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树结构广义自回归条件异方差模型AI 解读

Summary We propose a new generalized autoregressive conditional heteroscedastic (GARCH) model with tree-structured multiple thresholds for the estimation of volatility in financial time series. The approach relies on the idea of a binary tree where every terminal node parameterizes a (local) GARCH model for a partition cell of the predictor space. The fitting of such trees is constructed within the likelihood framework for non-Gaussian observations: it is very different from…

摘要暂缺

长记忆与状态转换AI 解读

The theoretical and empirical econometric literatures on long memory and regime switching have evolved largely independently, as the phenomena appear distinct. We argue, in contrast, that they are intimately related, and we substantiate our claim in several environments, including a simple mixture model, Engle and Lee's (1999) stochastic permanent break model, and Hamilton's (1989) Markov switching model. In particular, we show analytically that stochastic regime switching…

嵌套模型的预测精度相等与预测涵盖检验AI 解读

We examine the asymptotic and finite-sample properties of tests for equal forecast accuracy and encompassing applied to 1-step ahead forecasts from nested parametric models. We first derive the asymptotic distributions of two standard tests and one new test of encompassing. Tables of asymptotically valid critical values are provided. Monte Carlo methods are then used to evaluate the size and power of the tests of equal forecast accuracy and encompassing. The simulations…

This paper employs recently developed techniques for testing hypotheses in cointegrated panels to test the strong version of purchasing power parity for a panel of post Bretton Woods data. We compare results using fully modified and dynamic OLS approaches, and strongly reject the hypothesis. We also introduce a new between-dimension dynamic OLS estimator and find that the between-dimension FMOLS and DOLS estimates of the long-run deviation from purchasing power parity are…

含单位根的门槛自回归模型AI 解读

This paper develops an asymptotic theory of inference for an unrestricted two-regime threshold autoregressive (TAR) model with an autoregressive unit root. We find that the asymptotic null distribution of Wald tests for a threshold are nonstandard and different from the stationary case, and suggest basing inference on a bootstrap approximation. We also study the asymptotic null distributions of tests for an autoregressive unit root, and find that they are nonstandard and…

滞后阶数选择与兼具良好尺寸和功效的单位根检验AI 解读

It is widely known that when there are errors with a moving-average root close to −1, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and the BIC tend to select a truncation lag (k) that is very small. We consider a class of Modified Information Criteria (MIC) with a penalty factor that is sample dependent. It takes into account the fact that the bias in the sum of the autoregressive…

重新审视远期贴水异象：计量经济学证据AI 解读

Abstract Using both semiparametric and parametric estimation methods, this paper corroborates earlier findings of fractionally integrated behaviour in the forward premium. Two new explanations are also proposed to help reconcile earlier conflicting empirical evidence on the time series properties of the forward premium. Traditional regression approaches used to test the forward rate unbiasedness hypothesis are then evaluated, including regression in levels, in returns…

可预测性的度量：理论与宏观经济应用AI 解读

Abstract We propose a measure of predictability based on the ratio of the expected loss of a short‐run forecast to the expected loss of a long‐run forecast. This predictability measure can be tailored to the forecast horizons of interest, and it allows for general loss functions, univariate or multivariate information sets, and covariance stationary or difference stationary processes. We propose a simple estimator, and we suggest resampling methods for inference. We then…

逻辑斯蒂混合自回归模型AI 解读

We generalise the mixture autoregressive, MAR, model to the logistic mixture autoregressive with exogenous variables, LMARX, model for the modelling of nonlinear time series. The models consist of a mixture of two Gaussian transfer function models with the mixing proportions changing over time. The model can also be considered as a generalisation of the self‐exciting threshold autoregressive, SETAR, model and the open‐loop threshold autoregressive, TARSO, model. The…

分数高斯过程的惩罚极大似然估计AI 解读

We apply and extend Firth's (1993) modified score estimator to deal with a class of stationary Gaussian long‐memory processes. Our estimator removes the first‐order bias of the maximum likelihood estimator. A small simulation study reveals the reduction in the bias is considerable, while it does not inflate the corresponding mean squared error.

保序回归：变点问题的另一种视角AI 解读

A test based on isotonic regression is developed for monotonic trends in short range dependent sequences and is applied to Argentina rainfall data and global warming data. This test provides another perspective for changepoint problems. The isotonic test is shown to be more powerful than some existing tests for trend.

连续时间半鞅回归模型中的参数序贯估计AI 解读

We consider the problem of parameter estimation for multidimensional continuous-time linear stochastic regression models with an arbitrary finite number of unknown parameters and with martingale noise. The main result of the paper claims that the unknown parameters can be estimated with prescribed mean-square precision in this general model providing a unified description of both discrete and continuous time process. Among the conditions on the regressors there is one…

根接近1的自回归时间序列参数估计AI 解读

Estimators for the parameters of autoregressive time series are compared, emphasizing processes with a unit root or a root close to 1. The approximate bias of the sum of the autoregressive coefficients is expressed as a function of the test for a unit root. This expression is used to construct an estimator that is nearly unbiased for the parameter of the first-order scalar process. The estimator for the first-order process has a mean squared error that is about 40% of that…

马尔可夫状态转换与单位根检验AI 解读

We investigate the power and size performance of unit-root tests when the data undergo Markov regime switching. All tests, including those robust to a single break in trend growth rate, have low power against a process with a Markov-switching trend. Under the null hypothesis, we find that previously documented size distortions in Dickey-Fuller-type tests caused by a single break in trend growth rate or variance do not generalize to most parameterizations of Markov switching…

克服基于 ARIMA 模型的信号提取中分解不可容许的问题AI 解读

We analyze the situation in which the decomposition of a time series into orthogonal balanced components as performed by the AR IMA-model-based (AMB) method is nonadmissible. We show that considering top-heavy models for the components can solve the problem. The top-heavy decomposition is derived and the improvement achieved is illustrated by an application to a class of models often encountered in practice. Two empirical applications allow us to draw a comparison with the…

美国牺牲率的结构估计AI 解读

This article investigates the statistical properties of the U.S. sacrifice ratio--the cumulative output loss arising from a permanent reduction in inflation. We derive estimates of the sacrifice ratio from three structural vector autoregression models and then conduct a series of simulation exercises to analyze their sampling distribution. We obtain point estimates of the sacrifice ratio that are consistent with results reported in earlier studies. However, the estimates are…

协整向量自回归模型长短期关系的识别与划分AI 解读

We show that the usual rank condition is necessary and sufficient to identify a vector autoregressive process whether the variables are I (0) or I ( d ) for d = 1,2,.... We then use this rank condition to demonstrate the interdependence between the identification of short-run and long-run relations of cointegrated process. We find that both the short-run and long-run relations can be identified without the existence of prior information to identify either relation. But if…

复单位根与经济周期：它们真实存在吗？AI 解读

In this paper the asymptotic properties of ARMA processes with complex-conjugate unit roots in the AR lag polynomial are studied. These processes behave quite differently from regular unit root processes (with a single root equal to one). In particular, the asymptotic properties of a standardized version of the periodogram for such processes are analyzed, and a nonparametric test of the complex unit root hypothesis against the stationarity hypothesis is derived. This test is…

摘要暂缺

检验可能存在依赖的过程是否不相关AI 解读

An analysis is presented of a new testing procedure for the null hypothesis that a stochastic process is uncorrelated when the process is possibly dependent. Unlike with existing procedures, the user does not need to choose any arbitrary number to implement the proposed test. The asymptotic null distribution of the proposed test statistic is not standard, but it is tabulated by means of simulations. The test is compared with two alternative test procedures that require…

混合自回归条件异方差模型AI 解读

We propose a mixture autoregressive conditional heteroscedastic (MAR-ARCH) model for modeling nonlinear time series. The models consist of a mixture of K autoregressive components with autoregressive conditional heteroscedasticity; that is, the conditional mean of the process variable follows a mixture AR (MAR) process, whereas the conditional variance of the process variable follows a mixture ARCH process. In addition to the advantage of better description of the…

随机神经网络及其在非线性时间序列中的应用AI 解读

We consider a variant of the conventional neural network model, called the stochastic neural network, that can be used to approximate complex nonlinear stochastic systems. We show how the expectation-maximization algorithm can be used to develop efficient estimation schemes that have much lower computational complexity than those for conventional neural networks. This enables us to carry out model selection procedures, such as the Bayesian information criterion, to choose…

非线性模型中的简便稳健假设检验AI 解读

We develop test statistics to test hypotheses in nonlinear weighted regression models with serial correlation or conditional heteroscedasticity of unknown form. The novel aspect is that these tests are simple and do not require the use of heteroscedasticity autocorrelationconsistent (HAC) covariance matrix estimators. This new class of tests uses stochastic transformations to eliminate nuisance parameters as a substitute for consistently estimating the nuisance parameters.…