Diffusion of new products in risk-sensitive markets

Diffusion of new products may be deterred by consumers' uncertainties about how they will perform. This paper introduces a decision-theoretic framework for modeling the diffusion of consumables, in which consumers choose between a current and new product so as to maximize expected utility. Consumers that are sufficiently risk-averse delay adoption, and change their prior uncertainties in a Bayesian fashion using information generated by early adopters. Under certain assumptions about the underlying consumer choice process and the market dynamics, the result is logistic growth in the share of consumers that choose the new product. The model can be generalized by allowing for consumer heterogeneity with respect to performance of the new product. The paper concludes with a discussion of applications for market forecasting, design of market trials and other extensions.     

Forecasting inflation using univariate continuous-time stochastic models

In this paper we investigate the applicability of several continuous-time stochastic models to forecasting inflation rates with horizons out to 20 years. While the models are well known, new methods of parameter estimation and forecasts are supplied, leading to rigorous testing of out-of-sample inflation forecasting at short and long time horizons. Using US consumer price index data we find that over longer forecasting horizons—that is, those beyond 5 years—the log-normal index model having Ornstein–Uhlenbeck drift rate provides the best forecasts.     

Geopolitical risk and global financial cycle: Some forecasting experiments

In this study, we investigate the connection between geopolitical risk (GPR) and global financial cycle (GFCy) as well as whether the former has predictive value for the out-of-sample predictability of the latter. We utilize both the historical and recent GPR data and their variants, namely, GPR act covering all “acts” that constitute GPR such as war, nuclear invasion and terrorism, and GPR threat, which represents threats of these acts. We construct a predictive model that accommodates the salient features of the predicted and predictor series while the forecast evaluation is conducted for both in-sample and out-of-sample periods. Our findings reveal that a rise in GPR discourages investments in risky assets and by implication worsens GFCy. The impact is more severe after the global financial crisis (gfc), and the GPR threat exerts more adverse effect on GFCy compared with GPR act regardless of whether historical GPR or recent GPR is used. Meanwhile, the predictive model of GFCy that accommodates the GPR data outperforms the benchmark model that ignores it both in the in-sample and out-of-sample estimates albeit with improved forecast performance during the post-gfc period and at a longer forecast horizon. However, the recent GPR data, which are broader in scope, offer better forecast accuracy than the historical GPR data. Additional analyses involving the vulnerability of global economic conditions reveal similar outcomes as GFCy.     

Which survey indicators are useful for monitoring consumption? Evidence from European countries

This paper assesses the information content of two survey indicators for consumption developments in the near future for eight European countries in the period 1985–1998. Empirical work on this topic typically focuses on consumer confidence, the perceptions of buyers of consumption goods. This paper examines whether perceptions of sellers of consumption goods, measured by retail trade surveys, may also improve short‐term monitoring of consumption. We find that both consumer confidence and retailer confidence embody valuable information, when analysed in isolation. For France, Italy and Spain we conclude that adding retail confidence does not improve the indicator model once consumer confidence has been included. For the UK the reverse case is obtained. For the remaining four countries we show that combining consumer sentiment and retail trade confidence into a composite indicator leads to optimal results. Our results suggest that incorporating information from retail trade surveys may offer significant benefits for the analysis of short‐term prospects of consumption. Copyright © 2004 John Wiley & Sons, Ltd.     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.     

Identifying US business cycle regimes using dynamic factors and neural network models

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.     

State-space models for predicting IBNR reserve in row-wise ordered runoff triangles: Calendar year IBNR reserves & tail effects

The issue of modeling and forecasting IBNR (incurred but not reported) actuarial reserve under Kalman filter techniques and extensions, using data arranged in a runoff triangle, is a frequent theme in the literature. One quite recent approach is to order the runoff triangle under a row-wise fashion and use linear state-space models for the resulting data set. To allow new possibilities for short-term IBNR reserves as well as to mitigate insolvency risk, in this paper we extend such a state-space method by: (i) a calendar year IBNR reserve prediction; and (ii) a tail effect for the row-wise ordered triangle. The extension is implemented with a real runoff triangle and compared with some traditional IBNR predictors. Empirical results indicate that the approach of this paper outperforms the competing methods in terms of out-of-sample comparisons and gives more conservative IBNR reserves than the original state-space method.     

A simple parameter-driven binary time series model

We introduce a parameter-driven, state-space model for binary time series data. The model is based on a state process with a binomial-beta dynamics, which has a Markov, endogenous switching regime representation. The model allows for recursive prediction and filtering formulas with extremely low computational cost, and hence avoids the use of computational intensive simulation-based filtering algorithms. Case studies illustrate the advantage of our model over popular intensity-based observation-driven models, both in terms of fit and out-of-sample forecast.     

Forecasting marginal costs of a multiple-output production technology

This paper presents the results of fitting a scaled translog restricted profit function to ‘pseudo’-data formed by repeated runs of a large linear programming model of domestic and international refining. The translog approximation is designed to estimate the marginal cost of producing eight petroleum products given the amounts of each product demanded and the price of crude oil. We test the model against out-of-sample data from the refinery model and historical data. The model is used in the US Department of Energy's Annual Energy Outlook forecasting system.     

Investigating the relationship between gold and silver prices

This paper analyses the long-run relationship between gold and silver prices. The three main questions addressed are: the influence of a large bubble from 1979:9 to 1980:3 on the cointegration relationship, the extent to which by including error-correction terms in a non-linear way we can beat the random walk model out-of-sample, and the existence of a strong simultaneous relationship between the rates of return of gold and silver. Different efficient single-equation estimation techniques are required for each of the three questions and this is explained within a simple bivariate cointegrating system. With monthly data from 1971 to 1990, it is found that cointegration could have occurred during some periods and especially during the bubble and post-bubble periods. However, dummy variables for the intercept of the long-run relationships are needed during the full sample. For the price of gold the non-linear models perform better than the random walk in-sample and out-of-sample. In-sample non-linear models for the price of silver perform better than the random walk but this predictive capacity is lost out-of-sample, mainly due to the structural change that occurs (reduction) in the variance of the out-of-sample models. The in-sample and out-of-sample predictive capacity of the non-linear models is reduced when the variables are in logs. Clear and strong evidence is found for a simultaneous relationship between the rates of return of gold and silver. In the three type of relationships that we have analysed between the prices of gold and silver, the dependence is less out-of-sample, possibly meaning that the two markets are becoming separated. © 1998 John Wiley & Sons, Ltd.     

基于USD—IOWA算子的方案优选群决策方法

针对方案优选决策中评估专家经多轮交互后没有达成群体共识的问题，提出了相对稳定偏好的概念。通过对评估专家偏好信息的计算获得专家的效用稳定度，并以此作为USD—IOWA算子的诱导分量完成对单个评估专家相对稳定偏好信息的群集结，得到评估专家群体的相对稳定偏好矩阵。在此基础上采用基于量化优势度的方法对备选方案进行选择。最后给出一个算例说明方法的可行性。     

有色噪声下随机时滞传染病模型的稳定性

本文运用O-U过程刻画环境变化性，在Edoardo Beretta基础上构造了有色噪声影响下的随机时滞的传染病模型。运用一般Lyapunonv方法研究了有色噪声对该系统的影响并得到系统正平衡点保持稳定的充分条件。最后通过对比发现随机扰动对系统稳定性影响仅仅与其随机过程的方差有关。     

A detailed look at crude oil price volatility prediction using macroeconomic variables

We investigate whether crude oil price volatility is predictable by conditioning on macroeconomic variables. We consider a large number of predictors, take into account the possibility that relative predictive performance varies over the out-of-sample period, and shed light on the economic drivers of crude oil price volatility. Results using monthly data from 1983:M1 to 2018:M12 document that variables related to crude oil production, economic uncertainty and variables that either describe the current stance or provide information about the future state of the economy forecast crude oil price volatility at the population level 1 month ahead. On the other hand, evidence of finite-sample predictability is very weak. A detailed examination of our out-of-sample results using the fluctuation test suggests that this is because relative predictive performance changes drastically over the out-of-sample period. The predictive power associated with the more successful macroeconomic variables concentrates around the Great Recession until 2015. They also generate the strongest signal of a decrease in the price of crude oil towards the end of 2008.     

A BVAR model for the connecticut economy

A Bayesian vector autoregressive (BVAR) model is developed for the Connecticut economy to forecast the unemployment rate, nonagricultural employment, real personal income, and housing permits authorized. The model includes both national and state variables. The Bayesian prior is selected on the basis of the accuracy of the out-of-sample forecasts. We find that a loose prior generally produces more accurate forecasts. The out-of-sample accuracy of the BVAR forecasts is also compared with that of forecasts from an unrestricted VAR model and of benchmark forecasts generated from univariate ARIMA models. The BVAR model generally produces the most accurate short- and long-term out-of-sample forecasts for 1988 through 1992. It also correctly predicts the direction of change.     

Estimation of short-run predictive factor for US growth using state employment data

We estimate a predictive single factor model targeted to unobserved common growth in gross domestic product and gross domestic income (GDI) using a state-space framework with select state employment data. We use likelihood-based comparison to select the states to estimate the dynamic factor. The results show improved in-sample and out-of-sample performance than threshold principal component factors and financial spreads. Out-of-sample evaluations indicate larger gains for GDI growth with 14% to 20% lower mean squared forecast errors than other alternatives. Sectoral employment factors based on selected sectors using the state-space framework also show forecasting gains. An expanded model using both sectoral and state employment data shows that their common component is the primary predictive factor.     

基于PSO算法改进的ANFIS在织物染色配色中的应用研究

本文研究了PSO（粒子群优化）算法及一种用于织物染色配色的自适应模糊神经网络（ANFIS），提出了基于织物染色配色的粒子群算法改进的ANFIS配色模型，并进行了仿真试验。从仿真试验的结果看，该配色模型收敛速度快，精确度较未改进的ANFIS模糊神经网络有明显的提高，在解决织物染色配色问题上取得了令人满意的配色效果。     

A multi-country dynamic factor model with stochastic volatility for euro area business cycle analysis

This paper develops a dynamic factor model that uses euro area country-specific information on output and inflation to estimate an area-wide measure of the output gap. Our model assumes that output and inflation can be decomposed into country-specific stochastic trends and a common cyclical component. Comovement in the trends is introduced by imposing a factor structure on the shocks to the latent states. We moreover introduce flexible stochastic volatility specifications to control for heteroscedasticity in the measurement errors and innovations to the latent states. Carefully specified shrinkage priors allow for pushing the model towards a homoscedastic specification, if supported by the data. Our measure of the output gap closely tracks other commonly adopted measures, with small differences in magnitudes and timing. To assess whether the model-based output gap helps in forecasting inflation, we perform an out-of-sample forecasting exercise. The findings indicate that our approach yields superior inflation forecasts, both in terms of point and density predictions.     

A Method of Retail Mortgage Stress Testing: Based on Time‐Frame and Magnitude Analysis

In this study, a non‐stationary Markov chain model and a vector autoregressive moving average with exogenous variables coupled with a logistic function (VARMAX‐L) are used to analyze and predict the stability of a retail mortgage portfolio, based on the stress test framework. The method introduced in this paper can be used to forecast the transition probabilities in a retail mortgage over pre‐specified states, given a shock with a certain magnitude. Hence this method provides a dynamic picture of the portfolio transition process through which one can assess its behavior over time. While the paper concentrates on retail mortgages, the methodology of this study can be adapted also to analyze other credit products in banks. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessing inefficiency in the s&p 500 futures market

We analyse the price movement of the S&P 500 futures market for violations of the efficient market hypothesis on a short-term basis. To assess market inefficiency we construct a model and find that the returns, i.e. the difference in the logarithm of closing prices on consecutive days, exhibit the usual conditional heteroscedasticity behaviour typical of long series of financial data. To account for this non-linear behaviour we scale the returns by a volatility factor which depends on the daily high, low, and closing price. The rescaled series, which may be interpreted as the trend-countertrend component of the time series, is modelled using Box and Jenkins techniques. The resulting model is an ARMA(1,1). The scale factors are assumed to form a time series and are modelled using a semi-non-parametric method which avoids the restrictive assumptions of most ARCH or GARCH models. Using the combined model we perform 1000 simulations of market data, each simulation comprising 250 days (approximately one year). We then formulate a naive trading strategy which is based on the ratio of the one-day-ahead expected return to its one-day-ahead expected conditional standard deviation. The trading strategy has four adjustable parameters which are set to maximize profits for the simulation data. Next, we apply the trading strategy to one year of recent out-of-sample data. Our conclusion is that the S&P 500 futures market exhibits only slight inefficiencies, but that there exist, in principle, better trading strategies which take account of risk than the benchmark strategy of buy-and-hold. We have also constructed a linear model for the return series. Using the linear model, we have simulated returns and determined the optimum values for the adjustable parameters of the trading strategy. In this case, the optimum trading strategy is the same as the benchmark strategy, buy-and-hold. Finally, we have compared the profitability of the optimized trading strategy, based on the non-linear model, to three ad hoc trading strategies using the out-of-sample data. The three ad hoc strategies are more profitable than the optimized strategy.     

Combining algorithms based on robust estimation techniques and co-integrating restrictions

This paper will motivate alternative combining schemes, provide a statistic for comparing alternative combinations out-of-sample and provide an example demonstrating these techniques. The evidence suggests the proposed procedures are likely to do no worse than other approaches and promise to do better under circumstances commonly encountered with economic data: integrated series and contaminated data.