Assessing the historical accuracy of regional economic forecasts

This study addresses for the first time systematic evaluation of a widely used class of forecasts, regional economic forecasts. Ex ante regional structural equation model forecasts are analysed for 19 metropolitan areas. One- to ten-quarter-ahead forecasts are considered and the seven-year sample spans a complete business cycle. Counter to previous speculation in the literature, (1) dependency on macroeconomic forecasting model inputs does not substantially erode accuracy relative to univariate extrapolative methodologies and (2) stochastic time series models do not on average, yield more accurate regional economic predictions than structural models. Similar to findings in other studies, clear preferences among extrapolative methodologies do not emerge. Most general conclusions, however, are subject to caveats based on step-length effects and region-specific effects.     

Pre‐recession pattern of six economic indicators in the USA

This paper applies a tightly parameterized pattern recognition algorithm, previously applied to earthquake prediction, to the problem of predicting recessions. Monthly data from 1962 to 1996 on six leading and coincident economic indicators for the USA are used. In the full sample, the model performs better than benchmark linear and non‐linear models with the same number of parameters. Subsample and recursive analysis indicates that the algorithm is stable and produces reasonably accurate forecasts even when estimated using a small number of recessions. Copyright © 2000 John Wiley & Sons, Ltd.     

International interdependence of business cycles in the manufacturing industry: The use of leading indicators for forecasting and analysis

This article introduces new leading indicators for fifteen industrialized countries which enable the business cycle in manufacturing to be forecast fairly reliably between 4 and 6 months ahead. These indicators are based on an improved variant of the NBER method, yielding a composite leading indicator characterized by less erratic movements and clear turning points. The indicators are used to explore the international interdependence of business cycles and to examine the degree to which this interdependence is affected by growing economic integration, as in the EC. For each of the countries studied, the various foreign economies affecting the local business climate are identified. Since the business cycles of some countries clearly lead those of others, this international interdependence can be used to further improve the predictive power of the leading indicators in the lagging countries.     

System-based weights versus series-specific weights in the combination of forecasts

System-based combination weights for series r/step-length h incorporate relative accuracy information from other forecast step-lengths for r and from other series for step-length h. Such weights are examined utilizing the West and Fullerton (1996) data set-4275 ex ante employment forecasts from structural simultaneous equation econometric models for 19 metropolitan areas at 10 quarterly step-lengths and a parallel set of 4275 ARIMA forecasts. The system-based weights yielded combined forecasts of higher average accuracy and lower risk of large inaccuracy than seven alternative strategies: (1) averaging; (2) relative MSE weights; (3) outperformance (per cent best) weights; (4) Bates and Granger (1969) optimal weights with a convexity constraint imposed; (5) unconstrained optimal weights; (6) select a ‘best’ method (ex ante) by series and; (7) experiment in the Bischoff (1989) sense and select either method (2) or (6) based on the outcome of e experiment. Accuracy gains of the system-based combination were concentrated at step-lengths two to five. Although alternative (5) was generally outperformed, none of the six other alternatives was systematically most accurate when evaluated relative to each other. This contrasts with Bischoff's (1989) results that held promise for an empirically applicable guideline to determine whether or not to combine.     

Unbiasedness,efficiency and the combination of economic forecasts

This paper considers the problem of determining whether forecasts are unbiased and examines the implications this has for combining different forecasts. The practical issues of how economic forecasts might be combined are discussed. There is an empirical illustration of the procedures in which the properties of UK forecasts from the London Business School, the National Institute, the Henley Centre for Forecasting, Phillips and Drew and the OECD are examined.     

Modifying quantitative forecasts of livestock production using expert judgments: An application to the australian lamb industry

The judgmental modification of quantitative forecasts has become increasingly adopted in the production of agricultural commodity outlook information. Such modifications allow current period information to be incorporated into the forecast value, and ensure that the forecast is realistic in the context of current industry trends. This paper investigates the potential value of this approach in production forecasting in the Australian lamb industry. Several individual and composite econometric models were used to forecast a lamb-slaughtering series with a selected forecast being given to a panel of lamb industry specialists for consideration and modification. The results demonstrate that this approach offers considerable accuracy advantages in the short-term forecasting of livestock market variables, such as slaughtering, whose values can be strongly influenced by current industry conditions.     

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.     

Short-term forecasting of industrial production by means of quick indicators

This paper reports results on building transfer function models with linear combinations of quick indicators as inputs for very short-term prediction of the monthly time series of the volume of industrial production in Finland. The number of input variables in the transfer function models is reduced in two alternative ways: by replacing the original indicators by their two first principal components and by omitting certain indicators. The prediction accuracy of the transfer function models is checked outside the sample and found superior to that of corresponding ARIMA models. Neither of the two ways of reducing the number of input variables leads to consistently more accurate forecasts than the other. It is also found that the prediction accuracy of the transfer function models compares rather favourably with the preliminary values of the volume of industrial production published by the Central Statistical Office during the periods of rapid growth.     

An assessment of the economic value of non-linear foreign exchange rate forecasts

Recent research suggests that non-linear methods cannot improve the point forecasts of high-frequency exchange rates. These studies have been using standard forecasting criteria such as smallest mean squared error (MSE) and smallest mean absolute error (MAE). It is, however, premature to conclude from this evidence that non-linear forecasts of high-frequency financial returns are economically or statistically insignificant. We prove a proposition which implies that the standard forecasting criteria are not necessarily particularly suited for assessment of the economic value of predictions of non-linear processes where the predicted value and the prediction error may not be independently distributed. Adopting a simple non-linear forecasting procedure to 15 daily exchange rate series we find that although, when compared to simple random walk forecasts, all the non-linear forecasts give a higher MSE and MAE, when applied in a simple trading strategy these forecasts result in a higher mean return. It is also shown that the ranking of portfolio payoffs based on forecasts from a random walk, and linear and non-linear models, is closely related to a non-parametric test of market timing.     

Forecasting inflation using economic indicators: the case of France

In order to provide short‐run forecasts of headline and core HICP inflation for France, we assess the forecasting performance of a large set of economic indicators, individually and jointly, as well as using dynamic factor models. We run out‐of‐sample forecasts implementing the Stock and Watson (1999) methodology. We find that, according to usual statistical criteria, the combination of several indicators—in particular those derived from surveys—provides better results than factor models, even after pre‐selection of the variables included in the panel. However, factors included in VAR models exhibit more stable forecasting performance over time. Results for the HICP excluding unprocessed food and energy are very encouraging. Moreover, we show that the aggregation of forecasts on subcomponents exhibits the best performance for projecting total inflation and that it is robust to data snooping. Copyright © 2007 John Wiley & Sons, Ltd.     

Decision-analytic assessment of the economic value of weather forecasts: The fallowing/planting problem

A decision-analytic approach is taken to the problem of assessing the economic value of imperfect weather forecasts. Emphasis is placed on measures of the quality of such information and on the relationship between quality and economic value. The fallowing/planting problem for a spring wheat farmer is examined in detail as a specific application. It is assumed that the farmer's goal is to maximize the total expected discounted return over an infinite horizon, which places this problem within the general framework of Markov decision processes. By means of stochastic dynamic programming, the economic value to the farmer of currently available seasonal precipitation forecasts, as well as of hypothetical improvements in the quality of such forecasts, is estimated. Because the relationship between the quality and value of forecasts is highly nonlinear, the need to explicitly determine value-of-information estimates, rather than relying on quality as a surrogate for value, is made clear.     

Measuring the probability of a business cycle turning point by using a multivariate qualitative hidden Markov model

A two‐step procedure to produce a statistical measure of the probability of being in an accelerating or decelerating phase of economic activity is proposed. It consists of, first, an extraction of the individual linear innovations of a set of relevant macroeconomic variables whose signs are accumulated into a qualitative vector process and, second, of a factor analysis applied to this vector. The factor process is a two‐state Markov process of order one whose states are described as favourable and unfavourable. Estimated on French business surveys, this measure appears to be a competitive coincident indicator. Copyright © 2000 John Wiley & Sons, Ltd.     

Automatic recognition of different phases of the cell cycle and automatic count of the cells by means of a computer coupled to a cytophotometer]

Existing relationships between cell cycle phases and chromatin scattering within the nucleus led us to state more precisely our first alogrithm for cell cycle phases recognition. These refundments give evidence for the accuracy and the improving performance of the densitometric and morphologic analysis system used in this study: the "Samba".     

Switching among functional states by means of neuromodulators in the lobster stomatogastric ganglion

Summary The lobster stomatogastric ganglion contains the central pattern generators (CPGs) for the pyloric and gastric mill rhythms. All of the neurons and their synaptic connections have been identified for each rhythm and serve as the basis for understanding the mechanisms by which chemical neuromodulators are able to alter the functional state of each CPG. Using examples of different amines and peptides, I show how these substances can be found within specific neurons and how their application to the CPG can alter the motor patterns in specific ways. I also discuss what changes in cellular and synaptic properties occur as a result of bath application and particularly in the case of proctolin, how these changes may have behavioral correlates. The various outputs appear to be the result of a functional rewiring of anatomically defined neural circuits and this may be a widespread mechanism for the production of closely-linked but behaviorally distinct movement patterns.     

Estimation and prediction under structural instability: The case of the U.S. pulp and paper market

The objectives of this paper are: first, to show empirically the relevance of using adaptive estimation techniques over more traditional estimation approaches when economic systems are believed to be structurally unstable over time; and secondly, to compare in an empirical framework two adaptive estimation techniques: Kalman filtering and the Carbone–Longini filter. For that purpose, an econometric model for the U.S. pulp and paper market is examined under the assumption of structural instability and, hence, constitutes the basis for comparing forecasting performances and estimation accuracy achieved by each technique. A version of Kalman filtering, modified in line with the basic idea of ‘tracking’ characterizing the Carbone–Longini filter, is also presented and applied. The analysis of the results shows that it may be worth using adapative estimation methods to estimate structurally unstable models, even if there is no prior knowledge about the patterns of variation of the parameters. Also, it shows the Carbone–Longini filter and Kalman filtering as being complementary estimation techniques. An estimation/forecasting methodology involving a sequential application mode of these two techniques is suggested.     

Selective killing of smooth muscle cells in culture by the ricin A-chain conjugated with monoclonal antibodies to a cell surface antigen via a dextran bridge

Summary Monoclonal antibodies to a surface antigen of the modulated smooth muscle cells originally isolated from the rat aorta media were conjugated with ricin A-chain via an oxidized dextran bridge. The interaction of cultured cells with the conjugates obtained and with control substances was monitored following incorporation of¹⁴C-leucine radioactivity. It was found that¹⁴C-leucine incorporation was suppressed by 80–90% at a conjugate concentration of 10^–6–10^–7 M. Antigen-negative cells (line IAR; rat hepatocytes) were insensitive to the conjugate at any concentration used. Control use of purified ricin A-chain, native or oxidized dextran, specific and nonspecific IgG did not affect normal¹⁴C-leucine incorporation. The data obtained may be useful for designing targeted drug transport systems and for selective screening of modulated smooth cells in vascular pathology models in vivo.     

Resting energy expenditure in patients with cirrhosis of the liver measured by indirect calorimetry,anthropometry and bioelectrical impedance analysis

Energy expenditure was investigated in 15 patients with liver cirrhosis and 20 healthy controls by three methods: indirect calorimetry, anthropometry using the Harris-Benedict equation and bioelectrical impedance analysis. The energy expenditure was expressed in kcal/day, kcal/kg BW/day (BW — body weight), kcal/kg LBM/day (LBM — lean body mass, derived by bioelectrical impedance analysis) or in kcal/m²/day. We did not find statistical differences between values of resting energy expenditure obtained in patients with cirrhosis of the liver and healthy controls whichever method we used. We also did not find statistical differences between values obtained by indirect calorimetry, anthropometry and bioelectrical impedance analysis. There was a significant correlation between indirect calorimetry and anthropometry in both groups. We found significant correlations between indirect calorimetry and anthropometry, and between indirect calorimetry and bioelectrical impedance analysis, in the control group only. We can conclude that (1) resting energy expenditure of patients with cirrhosis of the liver is not changed when compared with healthy controls, and (2) bioelectrical impedance is a useful method to calculate body composition from which energy expenditure is derived; however, it gives an appropriate result only in healthy people, and only approximate values in patients with cirrhosis.