Judgmental adjustment of earnings forecasts

Earnings forecasts have received a great deal of attention, much of which has centered on the comparative accuracy of judgmental and objective forecasting methods. Recently, studies have focused on the use of combinations of subjective and objective forecasts to improve forecast accuracy. This research offers an extension on this theme by subjectively modifying an objective forecast. Specifically, ARIMA forecasts are judgmentally adjusted by analysts using a structured approach based on Saaty's (1980) analytic hierarchy process. The results show that the accuracy of the unadjusted objective forecasts can be improved when judgmentally adjusted.     

A super-resolution algorithm for spectral estimation and time series extrapolation

Recent developments in the signal processing field of electrical engineering have resulted in several frequency domain methods of extrapolating a time series. Insight gained in testing one such method, the Papoulis algorithm, has been used to suggest modifications which greatly improve its performance under most operating conditions where real data are concerned. The modified Papoulis method thus developed has been applied to electricity load forecasting over the short and medium term, as well as to world economic and energy data, to assess the cyclic structure present in each series about a trend.     

The accuracy of extrapolation (time series) methods: Results of a forecasting competition

In the last few decades many methods have become available for forecasting. As always, when alternatives exist, choices need to be made so that an appropriate forecasting method can be selected and used for the specific situation being considered. This paper reports the results of a forecasting competition that provides information to facilitate such choice. Seven experts in each of the 24 methods forecasted up to 1001 series for six up to eighteen time horizons. The results of the competition are presented in this paper whose purpose is to provide empirical evidence about differences found to exist among the various extrapolative (time series) methods used in the competition.     

The accuracy of extrapolation methods; an automatic box–jenkins package sift

Hill and Woodworth (1980) proposed an algorithm suitable for identifying Box–Jenkins models automatically without reliance on the investigator. This paper first reviews the method. It is then used on the 111 series analysed by Anderson in the Makridakis forecasting competition. The results show that the automatic method of Hill and Woodworth is comparable in terms of accuracy to the full Box–Jenkins identification procedure.     

Bounds for the least squares extrapolation in non‐linear AR(1) processes

It is proved that formula for least squares extrapolation in stationary non‐linear AR(1) process is valid also for non‐stationary non‐linear AR(1) processes. This formula depends on the distribution of the corresponding white noise. If the non‐linear function used in the model is non‐decreasing and concave, upper and lower bounds are derived for least squares extrapolation such that the bounds depend only on the expectation of the white noise. It is shown in an example that the derived bounds in some cases give a good approximation to the least squares extrapolation. Copyright © 2001 John Wiley & Sons, Ltd.     

Adjusting Judgemental Extrapolations using Theil's Method and Discounted Weighted Regression

Theil's method can be applied to judgemental forecasts to remove systematic errors. However, under conditions of change the method can reduce the accuracy of forecasts by correcting for biases that no longer apply. In these circumstances, it may be worth applying an adaptive correction model which attaches a greater weight to more recent observations. This paper reports on the application of Theil's original method and a discounted weighted regression form of Theil's method (DWR) to the judgemental extrapolations made by 100 subjects in an experiment. Extrapolations were made for both stationary and non-stationary and low- and high-noise series. The results suggest DWR can lead to significant improvements in accuracy where the underlying time-series signal becomes more discernible over time or where the signal is subject to change. Theil's method appears to be most effective when a series has a high level of noise. However, while Theil's corrections seriously reduced the accuracy of judgemental extrapolations for some series the DWR method performed well under a wide range of conditions and never significantly degraded the original forecasts. © 1997 by John Wiley & Sons, Ltd.     

Requirements for effective use of graphical methods in interactive forecasting

The construction of forecasts using interactive data analysis systems is greatly aided by the availability of graphical procedures. Data exploration, model identification and estimation, and interpretation of final forecasts are made considerably easier by the visual relay of information. This article discusses some recent developments in time series graphics designed to assist in the forecasting process. A discussion of requirerients for effective use of graphics in interactive forecasting is included as illustrated through an application of the Box-Jenkins methodology. Illustrations are included from the STATGRAPHICS system, a prototype implementation in APL.     

The teachers/practitioners corner the effects of indexing ARMA series using the consumer price index

The authors demonstrate that indexing a time series with an ARMA representation using the Consumer Price Index does not materially alter the ARMA form of the model. They further demonstrate that the forecasting error of the indexed series and of the product of the forecasts of the index and the time series are, for practical purpose, the same. Simulation results are reported for five model classes.     

ARIMA model building and the time series analysis approach to forecasting

This paper reviews the approach to forecasting based on the construction of ARIMA time series models. Recent developments in this area are surveyed, and the approach is related to other forecasting methodologies.     

Note: Nyse volume: An application of outlier analysis

The purpose of this study is to examine the monthly volume series of the New York Stock Exchange (NYSE) during the January 1965-December 1987 period. The NYSE volume series follows a random walk with drift process; however, the events of October 1987 give rise to the application of outlier analysis.     

Forecasting contemporaneous aggregates and the combination of forecasts: The case of the U.K. monetary aggregates

A number of papers in recent years have investigated the problems of forecasting contemporaneously aggregated time series and of combining alternative forecasts of a time series. This paper considers the integration of both approaches within the example of assessing the forecasting performance of models for two of the U.K. monetary aggregates, £M3 and MO. It is found that forecasts from a time series model for aggregate £M3 are superior to aggregated forecasts from individual models fitted to either the components or counterparts of £M3 and that an even better forecast is obtained by forming a linear combination of the three alternatives. For MO, however, aggregated forecasts from its components prove superior to either the forecast from the aggregate itself or from a linear combination of the two.     

Expectations,use and judgmental adjustment of external financial and economic forecasts: an empirical investigation

A survey of 124 users of externally produced financial and economic forecasts in Turkey investigated their expectations and perceptions of forecast quality and their reasons for judgmentally adjusting forecasts. Expectations and quality perceptions mainly related to the timeliness of forecasts, the provision of a clear justifiable rationale and accuracy. Cost was less important. Forecasts were frequently adjusted when they lacked a justifiable explanation, when the user felt they could integrate their knowledge into the forecast, or where the user perceived a need to take responsibility for the forecast. Forecasts were less frequently adjusted when they came from a well‐known source and were based on sound explanations and assumptions. The presence of feedback on accuracy reduced the influence of these factors. The seniority and experience of users had little effect on their attitudes or propensity to make adjustments. Copyright © 2008 John Wiley & Sons, Ltd.     

Ex post and ex ante prediction of unobserved multivariate time series: a structural‐model based approach

A methodology for estimating high‐frequency values of an unobserved multivariate time series from low‐frequency values of and related information to it is presented in this paper. This is an optimal solution, in the multivariate setting, to the problem of ex post prediction, disaggregation, benchmarking or signal extraction of an unobservable stochastic process. Also, the problem of extrapolation or ex ante prediction is optimally solved and, in this context, statistical tests are developed for checking online the ocurrence of extreme values of the unobserved time series and consistency of future benchmarks with the present and past observed information. The procedure is based on structural or unobserved component models, whose assumptions and specification are validated with the data alone. Copyright © 2007 John Wiley & Sons, Ltd.     

Forecasting the Italian industrial production index in real time

Daily electricity consumption data, available almost in real time, can be used in Italy to estimate the level of industrial production in any given month before the month is over. We present a number of procedures that do this using electricity consumption in the first 14 days of the month. (This is an extension of a previous model that used monthly electricity data.) We show that, with a number of adjustments, a model using half-monthly electricity data generates acceptable estimates of the monthly production index. More precisely, these estimates are more accurate than univariate forecasts but less accurate than estimates based on monthly electricity data. A further improvement can be obtained by combining ‘half-monthly’ electricity-based estimates with univariate forecasts. We also present quarterly estimates and discuss confidence intervals for various types of forecasts.     

Forecast comparisons of four models of U.S. interest rates

In this paper we compare the out of sample forecasts from four alternative interest rate models based on expanding information sets. The random walk model is the most restrictive. The univariate time series model allows for a richer dynamic pattern and more conditioning information on own rates. The multivariate time series model permits a flexible dynamic pattern with own- and cross-series information. Finally, the forecasts from the MPS econometric model depend on the full model structure and information set. In theory, more information is preferred to less. In practice, complicated misspecified models can perform much worse than simple (also probably misspecified) models. For forecasts evaluated over the volatile 1970s the multivariate time series model forecasts are considerably better than those from simpler models which use less conditioning information, as well as forecasts from the MPS model which uses substantially more conditioning information but also imposes ‘structural’ economic restrictions.     

Minimum variance prediction of bilinear time series: Direct and adaptive versions

Bilinear models of time series are considered. Minimum variance predictor for bilinear time series, homogeneous in the input and output, is proposed. Results of minimum variance prediction of bilinear time series are included. They are compared to the results of linear prediction of bilinear time series. A minimum variance prediction algorithm for bilinear time series of the general form is developed and an adaptive version of minimum variance algorithm is derived.     

A Comparison of Box—Jenkins and objective methods for determining the order of a non-seasonal ARMA Model

This paper evaluates different procedures for selecting the order of a non-seasonal ARMA model. Specifically, it compares the forecasting accuracy of models developed by the personalized Box-Jenkins (BJ) methodology with models chosen by numerous automatic procedures. The study uses real series modelled by experts (textbook authors) in the BJ approach. Our results show that many objective selection criteria provide structures equal or superior to the time-consuming BJ method. For the sets of data used in this study, we also examine the influence of parsimony in time-series forecasting. Defining what models are too large or too small is sensitive to the forecast horizon. Automatic techniques that select the best models for forecasting are similar in size to BJ models although they often disagree on model order.     

Linear constraints,robust-weighting and efficient composite modeling

Recent studies have shown that composite forecasting produces superior forecasts when compared to individual forecasts. This paper extends the existing literature by employing linear constraints and robust regression techniques in composite model building. Security analysts forecasts may be improved when combined with time series forecasts for a diversified sample of 261 firms with a 1980-1982 post-sample estimation period. The mean square error of analyst forecasts may be reduced by combining analyst and univariate time series model forecasts in constrained and unconstrained ordinary least squares regression models. These reductions are very interesting when one finds that the univariate time series model forecasts do not substantially deviate from those produced by ARIMA (0,1,1) processes. Moreover, security analysts' forecast errors may be significantly reduced when constrained and unconstrained robust regression analyses are employed.     

Seven components of judgmental forecasting skill: Implications for research and the improvement of forecasts

A decomposition of the Brier skill score shows that the performance of judgmental forecasts depends on seven components: environmental predictability, fidelity of the information system, match between environment and forecaster, reliability of information acquisition, reliability of information processing, conditional bias, and unconditional bias. These components provide a framework for research on the forecasting process. Selected literature addressing each component is reviewed, and implications for improving judgmental forecasting are discussed.     

Identification of the multi-input box-Jenkins transfer function model

In this paper different ways to identify the order of the Box–Jenkins transfer function model are discussed. The discussion concerns estimation of the impulse response weight function in the case of more than one input variable. It is found that most of the existing methods are either unsuitable when there is more than one input variable, or expensive or difficult to use. To overcome these deficiencies an extended regression method is proposed. The new method is based on the solution of some problems in connection with the use of the regression method. The impulse response weights are estimated by a biased regression estimator on variables transformed with respect to the noise model. To test the new approach a small simulation experiment has been performed. The results from the simulations indicate that the proposed method may be of value to the practitioner.