observations of the TFR, and on expert knowledge. This latter argument‐based approach refers to the TFR distinguished by birth order. In the most recent Dutch forecast the 95% forecast interval of the total fertility rate in 2050 is assumed to range from 1.2 to 2.3 children per woman.     

Modeling and forecasting the time series of US fertility: age distribution,range, and ultimate level

This discussion of modeling focuses on the difficulties in longterm, time-series forecasting of US fertility. Four possibilities are suggested. One difficulty with the traditional approach of using high or low bounds on fertility and mortality is that forecast errors are perfectly correlated over time, which means there are no cancellation of errors over time. The shape of future fertility intervals first increases, then stabilizes, and then decreases instead of remaining stable. This occurs because the number of terms being averaged increases with horizontal length. Alho and Spencer attempted to reduce these errors in time-series. Other difficulties are the idiosyncratic behavior of age specific fertility over time, biological bounds for total fertility rates (TFR) of 16 and zero, the integration of knowledge about fertility behavior that narrows the bounds, the unlikelihood of some probability outcomes of stochastic models with a normally distributed error term, the small relative change in TFR between years, a US fertility cycle of about 40 years, unimportant extrapolation of past trends in child and infant mortality, and the unlikelihood of reversals in mortality and contraceptive use trends. Another problem is the unsuitability of longterm forecasts. New methods include a model which estimates a one parameter family of fertility schedules and then forecasts that single parameter. Another method is a logistic transformation to account for prior information on the bounds on fertility estimates; this method is similar to Bayesian methods for ARMA models developed by Monahan. Models include information on the ultimate level of fertility and assume that the equilibrium level is a stochastic process trending over time. The horizon forecast method is preferred unless the effects of the outliers are known. Estimates of fertility are presented for the equilibrium constrained and logistic transformed model. Forecasts of age specific fertility rates can be calculated from forecasts of the fertility index (a single time varying parameter). The model of fertility fits poorly at older ages but captures some of the wide swings in the historical pattern. Age variations are not accounted for very well. Longterm forecasts tell a great deal about the uncertainty of forecast errors. Estimates are too sensitive to model specification for accuracy and ignore the biological and socioeconomic context.     

Forecasting Errors: the Track Record

This Briefing Paper is the last of a series of three about forecasting. In this one we examine our forecasting record; it complements the February paper in which we analysed the properties of our forecasting model in terms of the error bands attached to the central forecast.
There are many ways of measuring forecasting errors, and in the first part of this Briefing Paper we describe briefing how we have tackled the problem. (A more detailed analysis can be found in the Appendix.) In Part II we report and comment upon the errors in our forecasts of annual growth rates and show how our forecasting performance has improved over the years. In Part III we focus on quarterly forecasts up to 8 quarters ahead, and compare our forecasting errors with measurement errors in the oficial statistics; with the estimation errors built into our forecast equations; and with the stochastic model errors we reported last February. A brief summary of the main conclusions is given below.     

证券分析师业绩预测和投资评级准确性实证分析

文章以国内证券分析师业的业绩预测和投资评级为研究对象,从投资评级的准确性、投资建议赢利性、业绩预测误差及其来源等几个方面进行了实证分析。研究结果表明,证券分析师的投资建议无论在短期还是中长期均不能产生显著的超额收益,业绩预测误差是导致投资评级失误的原因之一,而业绩预测误差主要源于分析师对公司层面信息的错误判断。     

Forecasting with Bayesian Vector Autoregressions Estimated Using Professional Forecasts

We propose a Bayesian shrinkage approach for vector autoregressions (VARs) that uses short‐term survey forecasts as an additional source of information about model parameters. In particular, we augment the vector of dependent variables by their survey nowcasts, and claim that each variable modelled in the VAR and its nowcast are likely to depend in a similar way on the lagged dependent variables. In an application to macroeconomic data, we find that the forecasts obtained from a VAR fitted by our new shrinkage approach typically yield smaller mean squared forecast errors than the forecasts obtained from a range of benchmark methods. Copyright © 2015 John Wiley & Sons, Ltd.     

On economic evaluation of directional forecasts

It is commonly accepted that information is helpful if it can be exploited to improve a decision making process. In economics, decisions are often based on forecasts of the upward or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework for assessing the economic forecast value when loss functions (or success measures) are properly formulated to account for the realized signs and realized magnitudes of directional movements. We discuss a general approach to (directional) forecast evaluation which is based on the loss function proposed by Granger, Pesaran and Skouras. It is simple to implement and provides an economically interpretable loss/success functional framework. We show that, in addition, this loss function is more robust to outlying forecasts than traditional loss functions. As such, the measure of the directional forecast value is a readily available complement to the commonly used squared error loss criterion.     

Does the Capitalization of Development Costs Improve Analyst Forecast Accuracy? Evidence from the UK

It has been documented that investments in Research and Development (R&D) are associated with increased errors and inaccuracy in earnings forecasts made by financial analysts. These deficiencies have been generally attributed to information complexity and the uncertainty of the future benefits of R&D. This paper examines whether the capitalization of development costs can reduce analyst uncertainty about the future economic outcome of R&D investments, provide outsiders with a better matching of future R&D‐related revenues and costs, and therefore promote accuracy in analyst forecasts. UK data is used, because accounting rules in the United Kingdom permitted firms to conditionally capitalize development costs even before the introduction of the International Financial Reporting Standards. The choice to expense R&D rather than conditionally capitalize development costs is found to relate positively to signed analyst forecast errors. This finding is robust to controlling for the influence of other factors that may affect errors, as well as for the influence of R&D investments on forecast errors. The decision to capitalize versus expense is not observed to have a significant influence on analyst forecast revisions. The findings are interpreted as evidence that the choice to capitalize as opposed to expense may help to reduce deficiencies in analyst forecasts; hence, is informative for users of financial statements. Increased informativeness is expected to have repercussions for the effectiveness with which analysts produce earnings forecasts, and, as a result, market efficiency.     

Unobserved Heterogeneity or Measurement Errors? Testing for Correlated Effects with Measurement Errors *

This paper addresses the problem of endogenous regressors due to the presence of unobserved heterogeneity, when this is correlated with the regressors, and caused by regressors’ measurement errors. A simple two‐stage testing procedure is proposed for the identification of the underlying cause of correlation between regressors and the error term. The statistical performance of the resulting sequential test is assessed using simulated data.     

The HRM–capital market link: Effects of securities analysts on strategic human capital

This article develops theory about an agency problem affecting the strategic human capital (SHC) of the firm. It proposes three categories of SHC‐related choices managers must make that imply a trade‐off between near‐ and long‐term performance. Dispersed shareholding, firm coverage by securities analysts, and their practice of publishing quarterly earnings forecasts are argued to entail a bias in management incentives, shifting the balance in this trade‐off toward near‐term performance. To restore the balance, securities analysts would need to distinguish transitory from recurring effects of SHC‐related choices in their valuation models (e.g., treating certain labor cost savings during cyclical downturns as transitory). Restoring the balance would also require them to anticipate long‐term effects in their long‐term earnings forecasts (e.g., long‐term positive effects of retaining employees with valuable skills during cyclical downturns). The article discusses specific transitory cost effects and long‐term effects they could potentially take into account. The skills and incentives needed by analysts to account for such effects are argued to vary across firm segments.     

Density Forecasts With Midas Models

We propose a parametric block wild bootstrap approach to compute density forecasts for various types of mixed‐data sampling (MIDAS) regressions. First, Monte Carlo simulations show that predictive densities for the various MIDAS models derived from the block wild bootstrap approach are more accurate in terms of coverage rates than predictive densities derived from either a residual‐based bootstrap approach or by drawing errors from a normal distribution. This result holds whether the data‐generating errors are normally independently distributed, serially correlated, heteroskedastic or a mixture of normal distributions. Second, we evaluate density forecasts for quarterly US real output growth in an empirical exercise, exploiting information from typical monthly and weekly series. We show that the block wild bootstrapping approach, applied to the various MIDAS regressions, produces predictive densities for US real output growth that are well calibrated. Moreover, relative accuracy, measured in terms of the logarithmic score, improves for the various MIDAS specifications as more information becomes available. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting Euro‐Area Macroeconomic Variables Using a Factor Model Approach for Backdating

We suggest to use a factor model based backdating procedure to construct historical Euro‐area macroeconomic time series data for the pre‐Euro period. We argue that this is a useful alternative to standard contemporaneous aggregation methods. The article investigates for a number of Euro‐area variables whether forecasts based on the factor‐backdated data are more precise than those obtained with standard area‐wide data. A recursive pseudo‐out‐of‐sample forecasting experiment using quarterly data is conducted. Our results suggest that some key variables (e.g. real GDP, inflation and long‐term interest rate) can indeed be forecasted more precisely with the factor‐backdated data.     

Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro‐level Analysis?

Forecasting labour market flows is important for budgeting and decision‐making in government departments and public administration. Macroeconomic forecasts are normally obtained from time series data. In this article, we follow another approach that uses individual‐level statistical analysis to predict the number of exits out of unemployment insurance claims. We present a comparative study of econometric, actuarial and statistical methodologies that base on different data structures. The results with records of the German unemployment insurance suggest that prediction based on individual‐level statistical duration analysis constitutes an interesting alternative to aggregate data‐based forecasting. In particular, forecasts of up to six months ahead are surprisingly precise and are found to be more precise than considered time series forecasts.     

Raiders of the lost high-frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting

We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produce for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these data to study whether the staff forecasts efficiently. Prespecified regressions of forecast errors on forecast revisions show the staff's GDP forecasts exhibit time-varying inefficiency between FOMC meetings, and also show some evidence for inefficient inflation forecasts.     

Forecasting interest rates with shifting endpoints

We consider forecasting the term structure of interest rates with the assumption that factors driving the yield curve are stationary around a slowly time‐varying mean or ‘shifting endpoint’. The shifting endpoints are captured using either (i) time series methods (exponential smoothing) or (ii) long‐range survey forecasts of either interest rates or inflation and output growth, or (iii) exponentially smoothed realizations of these macro variables. Allowing for shifting endpoints in yield curve factors provides substantial and significant gains in out‐of‐sample predictive accuracy, relative to stationary and random walk benchmarks. Forecast improvements are largest for long‐maturity interest rates and for long‐horizon forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluation of the best M4 competition methods for small area population forecasting

The ‘M4’ forecasting competition results were featured recently in a special issue of the International Journal of Forecasting and included projections for demographic time series. We sought to investigate whether the best M4 methods could improve the accuracy of small area population forecasts, which generally suffer from much higher forecast errors than regions with larger populations. The aim of this study was to apply the top ten M4 forecasting methods to produce 5- and 10-year forecasts of small area total populations using historical datasets from Australia and New Zealand. Forecasts were compared against the actual population numbers and forecasts from two simple benchmark models. The M4 methods were found to perform relatively well compared to our benchmarks. In the light of these findings, we discuss possible future directions for small area population forecasting research.     

What does Variation in Survey Design Reveal about the Nature of Measurement Errors in Household Consumption?

We randomly assigned eight different consumption surveys to obtain evidence on the nature of measurement errors in estimates of household consumption. Regressions using data from more error‐prone designs are compared with results from a ‘gold standard’ survey. Measurement errors appear to have a mean‐reverting negative correlation with true consumption, especially for food and especially for rural households.     

Forecasting realized volatility: a Bayesian model‐averaging approach

How to measure and model volatility is an important issue in finance. Recent research uses high‐frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model‐averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log‐volatility. Copyright © 2009 John Wiley & Sons, Ltd.     

高职高专英语口语常见错误分析及对策

本文通过对高职高专学生英语口语中常见错误的分析,应用Corder的错误分析理论,结合教学实践,提出了若干具体可行的提高高职高专英语口语教学的纠错方法,以提高英语口语课堂教学的高效性。     

Forecasting Tail Risks

This paper presents an early warning system as a set of multi‐period forecasts of indicators of tail real and financial risks obtained using a large database of monthly US data for the period 1972:1–2014:12. Pseudo‐real‐time forecasts are generated from: (a) sets of autoregressive and factor‐augmented vector autoregressions (VARs), and (b) sets of autoregressive and factor‐augmented quantile projections. Our key finding is that forecasts obtained with AR and factor‐augmented VAR forecasts significantly underestimate tail risks, while quantile projections deliver fairly accurate forecasts and reliable early warning signals for tail real and financial risks up to a 1‐year horizon. Copyright © 2016 John Wiley & Sons, Ltd.     

Long‐term rates: lower for longer

With the Federal Reserve and other central banks likely to start raising interest rates from next year, the focus is now on how high interest rates might ultimately go. Long‐term analysis of the path of interest rates in the world's main economies suggests that interest rates tend over time to gravitate towards a level reflecting long‐run growth and inflation. But there is scope for real interest rates to depart substantially from growth for lengthy periods of time. Based on our long‐run forecasts for growth and inflation we take the view that long‐term interest rates are likely to settle in at levels a bit lower than their recent historic averages. Structural changes in the world economy and vulnerabilities in the advanced economies are also likely to slow the process by which long‐term rates rise from current levels to their steady state positions. OE forecasts for long‐term rates in the major economies are generally lower at the 1‐year and long‐term horizons than consensus.