Quasi‐rational and ex ante price expectations in commodity supply models: an empirical analysis of the US broiler market

A statistically optimal inference about agents' ex ante price expectations within the US broiler market is derived using futures prices of related commodities along with a quasi‐rational forecasting regression equation. The modelling approach, which builds on a Hamilton‐type framework, includes endogenous production and allows expected cash price to be decomposed into anticipated and unanticipated components. We therefore infer the relative importance of various informational sources in expectation formation. Results show that, in addition to the quasi‐rational forecast, the true supply shock, future prices, and ex post commodity price forecast errors have, at times, been influential in broiler producers' price expectations. Copyright © 2003 John Wiley & Sons, Ltd.     

The discourse of inflation accounting

The value of a share is given by the dividend discount model as a simple function of future dividends; but the actual determination of the share price is rarely based upon the direct estimation of these future dividends. A ranking of the valuation models used by analysts and fund managers shows a preference for ‘unsophisticated’ valuation using, for example, the dividend yield rather than the dividend discount model. This finding is shown to depend upon the practical difficulty of using currently-available information to forecast future cash flows. This difficulty limits the quantitative basis of valuations to short forecast horizons, while the subjective, qualitative estimation of terminal value assumes great importance. Crucially, both analysts and fund managers use their own assessment of management quality to underpin the estimation of terminal value, on the basis that superior quality causes outperformance and that, whereas management quality can be assessed now, future performance itself is unobservable. Linked with this and with information asymmetry, valuation is a dynamic, company-specific process, focused on personal communication with management and embodying ongoing signalling and implicit contracting, using both dividends and other variables. This method of valuation causes formal valuation models such as the dividend yield to play only a limited role. They offer a benchmark of relative price differences, which serves as a basis from which to conduct subjective, company-specific analysis and to make investment decisions; but valuation models are not used exclusively, in themselves, to value shares.     

Existence of an equilibrium for infinite horizon economies with and without complete information

This work proves the existence of an equilibrium for an infinite horizon economy where trade takes place sequentially over time. There exist two types of agents: the first correctly anticipates all future contingent endogenous variables with complete information as in Radner [Radner, R. (1972). Existence of equilibrium of plans, prices and price expectations in a sequence of markets. Econometrica, 289–303] and the second has exogenous expectations about the future environment as in Grandmont [Grandmont, J. M. (1977). Temporary general equilibrium theory. Econometrica, 535–572] and information based on the current and past aggregate variables including those which are private knowledge. Agents with exogenous expectations may have inconsistent optimal plans but have predictive beliefs in the context of Blackwell and Dubbins [Blackwell, D., Dubins, L. (1962). Merging of opinions with increasing information. The Annals of Mathematical Statistics, 882–886] with probability transition rules based on all observed variables. We provide examples of this framework applied to models of differential information and environments exhibiting results of market selection and convergence of an equilibrium. The existence result can be used to conclude that, by adding the continuity assumption on the probability transition rules, we obtain the existence of an equilibrium for some models of differential information and incomplete markets.     

Time series properties of an artificial stock market

This paper presents results from an experimental computer simulated stock market. In this market artificial intelligence algorithms take on the role of traders. They make predictions about the future, and buy and sell stock as indicated by their expectations of future risk and return. Prices are set endogenously to clear the market. Time series from this market are analyzed from the standpoint of well-known empirical features in real markets. The simulated market is able to replicate several of these phenomenon, including fundamental and technical predictability, volatility persistence, and leptokurtosis. Moreover, agent behavior is shown to be consistent with these features, in that they condition on the variables that are found to be significant in the time series tests. Agents are also able to collectively learn a homogeneous rational expectations equilibrium for certain parameters giving both time series and individual forecast values consistent with the equilibrium parameter values.     

Identification of rational expectations models

This paper deals with the problem of the identification of simultaneous Rational Expectations (RE) models. In the case of RE models with current expectations of the endogenous variables, the necessary and sufficient conditions for the global identification are derived explicitly in terms of the structural parameters and the linear homogenous identifying restrictions. It is shown that in the absence of a priori restrictions on the processes generating the exogenous variables and the disturbances, RE models and general distributed lag models are ‘observationally equivalent’. In the case of RE models with future expectations of the endogenous variables, a general solution that highlights the ‘non-uniqueness’ problem and from which other solutions such as forward or backward solutions can be obtained, is derived. It is shown that untestable and often quite arbitrary restrictions are needed if RE models with future expectations are to be identifiable. Certain order conditions similar to those obtained for the identification of RE models with current expectations are also derived for this case.     

Leading Indicators for Euro‐area Inflation and GDP Growth*

In this paper, we evaluate the role of a set of variables as leading indicators for Euro‐area inflation and GDP growth. Our leading indicators are taken from the variables in the European Central Bank's (ECB) Euro‐area‐wide model database, plus a set of similar variables for the US. We compare the forecasting performance of each indicator ex post with that of purely autoregressive models. We also analyse three different approaches to combining the information from several indicators. First, ex post, we discuss the use as indicators of the estimated factors from a dynamic factor model for all the indicators. Secondly, within an ex ante framework, an automated model selection procedure is applied to models with a large set of indicators. No future information is used, future values of the regressors are forecast, and the choice of the indicators is based on their past forecasting records. Finally, we consider the forecasting performance of groups of indicators and factors and methods of pooling the ex ante single‐indicator or factor‐based forecasts. Some sensitivity analyses are also undertaken for different forecasting horizons and weighting schemes of forecasts to assess the robustness of the results.     

Analysts' utilization of historical earnings information

This study examines whether security analysts (in)efficiently utilize the information contained in past series of annual and quarterly earnings in producing earnings forecasts. To do so, it investigates whether equal-weighted combinations of security analysts' forecasts with forecasts from statistical models based on historical earnings are superior, both in terms of being a better surrogate for the market's expectations of earnings and of accuracy, to forecasts from either one of these two sources. The empirical findings indicate that, although analysts' forecasts are superior to forecasts from statistical models, performance can be improved—both in terms of accuracy and also of being a better surrogate for market earnings expectations—by combining analysts' forecasts with forecasts from statistical models based on past quarterly earnings. Improvements in proxying for market earnings expectations were obtained even when analysts' forecasts made in June of the forecast year were used in the combinations. An implication of these findings is that investors can improve their investment decisions by using an average of the mean analysts' forecasts and the forecast produced by a time-series model of quarterly earnings in their investment decisions.     

Alternative procedures for converting qualitative response data to quantitative expectations: An application to Australian manufacturing

This paper analyses and extends alternative procedures for converting qualitative expectations responses to quantitative expectations. A number of conversion procedures is investigated, including the probability model, the time-varying parameter probability model, and the regression approach. The informational content of the survey expectations is compared with simple time series models. It is found that the expectations models are superior for many series, both in terms of producing lower forecast root mean square error (RMSE) values and in detecting turning points in the actual data. Survey expectations are also tested for rational expectations in aggregate using the orthogonality test.     

Solving generalized multivariate linear rational expectations models

We generalize the linear rational expectations solution method of Whiteman (1983) to the multivariate case. This facilitates the use of a generic exogenous driving process that must only satisfy covariance stationarity. Multivariate cross-equation restrictions linking the Wold representation of the exogenous process to the endogenous variables of the rational expectations model are obtained. We argue that this approach offers important insights into rational expectations models. We give two examples in the paper—an asset pricing model with incomplete information and a monetary model with observationally equivalent monetary-fiscal policy interactions. We relate our solution methodology to other popular approaches to solving multivariate linear rational expectations models, and provide user-friendly code that executes our approach.     

Examining the credibility of macroeconomic forecasts: Null of cointegration approach

In this study we examine the accuracy of forecasts of a select group of major macroeconomic variables, representing both the real and the financial sector of the economy. The theoretical foundations are similar to the one used to study exchange rate expectations, i.e. a verification of consistency and rationality in forecast formation. The empirical measure of accuracy is consistency in the expectation formation process, a precursor to rational forecasts. Here we examine the cointegration properties of the actual and forecast series (at multiple horizons) using the modern null of cointegration approach. A very reliable and continuos data set, the ASA-NBER survey is used. We find evidence of short (long) term expectational consistency (inconsistency) i.e. bandwagon effects and a mean reversion tendency in case of real variables, while the forecasts of financial variables are inconsistent across all forecast horizons.     

Inflation perceptions and expectations in Sweden – Are media reports the missing link?

We analyze the interrelation between consumers' quantitative inflation perceptions and expectations as well as actual inflation rates in Sweden. The role of media reports about inflation is emphasized. Structural vector error correction models show stable cointegration between actual, perceived and expected inflation. Impulse responses and forecast error variance decompositions suggest strong interaction between perceived and expected inflation, with a lesser role for actual inflation. Media effects are generally small, but imply an asymmetric reaction of inflation expectations and perceptions to news on increasing vs. decreasing inflation. Thus, to anchor inflation expectations, central banks should explore better communication channels to inform consumers about actual inflation.     

Financial nowcasts and their usefulness in macroeconomic forecasting

Financial data often contain information that is helpful for macroeconomic forecasting, while multi-step forecast accuracy benefits from incorporating good nowcasts of macroeconomic variables. This paper considers the usefulness of financial nowcasts for making conditional forecasts of macroeconomic variables with quarterly Bayesian vector autoregressions (BVARs). When nowcasting quarterly financial variables’ values, we find that taking the average of the available daily data and a daily random walk forecast to complete the quarter typically outperforms other nowcasting approaches. Using real-time data, we find gains in out-of-sample forecast accuracy from the inclusion of financial nowcasts relative to unconditional forecasts, with further gains from the incorporation of nowcasts of macroeconomic variables. Conditional forecasts from quarterly BVARs augmented with financial nowcasts rival the forecast accuracy of mixed-frequency dynamic factor models and mixed-data sampling (MIDAS) models.     

Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information

How did DSGE model forecasts perform before, during and after the financial crisis, and what type of off-model information can improve the forecast accuracy? We tackle these questions by assessing the real-time forecast performance of a large DSGE model relative to statistical and judgmental benchmarks over the period from 2000 to 2013. The forecasting performances of all methods deteriorate substantially following the financial crisis. That is particularly evident for the DSGE model’s GDP forecasts, but augmenting the model with a measure of survey expectations made its GDP forecasts more accurate, which supports the idea that timely off-model information is particularly useful in times of financial distress.     

Managerial evaluation of exogenous forecast sources

This paper concerns the managerial evaluation of forecast vendors—individuals or firms offering for sale future forecasts of random variables relevant to managerial decision making. Assuming the forecasts are exogenous in the sense they are generated by a methodology unknown or unproven to management, the paper uses a logistic regression model to present a statistical test for informativeness that allows for an interpretation of the vendor's abilities. The advantage of the approach is that it requires as input only knowledge of the unconditional probability distribution of the variable being forecast and a relatively small historical track record of the vendor's forecasting performance. No benchmark forecast is necessary and few assumptions are required about the statistical process that generates the forecasts. As an illustrative empirical application, the paper presents an evaluation of the informativeness of the published long-range price forecasts by a veteran analyst of the Iowa hog market.     

Non-Parametric Regression Models of Deviations from Othogonality in the Expectations Theory of the Term Structure

Regression tests of the expectations theory of the term structure typically reject the null hypothesis of orthogonality between implied forecast errors and the yield spreads. In the statistical literature on the term structure, these rejections are sometimes attributed to time-varying liquidity premia, and Engle et al . (1987) suggest that the ARCH-M model of time-variation in the liquidity premium may be sufficient to account for rejections of the expectations theory. We use non-parametric (kernel) regression to explore the regression test results on a number of data sets, and find some evidence of a persistent deviation from orthogonality for large absolute values of the spread. Incorporating ARCH-in-mean into models of the term premium indicates that this specification does explain significant time variation in liquidity premia, but the effect does not apepar to be sufficient to account for all of the deviations from orthogonality of forecast errors and spreads.     

Anchoring the yield curve using survey expectations

The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations can accurately predict yields, but they are typically not available for all maturities and/or forecast horizons. We show how survey expectations can be exploited to improve the accuracy of yield curve forecasts given by a base model. We do so by employing a flexible exponential tilting method that anchors the model forecasts to the survey expectations, and we develop a test to guide the choice of the anchoring points. The method implicitly incorporates into yield curve forecasts any information that survey participants have access to—such as information about the current state of the economy or forward‐looking information contained in monetary policy announcements—without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy relative to the class of models that are widely adopted by financial and policy institutions for forecasting the term structure of interest rates.     

On the determinants of quasi-option value

Quasi-option value is a concept or notion which suggests that resource decisions based on expected values of random variables may be biased in favor of development, if development is irreversible and the passage of time yields information on future expectations. This paper compares the limited and analytically divergent literature on the subject, derives the specific components of a measure of quasi-option value and speculates on the potential for quasi-option value becoming an operational component of analysis undertaken by governmental resource management agencies.     

Automobile insurance ratemaking in the presence of asymmetrical information

Automobile insurance is an example of a market where multi-period contracts are observed. This form of contract can be justified by asymmetrical information between the insurer and the insured. Insurers use risk classification together with bonus-malus systems. In this paper we show that the actual methodology for the integration of these two approaches can lead to inconsistencies. We develop a statistical model that adequately integrates risk classification and experience rating. For this purpose we present Poisson and negative binomial models with regression component in order to use all available information in the estimation of accident distribution. A bonus-malus system which integrates a priori and a posteriori information on an individual basis is proposed, and insurance premium tables are derived as a function of time, past accidents and the significant variables in the regression. Statistical results were obtained from a sample of 19,013 drivers.     

Voluntary Disclosure and Analyst Forecast

Empiricists document that firms more often voluntarily disclose bad news than good news and link this pessimism to managers’ increased incentives not to fall short of earnings expectations. This paper analyzes the voluntary disclosure of a manager’s private information by explicitly considering her incentives to meet or beat an analyst’s earnings forecast. The model predicts that managers who face strong incentives to meet or beat these forecasts more frequently disclose bad news than good news in order to guide analysts’ expectations about future earnings downward. This pessimism is higher in markets with less informed managers and may hold even if the manager has strong incentives for high stock prices and meet-or-beat incentives are comparably low.     

Comment choosing a monetary instrument the case of supply-side shocks

In a recent paper Craine and Havenner (1981) claim that ‘an additional advantage of the linear-quadratic framework (which is not well known) is that the basic forces affecting the instrument choice decision can be analyzed without an explicit specification for price expectations since the distribution of the error terms in a linear model is independent of the predetermined variables’ (p. 219). The aim of this note is to demonstrate that Craine and Havenner's claim does not generalize to models with expectations of future variables conditioned on present information, such as Turnovsky's (1980) model.