论文检索

澳大利亚通胀建模AI 解读

This article develops an empirically constant, data-coherent, error-correction model for inflation in Australia. The level of consumer prices is a markup over domestic and import costs, with adjustments for dynamics and relative aggregate demand. The authors address issues of cointegration, general to specific modeling, dynamic specification, model evaluation and testing, parameter constancy, forecasting, and exogeneity. They also test this model against existing models of…

计量经济学与统计学中的贝叶斯分析：Zellner 的观点与论文AI 解读

Part 1 Introduction. Part 2 Overview of Bayesian analysis. Part 3 Bayesian priors, models and information processing. Part 4 Bayesian methods. Part 5 Bayesian forecasting.

时间序列中的短期与长期因果性：理论AI 解读

Causality in Granger's sense is defined in terms of predictibility one period ahead. The notion of causality is generalized by considering causality at any given horizon 1 <= h <= inifinity, providing a rigorous formalization of indirect causal effects and causality chains in (possibly) nonstationary time series. The authors derive necessary and sufficient conditions for noncausality between vectors up to any given horizon. They observe that coefficients of lagged variables…

渐近校准AI 解读

Can we forecast the probability of an arbitrary sequence of events happening so that the stated probability of an event happening is close to its empirical probability? We can view this prediction problem as a game played against Nature, where at the beginning of the game Nature picks a data sequence and the forecaster picks a forecasting algorithm. If the forecaster is not allowed to randomise, then Nature wins; there will always be data for which the forecaster does…

经济预测的一个分析框架AI 解读

This paper proposes a tripartite framework of design, evaluation, and post-evaluation analysis for generating and interpreting economic forecasts. This framework?s value is illustrated by re-examining mean square forecast errors from dynamic models and nonlinearity biases from empirical forecasts of US external trade. Previous studies have examined properties such as nonlinearity bias and the possible nonmonotonicity and nonexistence of mean square forecast errors in…

马尔可夫转换与门限自回归模型对美国 GNP 的预测表现比较AI 解读

While there has been a great deal of interest in the modelling of non-linearities in economic time series, there is no clear consensus regarding the forecasting abilities of non-linear time-series models. We evaluate the performance of two leading non-linear models in forecasting post-war US GNP, the self-exciting threshold autoregressive model and the Markov-switching autoregressive model. Two methods of analysis are employed: an empirical forecast accuracy comparison of…

美国失业率预测AI 解读

This article presents a comparison of forecasting performance for a variety of linear and nonlinear time series models using the U.S. unemployment rate. Our main emphases are on measuring forecasting performance during economic expansions and contractions by exploiting the asymmetric cyclical behavior of unemployment numbers, on building vector models that incorporate initial jobless claims as a leading indicator, and on utilizing additional information provided by the…

预测涵盖检验AI 解读

The authors consider the situation in which two forecasts of the same variable are available. The possibility exists of forming a combined forecast as a weighted average of the individual ones and estimation the weights that should be optimally attached to each forecast. If the entire weight should optimally be associated with one forecast, that forecast is said to encompass the other. A natural test for forecast encompassing is based on least squares regression. The authors…

估计从未发生事件的概率：你的一票何时具有决定性？AI 解读

Researchers sometimes argue that statisticians have little to contribute when few realizations of the process being estimated are observed. We show that this argument is incorrect even in the extreme situation of estimating the probabilities of events so rare that they have never occurred. We show how statistical forecasting models allow us to use empirical data to improve inferences about the probabilities of these events. Our application is estimating the probability that…

估计从未发生事件的概率：你的一票何时具有决定性？AI 解读

Researchers sometimes argue that statisticians have little to contribute when few realizations of the process being estimated are observed. We show that this argument is incorrect even in the extreme situation of estimating the probabilities of events so rare that they have never occurred. We show how statistical forecasting models allow us to use empirical data to improve inferences about the probabilities of these events. Our application is estimating the probability that…

时间序列分析在天文学和气象学中的应用AI 解读

Part 1 Time series - theory and methodology: A short history of time series, M.B. Priestley Frequency estimation, E.J. Hannan et al Asymptotic expansion of estimators for diffusions with small noises, N. Yoshida Spatial-temporal spectral analysis with random sampling, I-Shang Chow, Ken-Shin Lii Detection of periodicities, M.B. Priestley Spectral and multivariate probability density estimation of continuous time stationary processes from randomly sampled data, Elias Masry…

非平稳 VAR 中脉冲响应与预测误差方差的渐近性质AI 解读

Estimated impulse responses and forecast error decompositions are shown to be inconsistent at long horizons in unrestricted VARs with some unit roots. Predictions from unrestricted VARs also do not converge to the optimal predictors over long forecast horizons. In contrast, reduced rank regressions produce impulse responses and forecast error variance estimates that are consistent and predictions that are asymptotically optimal, provided the cointegrating rank is correctly…

理性预期、通货膨胀与名义利率AI 解读

There is a substantial empirical literature, beginning with Fama (1975), that utilizes regressions of the inflation rate in a given period on initial interest rates (or inflation differentials on the slope of the initial yield curve) to test the Fisher hypothesis and/or to provide forecasts of inflation. Both uses depend critically on the maintained hypothesis that asset market prices fully incorporate all relevant current information about future yields. This paper will…

以样本外预测表现检验时间序列非线性AI 解读

This article uses a local-information, near-neighbor forecasting methodology as a prediction test for evidence of a noisy, chaotic data-generating process underlying the Divisia monetary-aggregate series. Using a nonparametric method known to perform well with low-dimensional chaotic processes infected by noise, accompanied by a robust test of forecast performance evaluation, the authors compare out-of-sample forecasting accuracy from the local-information method to…

推广用于区域行业间就业预测的贝叶斯向量自回归方法AI 解读

The Bayesian vector autoregression (BVAR) employment-forecast approach is generalized using data for the state of Georgia. This study advances previous regional BVAR approaches by (1) incorporating regional input-output coefficients, (2) using the coefficients both to specify the prior means in one model and to weight the variances of a Minnesota-type prior in a second model, and (3) including final-demand effects and links to national and world economies. Out-of-sample…

一类动态非线性统计模型的估计与预测AI 解读

A class of nonlinear state-space models, characterized by a single source of randomness, is introduced. A special case, the model underpinning the multiplicative Holt-Winters method of forecasting, is identified. Maximum likelihood estimation based on exponential smoothing instead of a Kalman filter, and with the potential to be applied in contexts involving non-Gaussian disturbances, is considered. A method for computing prediction intervals is proposed and evaluated on…

多元时间序列分析基础AI 解读

This study is devoted to the analysis of multivariate time series data. Such data might arise in business and economics, engineering, geophysical sciences, agriculture, and many other fields. The emphasis is on providing an account of the basic concepts and methods which are useful in analyzing such data. The book presupposes a familiarity with univariate time series as might be gained from one term of a graduate course, but it is otherwise self-contained. It covers the…

摘要暂缺

非对称损失下的最优预测AI 解读

Prediction problems involving asymmetric loss functions arise routinely in many fields, yet the theory of optimal prediction under asymmetric loss is not well developed. We study the optimal prediction problem under general loss structures and characterize the optimal predictor. We compute the optimal predictor analytically in two leading tractable cases and show how to compute it numerically in less tractable cases. A key theme is that the conditionally optimal forecast is…

金融利差能否预测欧盟国家的通胀与产出增长？AI 解读

This paper seeks to address the policy issue of the usefulness of financial spreads as indicators of future inflation and output growth in the countries of the European Union, placing a particular focus on out-of-sample forecasting performance. Such analysis is of considerable relevance to monetary authorities, given the breakdown of the money/income relation in a number of countries and following increased emphasis of domestic monetary policy on control of inflation…

金融利差能否预测欧盟国家的通胀与产出增长？AI 解读

This paper seeks to address the policy issue of the usefulness of financial spreads as indicators of future inflation and output growth in the countries of the European Union, placing a particular focus on out-of-sample forecasting performance. Such analysis is of considerable relevance to monetary authorities, given the breakdown of the money/income relation in a number of countries and following increased emphasis of domestic monetary policy on control of inflation…

动态渐近理想模型与有限阶逼近AI 解读

The authors extend W. A. Barnett and A. Jonas's (1983) asymptotically ideal model (AIM) to model for the possibility that the data were generated by a dynamic process. Prediction errors for dynamic and static AIM models are compared for various simulated datasets. Monetary data are also used to evaluate the AIM specifications. There is substantial evidence that an AR(1) correction considerably improves the quality of low-order finite approximations of AIM with the cost of…

非负贝叶斯时间序列模型的性质与推广AI 解读

Summary We study the most basic Bayesian forecasting model for exponential family time series, the power steady model (PSM) of Smith, in terms of observable properties of one-step forecast distributions and sample paths. The PSM implies a constraint between location and spread of the forecast distribution. Including a scale parameter in the models does not always give an exact solution free of this problem, but it does suggest how to define related models free of the…

用 TSMARS 建模海表温度的长程依赖、非线性与周期现象AI 解读

We analyze a time series of 20 years of daily sea surface temperatures measured off the California coast. The temperatures exhibit quite complicated features, such as effects on many different time scales, nonlinear effects, and long-range dependence. We show how a time series version of the multivariate adaptive regression splines (MARS) algorithm, TSMARS, can be used to obtain univariate adaptive spline threshold autoregressive models that capture many of the physical…