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预测振荡模式：随机场时空分解方法的综合AI 解读

Spatial-temporal decompositions of climatologic fields have been obtained using a range of techniques, including principal component analysis (PCA) and principal oscillation patterns (POPS). PCA decompositions are forced to be correlated to the original field, but they may not capture interesting aspects of temporal variation. On the other hand, POPS decompositions focus on temporal variation but are not forced to correlate to the field. Here we introduce a hybrid of these…

彩池博彩市场中预期假说的半参数（分布无关）检验AI 解读

The expectations hypothesis maintains that a current forward or futures price should be an unbiased forecast of the expected future price. This paper tests the expectations hypothesis in the parimutuel gambling market for greyhound racing parametric and semiparametric estimators. Parimutuel gambling markets are similar to speculative asset markets in many regards. Conventional maximum likelihood tests of asset pricing require a priori specification of the statistical…

通过整合多个模型预测转折点AI 解读

A new method for forming composite turning point (or other qualitative) forecasts is proposed. Rather than forming composite forecasts by the standard Bayesian approach with weights proportional to each model's posterior odds, weights are assigned to the individual models in proportion to the probability of each model's having the correct turning point prediction. These probabilities are generated by logit models estimated with data on the models' past turning point…

长序列向量一般线性过程的快速似然近似：分数差分应用AI 解读

The quadratic form appearing in the likelihood function of a Gaussian stationary and invertible vector general linear process is considered in this paper. A new expression for this quadratic form is developed, which allows repeated computations to be performed very efficiently by possibly using forecast and backcast values of the series. While the quadratic form itself involves the inverse of the covariance matrix, the new expression uses the autocovariances of the…

预测能力的渐近推断AI 解读

This paper develops procedures for inference about the moments of smooth functions of out-of-sample predictions and prediction errors when there is a long time series of predictions and realizations. The aim is to provide tools for analysis of predictive accuracy and efficiency and, more generally, of predictive ability. The paper allows for nonnested and nonlinear models as well as for possible dependence of predictions and prediction errors on estimated regression…

非对称损失下预测与模型选择的进一步结果AI 解读

We make three related contributions. First, we propose a new technique for solving prediction problems under asymmetric loss using piecewise-linear approximations to the loss function, and we establish existence and uniqueness of the optimal predictor. Second, we provide a detailed application to optimal prediction of a conditionally heteroscedastic process under asymmetric loss, the insights gained from which are broadly applicable. Finally, we incorporate our results into…

使用相关预测成本函数估计时间序列模型AI 解读

In many forecasting problems, the forecast cost function is used only in evaluating the forecasts; a second cost function is used in estimating the parameters in the model. In this paper, I explore some of the ways in which the forecast cost function can be used in estimating the parameters and, more generally, in producing the forecasts. I define the optimal forecast and note that it may depend on the entire conditional distribution of the data, which is typically unknown.…

能否改善人们感知的经济预测质量？AI 解读

A number of topics are discussed concerning how economic forecasts can be improved in quality or at least in presentation. These include the following: using 50% uncertainty intervals rather than 95%; noting that even though forecasters use many different techniques, they are all occasionally incorrect in the same direction; that there is a tendency to underestimate changes; that some expectations and recently available data are used insufficiently; lagged forecasts errors…

摘要暂缺

使用相关成本函数估计时间序列模型AI 解读

In many forecasting problems, the forecast cost function is used only in evaluating the forecasts; a second cost function is used in estimating the parameters in the model. In this paper, I explore some of the ways in which the forecast cost function can be used in estimating the parameters and, more generally, in producing the forecasts. I define the optimal forecast and note that it may depend on the entire conditional distribution of the data, which is typically unknown.…

协整系统的预测表现评估AI 解读

This paper examines the forecast performance of a cointegrated system relative to the forecast performance of a comparable VAR that fails to recognize that the system is characterized by cointegration. The cointegrated system we examine is composed of three vectors, a money demand representation, a Fisher equation, and a risk premium captured by an interest rate differential. The forecasts produced by the vector error correction model (VECM) associated with this system are…

股票市场波动率与商业周期AI 解读

This paper investigates the joint time series behavior of monthly stock returns and growth in industrial production. We find that stock returns are well characterized by year-long episodes of high volatility, separated by longer quiet periods. Real output growth, on the other hand, is subject to abrupt changes in the mean associated with economic recessions. We study a bivariate model in which these two changes are driven by related unobserved variables, and conclude that…

截距修正与结构变化AI 解读

Analyses of forecasting that assume a constant, time-invariant data generating process (DGP), and so implicitly rule out structural change or regime shifts in the economy, ignore an aspect of the real world responsible for some of the more dramatic historical episodes of predictive failure. Some models may offer greater protection against unforeseen structural breaks than others, and various tricks may be employed to robustify forecasts to change. We show that in certain…

摘要暂缺

股票市场波动率长记忆的建模与定价AI 解读

A new class of fractionally integrated GARCH and EGARCH models for characterizing financial market volatility is discussed. Monte Carlo simulations illustrate the reliability of quasi maximum likelihood estimation methods, standard model selection criteria, and residual-based portmanteau diagnostic tests in this context. New empirical evidence suggests that the apparent long-run dependence in U.S. stock market volatility is best described by a mean-reverting fractionally…

计量经济模型的确定AI 解读

This paper is concerned with model determination methods and their use in the prediction of economic time series. The methods are Bayesian but they can be justified by classical arguments as well. The paper continues some recent work on Bayesian asymptotic, develops embedding techniques for vector martingales, and implements the modeling ideas in a multivariate regression framework that includes Bayesian vector autoregression (BVAR's) and reduced rank regressions (RRR's). It…

宏观经济理论视角下的应用协整分析AI 解读

Cointegration analyses of macroeconomic time series are often not based on fully specified theoretical models. We use a theoretical model to scrutinize common procedures in applied cointegration analysis. Monte Carlo experiments show that 1) some tests of the cointegration vectors do not work well on series generated by an equilibrium cycle model; (2) cointegration restrictions add little to forecasting; (3) structural VAR models based on weak long-run restrictions seem to…

宏观计量模型中中位数无偏估计的计算AI 解读

A stochastic simulation model is proposed in this paper for obtaining median unbiased (MU) estimates in macroeconometric models. MU estimates are computed for the lagged dependent variable (LDV) coefficients in 18 equations of a macroeconometric model. The 2SLS bias for a coefficient, defined as the difference between the 2SLS estimate and the MU estimate, is on average smaller in absolute value than would be expected from Andrews' exact results for an equation with only a…

从宏观经济理论审视应用协整分析AI 解读

Cointegration analyses of macroeconomic time series are often not based on fully specified theoretical models. We use a theoretical model to scrutinize common procedures in applied cointegration analysis. Monte Carlo experiments show that 1) some tests of the cointegration vectors do not work well on series generated by an equilibrium cycle model; (2) cointegration restrictions add little to forecasting; (3) structural VAR models based on weak long-run restrictions seem to…

误设时间序列模型中的渐近最优估计AI 解读

A concept of asymptotically efficient estimation is presented when a misspecified parametric time series model is fitted to a stationary process. Efficiency of several minimum distance estimates is proved and the behavior of the Gaussian maximum likelihood estimate is studied. Furthermore, the behavior of estimates that minimize the h-step prediction error is discussed briefly. The paper answers to some extent the question what happens when a misspecified model is fitted to…

用预测最小拟偏差准则选择广义线性模型AI 解读

We consider the problem of selecting a model with the best predictive ability in a class of generalised linear models. A predictive least quasi-deviance criterion is proposed to measure the predictive ability of a model. This criterion is obtained by applying the idea of the predictive minimum description length principle and the theory of quasi-likelihood functions. The resulting predictive quasi-deviance function is an extension of the predictive stochastic complexity of…

多元时间序列广义预测误差方差的估计AI 解读

For a multivariate stationary time series, we propose a nonparametric estimator for its generalized prediction error variance using a multivariate analog of the Szegö-Kolmogorov formula, replacing the integral by a sum and replacing the unknown spectral density matrix by a consistent estimator. Asymptotic normality of this estimator is established, and its small-sample behavior is assessed through simulation and application to two real data sets. These examples show that the…

存在加性异常值的自回归过程的稳健贝叶斯模型选择AI 解读

Autoregressive (AR) models of order k are often used for forecasting and control of time series, as well as for the estimation of functionals such as the spectrum. Here we propose a method that consists of calculating the posterior probabilities of the competing AR(k) models in a way that is robust to outliers, and then obtaining the predictive distributions of quantities of interest, such as future observations and the spectrum, as a weighted average of the predictive…

含加性异常值的自回归过程稳健贝叶斯模型选择AI 解读

Autoregressive (AR) models of order k are often used for forecasting and control of time series, as well as for the estimation of functionals such as the spectrum. Here we propose a method that consists of calculating the posterior probabilities of the competing AR(k) models in a way that is robust to outliers, and then obtaining the predictive distributions of quantities of interest, such as future observations and the spectrum, as a weighted average of the predictive…