Financial Variables and the Simulated Out-of-Sample Forecastability of U.S. Output Growth Since 1985: An Encompassing Approach

We reconsider the out-of-sample forecasting ability of a large number of financial variables with respect to real output growth over the 1985:1–1999:4 period. We show that models including financial variables display almost no forecasting ability relative to an autoregressive benchmark model over this period according to a mean squared forecast error metric. However, tests based on forecast encompassing indicate that many financial variables do, in fact, contain information that is useful for forecasting real output growth over the 1985:1–1999:4 out-of-sample period. Our results suggest that the extant literature exaggerates the demise of the forecasting power of financial variables with respect to real activity since the mid-1980s.     

FORECASTING U.S. MORTALITY:

This article compares two methodologies for modeling and forecasting statistical time series models of demographic processes: Box-Jenkins ARIMA and structural time series analysis. The Lee-Carter method is used to construct nonlinear demographic models of U.S. mortality rates for the total population, gender, and race and gender combined. Single time varying parameters of k, the index of mortality, are derived from these model and fitted and forecasted using the two methodologies. Forecasts of life expectancy at birth, e₀, are generated from these indexes of k. Results show marginal differences in fit and forecasts between the two statistical approaches with a slight advantage to structural models. Stability across models for both methodologies offers support for the robustness of this approach to demographic forecasting.     

A test of uncertainty,expectations, and error response in two strike models

An information-uncertainty form of the Hicks strike model is used to test earlier work on the role of forecasts and uncertainty in determining strike activity. The expected zero coefficients for economic forecasts appear in preferred equations, but the expected positive coefficient for inflation uncertainty often appears as significantly negative. Alternative formulations and reasoning make the results appear somewhat more plausible. The performance of the Hicks model is contrasted with an updated Ashenfelter and Johnson model, which performs and predicts well without any untidy coefficients. Both models predict better than naive forecasting. Certain data and concept refinements are added to the testing of both analyses to bring them closer to the spirit of their models and to established research in wage determination and macroeconomics.     

Linking Migration Intentions with Flows: Evidence and Potential Use

In the absence of reliable, internationally available migration flow data necessary for statistical forecasting, policymakers increasingly turn to survey data on emigration intentions to evaluate future migration trends. The important assumption – i.e. that there is a measurable and systematic relationship between the intention to migrate and actual migration – has not been firmly established at the international level. We examine the association between estimated population averages of emigration intentions and official migration flow data based on data for more than 160 countries. The results show a strong association between emigration intentions and recorded bilateral flows to industrialized countries, as well as between intentions and aggregated out‐migration. The results provide policymakers with a reliability assessment of survey data on emigration intentions and encourage future attempts to incorporate survey data in formal statistical migration forecasting models.     

PREDICTING INFLATION: DOES THE QUANTITY THEORY HELP?

Various inflation forecasting models are compared for the period 1979–2003 using a simulated out-of-sample forecasting framework. Our findings are (1) M2 has marginal predictive content for inflation; (2) it is necessary to allow for the possibility that money, prices, and output are cointegrated; and (3) cointegration vector parameter estimation error is important when making out-of-sample forecasts. Consistent with previous work, we find a structural break in the early 1990s, but the break was easily detected and would not have affected out-of-sample inflation forecasts. Two Monte Carlo experiments that lend credence to our findings are also reported on.(JEL E31 , C32 )     

The contribution of E.C. consumer surveys in forecasting consumer expenditures: An econometric analysis for four major countries

In this article we investigate the forecasting performance of alternative models of private consumption using the EEC consumer surveys. Two basic conclusions emerge from the study:(1) in absolute as well as in comparison with a standard economic model, consumption functions incorporating opinion variables perform surprisingly well given the important measurement problems (missing data, qualitative character of the responses, strong collinearity among responses), and(2) consumers' opinions are helpful guides only in very short-term forecasting (between 0 and 3 quarters).The article extends previous investigations both on the basic characteristics of opinion variables and on their relation with actual economic variables.     

ECONOMIC LOSSES DUE TO FORECASTING ERROR AND THE U.S. POPULIST MOVEMENT

A farmer's crop mix is affected by the expected relative prices of the crops. A price forecasting error results in economic loss from planning a suboptimal mix. A new method of estimating economic loss due to forecasting error indicates that economic loss increased during the height of the Populist movement and then decreased. Using data for Kansas counties from 1882 to 1907, I estimate agricultural crop mix functions using a SUR (Seemingly Unrelated Regression) procedure and calculate the economic loss due to forecasting error. Although new market opportunities must have made farmers better oth these nnnnrtunities increased the cost of price uncertainly.     

FISCAL READJUSTMENTS IN THE UNITED STATES: A NONLINEAR TIME-SERIES ANALYSIS

We analyze the fiscal adjustment process in the United States using a multivariate threshold vector error regression model. The shift from single-equation to multivariate setting adds value both in terms of our economic understanding of the fiscal adjustment process and the forecasting performance of nonlinear models. We find evidence that fiscal authorities intervene to reduce real per capita deficit only when it reaches a certain threshold and that fiscal adjustment takes place primarily by cutting government expenditure. The results of out-of-sample density forecast and probability forecasts suggest that a shift from a univariate autoregressive model to a multivariate model improves forecast performance. ( JEL C32, C53, E62)     

Gender differences in the use of high-variance strategies in tournament competition

We report on a series of experiments in which participants first completed a simple game of chance and then competed in a tournament based on a multiple cue probability learning task. The results show that men made riskier choices in the simple game of chance, but women adopted the high-variance strategy more frequently in the tournament. However, after controlling for differences in forecasting skill, we find no significant differences in the rates at which men and women adopted a high-variance strategy. Although altering the difficulty of the forecasting task produces differences in the rate at which participants selected the high-variance strategy, it did not produce a significant difference in the rate at which men and women selected the strategy. Thus, this paper suggests that although women are more risk-averse than men, women are no less likely to adopt a high-variance strategy in a tournament competition.     

AT THE MOVIES: THE ECONOMICS OF EXHIBITION CONTRACTS

We describe a real-world profit sharing contract—the movie exhibition contract—and consider alternative explanations for its use. Two explanations based on difficulties with forecasting fit the facts better than asymmetric information models. The first emphasizes two-sided risk aversion; the second emphasizes measurement costs. Transaction costs and long-term relationships also affect contractual practices. We use an original data set of all exhibition contracts involving 13 theaters owned by a prominent St. Louis exhibitor over a two-year period to inform our theories and test hypotheses. The findings question traditional contract theory and may be relevant for other contracting environments.(JEL L14 , L82 , D45 , D80 )     

灰色模型在社会燃气事故预测中的应用

简要介绍灰色预测理论，以及GM（1，1）模型的建模过程。并应用灰色预测模型对南方某大型城市燃气公司管道燃气供应范围内的社会燃气事故死亡人数进行预测并实施检验。检验结果表明应用灰色预测模型预测社会燃气事故死亡人数的发展规律是可行有效的。     

INFERENCE USING QUALITATIVE AND QUANTITATIVE INFORMATION WITH AN APPLICATION TO MONETARY POLICY

I propose a framework for drawing inferences about an unobserved variable using qualitative and quantitative information. Using this framework, I study the timing and persistence of monetary policy regimes and compute probabilistic measures of the qualitative indicator's reliability. These estimates suggest that (1) it is over one and one-half times more likely that monetary policy is not restrictive at any point in time, (2) Boschen and Mills's [1995] policy index is a reliable indicator of the stance of monetary policy, and (3) certain qualitative indicators of monetary policy improve interest rate forecasts that are based on linear forecasting models. (JEL C22, E52)     

Wildfire forecasting: between criminal act and unintentional events

Forecasting represents the new credo in the reorganization of risks prevention. What does the introduction of such technology mean in terms of fire-fighting practice and policy? By applying Practice-Based Studies, forecasting practice emerges as a form of practical knowledge resulting from the alignment of the forecasting tool with foresters’ former competences, expertise, practices and tools. The acknowledgement of practical and scientific knowledge linked to forecasting allows the identification of the different organizational cultures linked to fire-fighting. For foresters wildfire is mainly a criminal act and forecasting a policing activity. This use of the artefact silences alternative approaches to wildfire-fighting such as the prevention of unintentional acts. While forecasting technology may reproduce forms of blindness in the future, anticipation becomes then an interesting research objet, embedded in dominant professional cultures and forms of knowledge.     

The role of consumer and business sentiment in forecasting telecommunications traffic

The objective of this study is to investigate the relevance of consumer and business sentiment surveys in forecasting international telecommunications traffic. The components of telecommunications data used in the study relate to total telephone traffic outgoing from Australia and its social and business telephone sub-categories. Three sentiment surveys are considered: one consumer survey conducted by the University of Melbourne, and two business surveys, each conducted by a business association and major bank. The above data series are available on a quarterly basis from the first quarter of 1973. The approach employed consists of the application of transfer function modelling techniques to the traffic and various indexes measuring consumer and business sentiment. A major finding of this study is that sentiment series are correlated with international telephone traffic, and that in all but one case, the relationship can be modelled by some form of transfer function. The forecasts of the estimated models satisfactorily incorporate directional swings in the traffic.     

Book Review

Affective forecasting refers to the process of predicting emotional reactions to future events. It plays an important role in decision making, but is also prone to errors, such as the ‘impact bias’: a tendency to overestimate the intensity of future reactions. The impact bias has been considered evolutionarily adaptive, as it performs a protective function in motivating people to avoid risky behaviour. Affective forecasting may be qualitatively different in a risk-taking population such as problem gamblers (PGs). In particular, PGs may fail to show the impact bias. This study was the first to examine affective forecasting in PGs. PGs (N = 25) and controls (N = 29) were asked to predict how they would feel after completing a guessing task. As hypothesized, controls exaggerated how bad they would feel after losing at the task, whereas PGs accurately predicted their reactions. Encouraging PGs to focus on anticipated emotions may be a novel target for treatment interventions.     

THE USEFULNESS OF CONSUMER CONFIDENCE IN FORECASTING HOUSEHOLD SPENDING IN CANADA: A NATIONAL AND REGIONAL ANALYSIS

We examine the ability of the Conference Board of Canada's Index of Consumer Attitudes (ICA) to forecast Canadian household spending both nationally and regionally. Our results indicate that at the national level, the ICA is able to predict total personal consumption expenditures and various subcategories of consumer spending, even when controlling for other macroeconomic variables. We find, however, that the forecasting ability of the regional indices is somewhat weaker when compared to that of the national ICA. Overall, our results reconfirm that consumer confidence is a reliable predictor of household spending in Canada. (JEL C53 , E21 )     

The effect of unemployment duration on national suicide rates: a time series analysis, 1948-1982

The relationship between unemployment and suicide in the United States is examined. Data for the period 1948 to 1978, primarily from the U.S. Public Health Service, are used to examine the effect of the duration of unemployment on suicide. "The results of a Cochrane-Orcutt iterative regression analysis indicate that the greater the duration of unemployment the greater the suicide rate. Using ex post forecasting techniques it is estimated that increases in unemployment during the Reagan administration have been associated with at least 929 additional deaths from suicide."     

A behavioral model of the demand for consumer durables

It is argued that models of the demand for consumer durables would be strengthened by the introduction of a behavioral element reflecting the nature of the decision making process within the individual household. A possible behavioural model is described. It is suggested that this would consist of the following elements: general economic confidence of the consumer (derived from personal expectations and attitudes to the general economic situation): general durable purchasing orientation (including willingness to devote resources to durable purchasesand house moving expectations); specific product choice considerations (including orders of acquisition of new durables: the implicit depreciation of existing equipment; perceptions of the characteristics of new products; purchase expectations). Such a model is more extensive than that normally described by others working in this field. The interaction between the different variables is discussed. It is shown that all these elements can be estimated through regular surveys and so the model can be made operational for forecasting purposes. However, it needs to be recognised that there are also unexpected even as which will affect the outcome in individual circumstances and the nature of these is also considered.     

A SIMPLE TEST FOR REGRESSION PARAMETER INSTABILITY

The relative advantages of simple, exact tests over sophisticated but conservative tests are stressed and a simple diagnostic test to detect regression parameter instability is proposed. The test is exact (in the statistical sense), easy to perform using any regression package, and simple enough to present in a first year course. Thus, this test has the potential for widespread use. Monte Carolo simulation results are presented which indicate that the power of the test is comparable to that of more sophisticated alternatives—e.g., the VPR test of Garbade (1977). As an illustration, the test is applied to a bivariate forecasting model for Texas personal income constructed using time series analysis techniques.     

Weathering Risk: Uncertainty,Weather Forecasting,and Expertise

Bridging the sociology of uncertainty and weather, this review paper provides a sociological examination of the weather forecasting enterprise in order to illustrate how expert decision makers negotiate high uncertainty. It begins with an outline of the literature on uncertainty and risk, particularly as it regards the modern experience. It then fleshes out and expands the discussion through an examination of weather forecasting practice. The final section reprises the theme of expert decision making under uncertainty, this time focusing on the communication of risk and uncertainty, and it concludes with a few thoughts on the future of the field.