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平稳长记忆高斯时间序列中 Whittle 最大似然估计量的有效 Edgeworth 展开AI 解读

In this paper, we prove the validity of an Edgeworth expansion to the distribution of the Whittle maximum likelihood estimator for stationary long-memory Gaussian models with unknown parameter . The error of the ( s − 2)-order expansion is shown to be o ( n −( s −2)/2 )—the usual independent and identically distributed rate—for a wide range of models, including the popular ARFIMA( p , d , q ) models. The expansion is valid under mild assumptions on the behavior of the…

部分、弱与强识别下的广义经验似然估计与检验AI 解读

The purpose of this paper is to describe the performance of generalized empirical likelihood (GEL) methods for time series instrumental variable models specified by nonlinear moment restrictions as in Stock and Wright (2000, Econometrica 68, 1055–1096) when identification may be weak. The paper makes two main contributions. First, we show that all GEL estimators are first-order equivalent under weak identification. The GEL estimator under weak identification is inconsistent…

含单位根与协整的短面板向量自回归：估计与推断AI 解读

This paper considers estimation and inference in panel vector autoregressions where (i) the individual effects are either random or fixed, (ii) the time-series properties of the model variables are unknown a priori and may feature unit roots and cointegrating relations, and (iii) the time dimension of the panel is short and its cross-sectional dimension is large. Generalized method of moments (GMM) and quasi maximum likelihood (QML) estimators are obtained and compared in…

不同观测频率时间序列中的协整向量估计AI 解读

We discuss the effects of temporal aggregation on the estimation of cointegrating vectors and on testing linear restrictions on this vector. We adopt a discrete time approach and demonstrate, in contrast with the findings of Chambers (2003, Econometric Theory 19, 49–77), who adopts a continuous time approach, that in some situations, when the regressand must be aggregated, systematic sampling is preferable to average sampling for estimation purposes. Like Chambers, we show…

刻画高持续性波动的非线性GARCH模型AI 解读

In this paper we study a new class of nonlinear GARCH models. Special interest is devoted to models that are similar to previously introduced smooth transition GARCH models except for the novel feature that a lagged value of conditional variance is used as the transition variable. This choice of the transition variable is mainly motivated by the desire to find useful models for highly persistent volatility. The underlying idea is that high persistence in conditional variance…

含跳跃仿射随机波动率模型推断的自适应 MCMC 方法AI 解读

In this paper we propose an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate stochastic volatility models with jumps and affine structure. Our idea relies on the use of adaptive methods that aim at reducing the asymptotic variance of the estimates. We focus on the Delayed Rejection algorithm in order to find accurate proposals and to efficiently simulate the volatility path. Furthermore, Bayesian model selection is addressed through the use of reduced runs of…

利用多变量结构时间序列模型进行时间分解AI 解读

In this paper, we provide a multivariate framework for temporal disaggregation of time series observed at a given frequency into higher frequency time series. The suggested method uses the seemingly unrelated time series equations model and it is estimated by the Kalman filter. The methodology is flexible enough to allow for almost any kind of temporal disaggregation problems of both raw and seasonally adjusted time series. Comparisons with other temporal disaggregation…

允许结构突变的面板平稳性检验：人均GDP应用AI 解读

This paper proposes a test statistic for the null hypothesis of panel stationarity that allows for the presence of multiple structural breaks. Two different specifications are considered depending on the structural breaks affecting the individual effects and/or the time trend. The model is flexible enough to allow the number of breaks and their position to differ across individuals. The test is shown to have a standard normal limit distribution with a good finite sample…

含单位根动态回归模型中最小二乘估计量的矩近似AI 解读

To find approximations for bias, variance and mean-squared error of least-squares estimators for all coefficients in a linear dynamic regression model with a unit root, we derive asymptotic expansions and examine their accuracy by simulation. It is found that in this particular context useful expansions exist only when the autoregressive model contains at least one non-redundant exogenous explanatory variable. Surprisingly, the large-sample and small-disturbance asymptotic…

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波动率长记忆的估计AI 解读

We consider semiparametric estimation of the memory parameter in a model that includes as special cases both long-memory stochastic volatility and fractionally integrated exponential GARCH (FIEGARCH) models. Under our general model the logarithms of the squared returns can be decomposed into the sum of a long-memory signal and a white noise. We consider periodogram-based estimators using a local Whittle criterion function. We allow the optional inclusion of an additional…

动态似不相关协整回归AI 解读

We propose the parametric Dynamic Seemingly Unrelated Regression (DSUR) estimator for simultaneous estimation of multiple cointegrating regressions. DSUR is efficient when the equilibrium errors are correlated across equations and is applicable for panel cointegration estimation in environments where the cross section is small relative to the available time series. We study the asymptotic and small sample properties of the DSUR estimator for both heterogeneous and…

随机效用框架中的参数演化建模AI 解读

Random utility models have become standard econometric tools, allowing parameter inference for individual-level categorical choice data. Such models typically presume that changes in observed choices over time can be attributed to changes in either covariates or unobservables. We study how choice dynamics can be captured more faithfully by also directly modeling temporal changes in parameters, using a vector autoregressive process and Bayesian estimation. This approach…

汇率与马尔可夫状态转换动态AI 解读

This article presents a systematic and extensive empirical study on the presence of Markov switching dynamics in three dollar-based exchange rates. A Monte Carlo approach is adopted to circumvent the statistical inference problem inherent to the test of regime-switching behavior. Two data frequencies, two sample periods, and various specifications are considered. Quarterly data yield inconclusive evidence; the test rejects neither random walk nor Markov switching. Monthly…

水平跃迁与经济时间序列中的长记忆假象AI 解读

When applied to time series processes containing occasional level shifts, the log-periodogram (GPH) estimator often erroneously finds long memory. For a stationary short-memory process with a slowly varying level, I show that the GPH estimator is substantially biased, and derive an approximation to this bias. The asymptotic bias lies on the (0, 1) interval, and its exact value depends on the ratio of the expected number of level shifts to a user-defined bandwidth parameter.…

Bass扩散模型的计量经济学问题AI 解读

The parsimonious Bass diffusion model is frequently used to forecast adoptions of new products and to compare the life cycles of specific products across countries. To meet these goals, reliable parameter estimates are needed. We develop the asymptotic theory for the three key parameters in the Bass model. For this purpose, we need to make assumptions about the stochastic error process. On doing so, we arrive at an alternative version of the Bass model than the one usually…

一类新的多元偏斜密度及其在GARCH模型中的应用AI 解读

We propose a practical and flexible method to introduce skewness in multivariate symmetric distributions. Applying this procedure to the multivariate Student density leads to a “multivariate skew-Student” density in which each marginal has a specific asymmetry coefficient. Combined with a multivariate generalized autoregressive conditional heteroscedasticity model, this new family of distributions is found to be more useful than its symmetric counterpart for modeling stock…

马尔可夫状态切换向量误差修正模型中的货币政策AI 解读

Monetary policy vector autoregressions (VARs) typically presume stability of the long-run outcomes. We introduce the possibility of switches in the long-run equilibrium in a cointegrated VAR by allowing both the covariance matrix and weighting matrix in the error-correction term to switch. We find that monetary policy alternates between sustaining long-run growth and disinflationary regimes. Allowing state changes can also help explain the price puzzle and justify the use of…

TSMod 4.03 使用导览AI 解读

Abstract We review the time series econometrics package TSMod. The new features in TSMod 4.03, released in April 2004, are described and its potential for teaching is analysed. Copyright © 2005 John Wiley & Sons, Ltd.

主成分的应用：利用大型数据集分析货币政策AI 解读

Abstract The empirical analysis of monetary policy requires the construction of instruments for future expected inflation. Dynamic factor models have been applied rather successfully to inflation forecasting. In fact, two competing methods have recently been developed to estimate large‐scale dynamic factor models based, respectively, on static and dynamic principal components. This paper combines the econometric literature on dynamic principal components and the empirical…

马尔可夫切换因果性与货币—产出关系AI 解读

Abstract The causal link between monetary variables and output is one of the most studied issues in macroeconomics. One puzzle from this literature is that the results of causality tests appear to be sensitive with respect to the sample period that one considers. As a way of overcoming this difficulty, we propose a method for analysing Granger causality which is based on a vector autoregressive model with time‐varying parameters. We model parameter time‐variation so as to…

日本低利率政策下货币需求估计的总量与分项分析悖论AI 解读

Abstract We use Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of a liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and…

ARCH(1) 误差下 AR(1) 过程的加权最小绝对偏差估计AI 解读

The weighted least absolute deviations estimator is studied for an AR(1) process with ARCH(1) errors εt. Unlike for the quasi maximum likelihood estimator, the estimator's, limiting distribution is shown to be normal even when E(εt4) = ∞. Furthermore, the estimator can be applied to examine the symmetry of the density of εt and to estimate the quantity E(log |α + λ½ εt|), which are of crucial importance for conducting asymptotic inference for quasi maximum likelihood…