论文检索

单指标系数回归模型AI 解读

In this article we investigate a class of single-index coefficient regression models under dependence. This includes many existing models, such as the smooth transition threshold autoregressive (STAR) model of Chan and Tong, the functional-coefficient autoregressive (FAR) model of Chen and Tsay, and the single-index model of Ichimura. Compared to the varying-coefficient model of Hastie and Tibshirani, our model can avoid the curse of dimensionality in multivariate…

脑电轨迹的评估与比较：非平稳时间序列中的潜在结构AI 解读

We explore and illustrate the use of time series decomposition methods for evaluating and comparing latent structure in nonstationary electroencephalographic (EEG) traces obtained from depressed patients during brain seizures induced as part of electroconvulsive therapy (ECT). Analysis of the patterns of change over time in the frequency structure of such EEG data provides insight into the neurophysiological mechanisms of action of this effective but poorly understood…

GARCH 模型中基于最大似然的估计量效率比较AI 解读

In this paper, we investigate the loss of asymptotic efficiency of semiparametric and quasi-maximum likelihood estimators relative to maximum likelihood estimators in models with Generalized Autoregressive Conditional Heteroscedasticity (GARCH). For a general time-varying location-scale model, the factors that contribute to differences in efficiency among the estimators can be divided in two categories. One pertains to the parametric specifications of the conditional mean…

随机永久突变AI 解读

This paper bridges the gap between processes where shocks are permanent and those with transitory shocks by formulating a process in which the long-run impact of each innovation is time-varying and stochastic. In the stochastic permanent breaks (STOPBREAK) process, frequent transitory shocks are supplemented by occasional permanent shifts. Consistency and asymptotic normality of quasi-maximum-likelihood estimates is established, and locally best hypothesis tests of the null…

变系数模型中的统计估计AI 解读

Varying coefficient models are a useful extension of classical linear models. They arise naturally when one wishes to examine how regression coefficients change over different groups characterized by certain covariates such as age. The appeal of these models is that the coef .cient functions can easily be estimated via a simple local regression.This yields a simple one-step estimation procedure. We show that such a one-step method cannot be optimal when different coefficient…

含马尔可夫转换与时变增长的GDP不可观测成分模型：贝叶斯分析AI 解读

We propose an unobserved-component time series model of gross domestic product that includes Markov switching as an unobserved component. In addition to a trend component, the model has two time-varying drift components. One drift represents the expected rate of growth during recession; the other drift represents the expected rate during expansion. Estimates indicate a substantial decline in the latter annual rate for the United States from 6.4% in 1950 to 3.6% by 1990. We…

定性响应时间序列的条件异方差：银行最优惠贷款利率的吉布斯抽样分析AI 解读

Previous time series applications of qualitative response models have ignored features of the data, such as conditional heteroscedasticity, that are routinely addressed in time series econometrics of financial data. This article addresses this issue by adding Markov-switching heteroscedasticity to a dynamic ordered probit model of discrete changes in the bank prime lending rate and estimating via the Gibbs sampler. The dynamic ordered probit model of Eichengreen, Watson, and…

具有时变系数的协整回归AI 解读

This paper considers cointegrating regressions with time varying coefficients. The coefficients are modeled as smooth functions evolving over time. It is shown that they can be estimated nonparametrically, using suitably modified series estimators. Presented is the efficient method of estimation, which relies on simple prefiltering of the data and preestimation of the model. The test for the adequacy of model specification is also developed. Our model and statistical methods…

纵向数据的半参数加性回归模型AI 解读

In previous work we have studied a nonparametric additive time-varying regression model for longitudinal data recorded at irregular intervals. The model allows the influence of each covariate to vary separately with time. For small datasets, however, only a limited number of covariates may be handled in this way. In this paper, we introduced a semiparametric regression model for longitudinal data. The influence of some of the covariates varies nonparametrically with time…

时变暴露对重复二元结局边际均值的因果效应估计AI 解读

We provide sufficient conditions for estimating from longitudinal data the causal effect of a time-dependent exposure or treatment on the marginal probability of response for a dichotomous outcome. We then show how one can estimate this effect under these conditions using the g-computation algorithm of Robins. We also derive the conditions under which some current approaches to the analysis of longitudinal data, such as the generalized estimating equations (GEE) approach of…

摘要暂缺

贝叶斯套利阈值分析AI 解读

A Bayesian estimation procedure is developed for estimating multiple-regime (multiple-threshold) error-correction models appropriate for deviations from financial arbitrage relationships. This approach has clear advantages over classical stepwise threshold autoregressive analysis. Unlike many other applications of threshold models, the knowledge of some costs involved in setting up arbitrage positions allows us to specify an informative prior. To illustrate the Bayesian…

失业流动的时间序列证据：样本时期的重要性AI 解读

Worker flows to and from unemployment occur at all stages of the U.S. business cycle. The flows' dynamics affect the dynamics of the unemployment rate. This article studies the dynamics of unemployment flows and the unemployment rate and explores and documents changes over time. Rigorous diagnostic tests are used to investigate the structural stability of projectional relationships involving flows and the unemployment rate.

脑电轨迹的评估与比较：非平稳时间序列中的潜在结构AI 解读

We explore and illustrate the use of time series decomposition methods for evaluating and comparing latent structure in nonstationary electroencephalographic (EEG) traces obtained from depressed patients during brain seizures induced as part of electroconvulsive therapy (ECT). Analysis of the patterns of change over time in the frequency structure of such EEG data provides insight into the neurophysiological mechanisms of action of this effective but poorly understood…

脑电轨迹的评估与比较：非平稳时间序列中的潜在结构AI 解读

We explore and illustrate the use of time series decomposition methods for evaluating and comparing latent structure in nonstationary electroencephalographic (EEG) traces obtained from depressed patients during brain seizures induced as part of electroconvulsive therapy (ECT). Analysis of the patterns of change over time in the frequency structure of such EEG data provides insight into the neurophysiological mechanisms of action of this effective but poorly understood…

通胀预测中的信息差异化解读AI 解读

We test the hypothesis associated with a standard assumption in the theoretical literature on learning: that economic agents interpret information identically. We use a data set based on a survey of Israeli business executives forecasting future inflation. One of the main advantages of using this data is that a major change in the inflation regime in 1985 can be treated as a natural experiment in new beliefs formation. We develop a methodology for testing this hypothesis and…

资产收益波动率的单位根检验AI 解读

It is now well established that the volatility of asset returns is time varying and highly persistent. One leading model that is used to represent these features of the data is the stochastic volatility model. The researcher may test for non‐stationarity of the volatility process by testing for a unit root in the log‐squared time series. This strategy for inference has many advantages, but is not followed in practice because these unit root tests are known to have very poor…

资产收益波动率的单位根检验AI 解读

It is now well established that the volatility of asset returns is time varying and highly persistent. One leading model that is used to represent these features of the data is the stochastic volatility model. The researcher may test for non-stationarity of the volatility process by testing for a unit root in the log-squared time series. This strategy for inference has many advantages, but is not followed in practice because these unit root tests are known to have very poor…

存在异常值时的平滑转换非线性检验AI 解读

Regime-switching models, like the smooth transition autoregressive (STAR) model, are typically applied to time series of moderate length. Hence, the nonlinear features that these models intend to describe may be reflected in only a few observations. Conversely, neglected outliers in a linear time series of moderate length may incorrectly suggest STAR (or other) type(s of) nonlinearity. In this article, the authors propose outlier robust tests for STAR-type nonlinearity.…

多变量纵向计数数据的状态空间模型AI 解读

We propose a nonstationary state space model for multivariate longitudinal count data driven by a latent gamma Markov process. The Poisson counts are assumed to be conditionally independent given the latent process, both over time and across categories. We consider a regression model where time-varying covariates may enter via either the Poisson model or the latent gamma process. Estimation is based on the Kalman smoother, and we consider analysis of residuals from both the…

刻画变点最大似然估计量的分布行为AI 解读

We consider the problem of estimating the unknown changepoint in a sequence of time-ordered observations. Upper and lower bounds are derived for the asymptotic distribution of the maximum likelihood estimator and methods of approximation are suggested. A computationally efficient algorithm is presented for deriving the bounds and approximations for the asymptotic probabilities of the maximum likelihood estimator when the parameters before and after the changepoint are…

利用变系数模型进行模型诊断AI 解读

The paper motivates the use of varying coefficient models for diagnostics in regression models with continuous and factorial covariates. The varying coefficient model, which is restricted only by assumptions concerning the smoothness of the effects, is considered as the alternative to a parametric generalised linear model. Estimation is based on local likelihood smoothing and asymptotic properties of the estimators are derived. For the investigation of the discrepancy…

实际利率的时变行为：对近期证据的重新评估AI 解读

A time-varying parameter model with Markov-switching conditional heteroscedasticity is employed to investigate two sources of shifts in real interest rates: (1) shifts in the coefficients relating the ex ante real rate to the nominal rate, the inflation rate and a supply shock variable and (2) unconditional shifts in the variance of the stochastic process. The results underscore the importance of modelling continual change in the ex ante real rate in terms of other economic…

实际利率的时变行为：重新评估近期证据AI 解读

A time-varying parameter model with Markov-switching conditional heteroscedasticity is employed to investigate two sources of shifts in real interest rates: (1) shifts in the coefficients relating the ex ante real rate to the nominal rate, the inflation rate and a supply shock variable and (2) unconditional shifts in the variance of the stochastic process. The results underscore the importance of modelling continual change in the ex ante real rate in terms of other economic…