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时间序列分析中的非参数多步预测AI 解读

Summary We consider the problem of multistep-ahead prediction in time series analysis by using nonparametric smoothing techniques. Forecasting is always one of the main objectives in time series analysis. Research has shown that non-linear time series models have certain advantages in multistep-ahead forecasting. Traditionally, nonparametric k-step-ahead least squares prediction for non-linear autoregressive AR(d) models is done by estimating E(Xt+k |Xt, …, Xt−d+1) via…

时频曲面估计的贝叶斯变量选择与正则化AI 解读

Summary We describe novel Bayesian models for time–frequency inverse modelling of non-stationary signals. These models are based on the idea of a Gabor regression, in which a time series is represented as a superposition of translated, modulated versions of a window function exhibiting good time–frequency concentration. As a necessary consequence, the resultant set of potential predictors is in general overcomplete—constituting a frame rather than a basis—and hence the…

Determining the impact of poverty on the likelihood of civil conflict in less developed countries is difficult because of omitted variable bias and endogeneity. We use exogenous weather variation -- as measured in satellite vegetation readings -- as an instrumental variable for economic growth in 40 Sub-Saharan African countries during 1983-1999, and estimate that economic growth is strongly negatively related to the incidence of civil conflict: a negative growth shock of 5…

利用广义谱与非线性时间序列模型推断汇率可预测性AI 解读

It is often documented, based on autocorrelation, variance ratio, and power spectrum, that exchange rates approximately follow a martingale process. Because these data check serial uncorrelatedness rather than martingale difference, they may deliver misleading conclusions in favor of the martingale hypothesis when the test statistics are insignificant. In this paper, we explore whether there exists a gap between serial uncorrelatedness and martingale difference for exchange…

AIC 下协整秩估计量的渐近分布AI 解读

We derive the asymptotic distribution of the estimate of the cointegration rank of a multivariate model when Akaike's information criterion is used. It is shown that the use of this criterion is ill-advised given that the estimate is severely upward biased even asymptotically.I thank the editor, Professor Peter Phillips, and three anonymous referees for comments and suggestions that improved this paper significantly. All remaining errors are my own.

一种简单的时间序列正态性检验AI 解读

This paper considers testing for normality for correlated data. The proposed test procedure employs the skewness-kurtosis test statistic, but studentized by standard error estimators that are consistent under serial dependence of the observations. The standard error estimators are sample versions of the asymptotic quantities that do not incorporate any downweighting, and, hence, no smoothing parameter is needed. Therefore, the main feature of our proposed test is its…

近积分模型中回归型季节单位根检验统计量的渐近分布AI 解读

In this paper we derive representations for the limiting distributions of the regression-based seasonal unit root test statistics of Hylleberg, Engle, Granger, and Yoo (1990, Journal of Econometrics 44, 215–238) and Beaulieu and Miron (1993, Journal of Econometrics 55, 305–328), inter alia, when the underlying process displays near seasonal integration. Our results generalize those presented in previous studies by allowing for an arbitrary seasonal periodicity (including the…

Fixed effects estimators of panel models can be severely biased because of the well-known incidental parameters problem. We show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large T. We give bias corrections for averages over the fixed effects, as well as model parameters. We find large bias reductions from using these approaches in examples. We consider asymptotics where T grows with n, as an approximation to the…

This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of nonstationarity in the data. We refer to it as PANIC-Panel Analysis of Nonstationarity in Idiosyncratic and Common components. PANIC can detect whether the nonstationarity in a series is pervasive, or variable-specific, or both. It can determine the number of independent stochastic trends driving the common factors. PANIC also permits valid…

具有共同随机方差的状态空间模型AI 解读

This article considers a combination of the linear Gaussian state space model and the stochastic volatility model. The focus is on the simultaneous estimation of parameters related to the stochastic processes of both the mean and variance parts of the model. Kalman filter and Monte Carlo maximum likelihood methods lead to an elegant estimation procedure for which the simulation error can be made arbitrarily small. The standard asymptotic properties of maximum likelihood…

分数协整的最优残差检验与汇率动态AI 解读

A Lagrange multiplier test of the null hypothesis of cointegration in fractionally cointegrated models is proposed. The test statistic uses fully modified residuals to cancel the endogeneity and serial correlation biases, and standard asymptotic properties apply under the null and under local alternatives. With iid Gaussian errors, the asymptotic Gaussian power envelope of all (unbiased) tests is achieved by the one-sided (two-sided) test. The finite-sample properties are…

最低工资AI 解读

Do moderate increases in the minimum wage reduce employment? If not, do they nevertheless raise wages? To examine these questions, we apply techniques of time series analysis and systems estimation that are commonly used in macroeconomics and finance to five panels of data that contain between 11 and 34 low-wage industries. Our answers are “No” and “Yes,” respectively. We find that increases in the federal minimum wage between 1947 and 1997 have raised average wages in many…

股票价格的持续期依赖AI 解读

This article studies time series dependence in the direction of stock prices by modeling the (instantaneous) probability that a bull or bear market terminates as a function of its age and a set of underlying state variables, such as interest rates. A random walk model is rejected both for bull and bear markets. Although it fits the data better, a generalized autoregressive conditional heteroscedasticity model is also found to be inconsistent with the very long bull markets…

相关回归误差的非参数检测AI 解读

In regression problems it is hard to detect correlated errors since the errors are not observed. In this paper, a nonparametric method is proposed for the detection of correlated errors when the design points are equally spaced. It turns out that the first‐order sample autocovariance of the residuals from the kernel regression estimates provides essential information about correlated errors and its bootstrap is quite effective in implementing such information.

存在异常值时的协整分析AI 解读

The effects of innovational outliers and additive outliers in cointegrated vector autoregressive models are examined and it is analyzed how outliers can be modelled with dummy variables. A Monte Carlo simulation illustrates that additive outliers are more distortionary than innovational outliers, and misspecified dummies may distort inference on the cointegration rank in finite samples. These findings question the common practice in applied cointegration analyses of…

多元长记忆过程的渐近推断结果AI 解读

In this paper, we extend the well-known Sims, Stock and Watson (SSW) (Sims et al. 1990; Econometrica 56, 113-44), analysis on estimation and testing in vector autoregressive process (VARs) with integer unit roots and deterministic components to a more general set-up where non-stationary fractionally integrated (NFI) processes are considered. In particular, we focus on partial VAR models where the conditioning variables are NFI since this is the only finite-lag VAR model…

利用桥采样估计混合模型与马尔可夫转换模型的边际似然AI 解读

This paper discusses the problem of estimating marginal likelihoods for mixture and Markov switching model. Estimation is based on the method of bridge sampling (Meng and Wong 1996; Statistica Sinica 11, 552-86.) where Markov Chain Monte Carlo (MCMC) draws from the posterior density are combined with an i.i.d. sample from an importance density. The importance density is constructed in an unsupervised manner from the MCMC draws using a mixture of complete data posteriors.…

线性检验与平稳性AI 解读

This paper shows that the asymptotic distributions of LM-type linearity tests against Smooth Transition Autoregressive (STAR) models, in the presence of a unit root, are non-standard and using standard χ2 critical values may lead to incorrect inference as the tails of the distribution of tests will be thicker than the χ2. This finding also indicates that one needs to test for stationarity prior to applying linearity tests. Copyright Royal Economic Socciety 2004

误设下均值与方差断点的最小二乘估计及检验AI 解读

In this paper we investigate the consequences of misspecification on the large sample properties of change-point estimators and the validity of tests of the null hypothesis of linearity versus the alternative of a structural break. Specifically this paper concentrates on the interaction of structural breaks in the mean and variance of a time series when either of the two is omitted from the estimation and inference procedures. Our analysis considers the case of a break in…

摘要暂缺

摘要暂缺

Is individual labor income more risky in recessions? This is a difficult question to answer because existing panel data sets are so short. To address this problem, we develop a generalized method of moments estimator that conditions on the macroeconomic history that each member of the panel has experienced. Variation in the cross‐sectional variance between households with differing macroeconomic histories allows us to incorporate business cycle information dating back to…

用于诊断相关结果线性模型正态误差的 Cholesky 残差AI 解读

Despite the widespread popularity of linear models for correlated outcomes (e.g., linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this article we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated marginal residual vector by the Cholesky decomposition of the inverse of the estimated marginal variance…

VAR(1) 过程的 I(2) 模型：问题 04.3.1AI 解读

This problem discusses an I (2) model in the VAR(1) case. The I (2) representation theorem of Johansen (1992) (JRT) holds also for VAR(1) processes. The I (2) model for VAR( k ) processes has been discussed for k ≥ 2 in Johansen (1996, Ch. 9; 1997). We here discuss a parametrization for the I (2) case of VAR(1) that differs from the VAR( k ) model.