“剪刀差”问题的对策研究

本文首先指出了传统的房地产销售预测方法的不足。为了改进原有的预测方法 ,作者结合影响单个房地产销售量各项因素的特殊性和模糊性 ,运用模糊数学的理论与方法 ,建立了新的数学模型并将其成功地运用于实践 ,取得了较好的效果 ,其理论与方法具有较高的参考和应用价值。     

The role of no-arbitrage on forecasting: Lessons from a parametric term structure model

Parametric term structure models have been successfully applied to numerous problems in fixed income markets, including pricing, hedging, managing risk, as well as to the study of monetary policy implications. In turn, dynamic term structure models, equipped with stronger economic structure, have been mainly adopted to price derivatives and explain empirical stylized facts. In this paper, we combine flavors of those two classes of models to test whether no-arbitrage affects forecasting. We construct cross-sectional (allowing arbitrages) and arbitrage-free versions of a parametric polynomial model to analyze how well they predict out-of-sample interest rates. Based on US Treasury yield data, we find that no-arbitrage restrictions significantly improve forecasts. Arbitrage-free versions achieve overall smaller biases and root mean square errors for most maturities and forecasting horizons. Furthermore, a decomposition of forecasts into forward-rates and holding return premia indicates that the superior performance of no-arbitrage versions is due to a better identification of bond risk premium.     

Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?

Structural breaks have been suggested by several economists as a possible explanation for the Meese–Rogoff puzzle, in the sense that an exchange rate model can outperform the random walk in terms of the out-of-sample forecasting error if the period under investigation is free of structural breaks. The results indicate that structural breaks cannot explain the inability of the flexible price monetary model to outperform the random walk. The only plausible explanation for the Meese–Rogoff puzzle is that forecasting accuracy is traditionally assessed by magnitude-only measures. When forecasting accuracy is assessed by alternative measures that do not rely exclusively on the magnitude of error, the monetary model can outperform the random walk regardless of the presence or otherwise of structural breaks.     

Where have all the flowers gone? Forecasting green trends in the automobile industry with a choice-based conjoint adoption model

With rising gas prices, global warming, and green thinking, all-electric vehicles are currently considered the automobile technology of the future. However, besides their advantages electric drive trains also exhibit several disadvantages. Moreover, history shows several failed attempts to establish electric vehicles. Thus, a reliable forecasting model is needed that predicts if the current trend is sustainable. We develop and empirically test a choice-based conjoint adoption model that uses individual-level preferences as a basis for prediction. Predictions are mapped to the time of the next planned purchase in order to establish the adoption process. The model extends existing research in several ways. First, no prior information, e.g., historical market data or a functional form of the adoption process, has to be integrated. Second, the model allows dynamic modifications of product specifications or competition at different points in time. Third, a no-choice option can be integrated so that a technology switch is not forced by the model itself and switching costs can be considered. The empirical results reveal different critical factors for the adoption of all-electric vehicles, such as purchase price, range, timing of the market entry, or environmental evolution, which could lead to a solid base of consumers preferring this option.     

Return enhancement trading strategies for size based portfolios

Recent theoretical work suggests that definitions of market efficiency that allow for the possibility of time-varying risk-premia will generally lead to return sign predictability. Consistent with this theory, we show that a logit model based on the lagged value of the market risk premium is useful for successfully predicting the return sign for CRSP small decile portfolio returns, but not large ones. We additionally employ this model in market timing simulations of micro-cap mutual funds in which investment can actually be made. The results indicate that a market-timing strategy based on our return-sign forecasting model outperforms a buy-and-hold strategy for 13 of 14 micro-cap funds studied. On average, the buy-and-hold strategy produces an average compound return of 11.98% per annum versus an average of 16.60% for the market-timing strategy. Nevertheless, trading restrictions make the return-sign forecasting model more practical to employ by the micro-cap fund portfolio manager rather than the individual fund investor.

Forecasting and turning point predictions in a Bayesian panel VAR model

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model, which accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for hierarchical and for Minnesota-type priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.     

Bayesian time series analysis of periodic behaviour and spectral structure

Analysis, model selection and forecasting in univariate time series models can be routinely carried out for models in which the model order is relatively small. Under an ARMA assumption, classical estimation, model selection and forecasting can be routinely implemented with the Box–Jenkins time domain representation. However, this approach becomes at best prohibitive and at worst impossible when the model order is high. In particular, the standard assumption of stationarity imposes constraints on the parameter space that are increasingly complex. One solution within the pure AR domain is the latent root factorization in which the characteristic polynomial of the AR model is factorized in the complex domain, and where inference questions of interest and their solution are expressed in terms of the implied (reciprocal) complex roots; by allowing for unit roots, this factorization can identify any sustained periodic components. In this paper, as an alternative to identifying periodic behaviour, we concentrate on frequency domain inference and parameterize the spectrum in terms of the reciprocal roots, and, in addition, incorporate Gegenbauer components. We discuss a Bayesian solution to the various inference problems associated with model selection involving a Markov chain Monte Carlo (MCMC) analysis. One key development presented is a new approach to forecasting that utilizes a Metropolis step to obtain predictions in the time domain even though inference is being carried out in the frequency domain. This approach provides a more complete Bayesian solution to forecasting for ARMA models than the traditional approach that truncates the infinite AR representation, and extends naturally to Gegenbauer ARMA and fractionally differenced models.     

Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems

Despite the extensive amount of data generated and stored during the maintenance capacity planning process, Maintenance, Repair, and Overhaul (MRO) organizations have yet to explore their full potential in forecasting the required capacity to face future and unprecedented maintenance interventions. This paper explores the integration of time series forecasting capabilities in a tool for maintenance capacity planning of complex product systems (CoPS), intended to value data that is routinely generated and stored, but often disregarded by MROs. State space formulations with multiplicative errors for the simple exponential smoothing (SES), Holt’s linear method (HLM), additive Holt-Winters (AHW), and multiplicative Holt-Winters (MHW) are assessed using real data, comprised of 171 maintenance projects collected from a major Portuguese aircraft MRO. A state space formulation of the MHW, selected using the bias-corrected Akaike information criterion (AICc), is integrated in a Decision Support System (DSS) for capacity planning with probabilistic inference capabilities and used to forecast the workload probability distribution of a future and unprecedent maintenance intervention. The developed tool is validated by comparing forecasted values with workloads of a particular maintenance intervention and with a model simulating current forecasting practices employed by MROs.     

A note on forecasting the prices of gold and silver: Asymmetric loss and forecast rationality

Given that the prices of gold and silver have witnessed large and substantial swings in recent years, policymakers and investors need readily available and reliable forecasts of the prices of these two precious metals. Survey data of forecasts of the prices of gold and silver provide a particularly rich data environment for policymakers and investors to study developments in the markets for gold and silver. Our research helps to develop a deeper understanding of the properties of survey data of the prices of gold and silver. We study the shape of forecasters’ loss function and the rationality of their forecasts. Assuming an asymmetric loss function weakens evidence against forecast rationality, but results depend on the empirical model being studied.     

Reciprocal influences in future thinking between Europe and the USA

La prospective is generally considered to have grown after WWII in developed countries with two main centers, France and the United States of America. In France, the development of prospective does constitute an important point in contemporary history. Stemming from an idea from philosopher Gaston Berger near the end of the 1950s, a spirit arose accompanied by a practice spread in the central administration (government) and in major French corporations. The objective of this article is not to claim any French originality in thinking about the future. Instead, the following pages show how an original approach blending reflection on the future and present action took shape and the relationship that developed involving current practices on the other side of the Atlantic, mainly the USA, with the help of a few intermediaries.