论文检索

时间序列与金融计量经济学前沿：概览AI 解读

Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have…

含内生变量的平滑转换回归模型矩估计AI 解读

Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, smooth transition regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated…

需求估计的另一种方法：含条件波动的高频航空旅客到达量神经网络回归AI 解读

In this paper we provide an alternative approach to analyze the demand for international tourism in the Balearic Islands, Spain, by using a neural network model that incorporates time-varying conditional volatility. We consider daily air passenger arrivals to Palma de Mallorca, Ibiza and Mahon, which are located in the islands of Mallorca, Ibiza and Menorca, respectively, as a proxy for international tourism demand for the Balearic Islands. Spain is a world leader in terms…

金融与风险管理中的计量建模：概览AI 解读

This paper gives an overview about the sixteen papers included in this special issue. The papers in this special issue cover a wide range of topics. Such topics include discussing a class of tests for correlation, estimation of realized volatility, modeling time series and continuous-time models with long-range dependence, estimation and specification testing of time series models, estimation in a factor model with high-dimensional problems, finite-sample examination of…

Log-ACD 模型准极大似然估计的有限样本性质：澳大利亚股票应用AI 解读

This paper concerns the properties of the Quasi Maximum Likelihood Estimator (QMLE) of the Logarithmic Autoregressive Conditional Duration (Log-ACD) model. Proofs of consistency and asymptotic normality of QMLE for the Log-ACD model with log-normal density are presented. This is an important issue as the Log-ACD is used widely for testing various market microstructure models and effects. Knowledge of the distribution of the QMLE is crucial for purposes of valid inference and…

具有 GARCH 误差的非平稳 ARMA 模型中的自适应估计AI 解读

This paper considers adaptive estimation in nonstationary autoregressive moving average models with the noise sequence satisfyinga generalized autoregressive conditional heteroscedastic process. The locally asymptotic quadratic form of the log-likelihood ratio for the model is obtained. It is shown that the limit experiment is neither LAN nor LAMN, but is instead LABF. For the model with symmetric density of the rescaled error, a new efficiency criterion is established for a…

向量 ARMA-GARCH 模型的渐近理论AI 解读

This paper investigates the asymptotic theory for a vector autoregressive moving average–generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model. The conditions for the strict stationarity, the ergodicity, and the higher order moments of the model are established. Consistency of the quasi-maximum-likelihood estimator (QMLE) is proved under only the second-order moment condition. This consistency result is new, even for the univariate autoregressive…

STAR 与 STAR-GARCH 模型的极大似然估计：理论与蒙特卡洛证据AI 解读

Abstract Theoretical and practical interest in non‐linear time series models, particularly regime switching models, have increased substantially in recent years. Given the abundant research activity in analysing time‐varying volatility through Generalized Autoregressive Conditional Heteroscedasticity (GARCH) processes (see Engle, 1982 ; Bollerslev, 1986 ), it is important to analyse regime switching models with GARCH errors. A popular specification in this class is the…

单整过程的线性与对数变换检验AI 解读

This article proposes nonnested tests of linear and logarithmic transformations of integrated processes against each other, where the innovations of both series follow autoregressive processes. It is shown that the null distributions of the test statistics for both the linear and logarithmic transformations are nonstandard when the processes have no drift, whereas they are asymptotically normal when the processes have positive drift. Monte Carlo experiments and illustrative…

单整过程的线性与对数变换检验AI 解读

This article proposes nonnested tests of linear and logarithmic transformations of integrated processes against each other, where the innovations of both series follow autoregressive processes. It is shown that the null distributions of the test statistics for both the linear and logarithmic transformations are nonstandard when the processes have no drift, whereas they are asymptotically normal when the processes have positive drift. Monte Carlo experiments and illustrative…

将定性调查回答转化为定量预期的方法：澳大利亚制造业应用AI 解读

Abstract This paper analyses and extends alternative procedures for converting qualitative expectations responses to quantitative expectations. A number of conversion procedures is investigated, including the probability model, the time‐varying parameter probability model, and the regression approach. The informational content of the survey expectations is compared with simple time series models. It is found that the expectations models are superior for many series, both in…

时间序列模型适切性检验的蒙特卡洛研究AI 解读

In this article, we conduct a Monte Carlo experiment of several computationally attractive tests of model adequacy in time series analysis. The experiment is designed to examine the small-sample properties of tests of fitted autoregressive and moving average models, to compare the performances of asymptotically equivalent procedures, to consider the effects on estimated significance levels and powers of testing against both appropriate and inappropriate alternatives, to…