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面板数据预测：估计不确定性与参数异质性AI 解读

We provide a comprehensive examination of the predictive accuracy of panel forecasting methods based on individual, pooling, fixed effects, and empirical Bayes estimation, and propose optimal weights for forecast combination schemes. We consider linear panel data models, allowing for weakly exogenous regressors and correlated heterogeneity. We quantify the gains from exploiting panel data and demonstrate how forecasting performance depends on the degree of parameter…

参数不稳定下高维线性回归的变量选择AI 解读

This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigate the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating…

含个体、时间及交互效应的短期动态面板模型AI 解读

Summary This paper proposes a transformed quasi‐maximum likelihood (TQML) estimator for short dynamic fixed effects panel data models allowing for interactive effects through a multifactor error structure. The proposed estimator is robust to the heterogeneity of the initial values and common unobserved effects, while at the same time allowing for standard fixed and time effects. It is applicable to both stationary and unit root cases. The order condition for identification…

利用报纸报道识别制裁对伊朗经济的影响AI 解读

Summary This paper focuses on the identification and quantitative estimation of sanctions on the Iranian economy over the period 1989–2019. It provides a new time series approach and proposes a novel measure of sanctions intensity based on daily newspaper coverage. In absence of sanctions, Iran's average annual growth could have been around 4–5%, as compared to the 3% realized. Estimates of the proposed sanctions‐augmented structural VAR show that sanctions significantly…

重印：异质面板中的单位根检验AI 解读

This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized t-bar test statistic based on the (augmented) Dickey–Fuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension) →∞. A diagonal…

摘要暂缺

因子强度的测量：理论与实践AI 解读

Abstract This paper proposes an estimator of factor strength and establishes its consistency and asymptotic distribution. The estimator is based on the number of statistically significant factor loadings, taking multiple testing into account. Both cases of observed and unobserved factors are considered. The small sample properties of the proposed estimator are investigated using Monte Carlo experiments. It is shown that the proposed estimation and inference procedures…

高维线性回归变量选择的逐协变量多重检验方法AI 解读

This paper provides an alternative approach to penalized regression for model selection in the context of high‐dimensional linear regressions where the number of covariates is large, often much larger than the number of available observations. We consider the statistical significance of individual covariates one at a time, while taking full account of the multiple testing nature of the inferential problem involved. We refer to the proposed method as One Covariate at a Time…

公共债务对产出增长存在门槛效应吗？AI 解读

This paper studies the relationship between public debt expansion and economic growth and investigates whether the debt-growth relation varies with the level of indebtedness. We contribute theoretically by developing tests for threshold effects in the context of dynamic heterogeneous panel data models with cross-sectionally dependent errors. In the empirical application, using data on a sample of forty countries over the 1965–2010 period, we find no evidence for a…

截面依赖指数：估计与推断AI 解读

Summary This paper provides a characterisation of the degree of cross‐sectional dependence in a two dimensional array, { x i t , i = 1,2,... N ; t = 1,2,..., T } in terms of the rate at which the variance of the cross‐sectional average of the observed data varies with N . Under certain conditions this is equivalent to the rate at which the largest eigenvalue of the covariance matrix of x t =( x 1 t , x 2 t ,..., x N t )′ rises with N . We represent the degree of…

利用采购经理指数预测产出增长的多国方法AI 解读

This paper derives new theoretical results for forecasting with Global VAR (GVAR) models. It is shown that the presence of strong unobserved common factors can lead to an undetermined GVAR model. To solve this problem, we propose augmenting the GVAR with additional proxy equations for the strong factors and establish conditions under which forecasts from the augmented GVAR model (AugGVAR) uniformly converge in probability (as the panel dimensions N,T→j∞) to the infeasible…

大型动态面板中的聚合AI 解读

This paper investigates the problem of aggregation in the case of large linear dynamic panels, where each micro unit is potentially related to all other micro units, and where micro innovations are allowed to be cross sectionally dependent. Following Pesaran (2003), an optimal aggregate function is derived and used (i) to establish conditions under which Granger’s (1980) conjecture regarding the long memory properties of aggregate variables from ‘a very large scale dynamic,…

存在结构突变时的最优预测AI 解读

This paper considers the problem of forecasting under continuous and discrete structural breaks and proposes weighting observations to obtain optimal forecasts in the MSFE sense. We derive optimal weights for one step ahead forecasts. Under continuous breaks, our approach largely recovers exponential smoothing weights. Under discrete breaks, we provide analytical expressions for optimal weights in models with a single regressor, and asymptotically valid weights for models…

多因子误差结构下的面板单位根检验AI 解读

This paper extends the cross-sectionally augmented panel unit root test (CIPS) proposed by Pesaran (2007) to the case of a multifactor error structure, and proposes a new panel unit root test based on a simple average of cross-sectionally augmented Sargan–Bhargava statistics (CSB). The basic idea is to exploit information regarding the m unobserved factors that are shared by k observed time series in addition to the series under consideration. Initially, we develop the tests…

多期预测中的变量选择、估计与推断AI 解读

This paper conducts a broad-based comparison of iterated and direct multi-period forecasting approaches applied to both univariate and multivariate models in the form of parsimonious factor-augmented vector autoregressions. To account for serial correlation in the residuals of the multi-period direct forecasting models we propose a new SURE-based estimation method and modified Akaike information criteria for model selection. Empirical analysis of the 170 variables studied by…

无限维 VAR 与因子模型AI 解读

This paper proposes a novel approach for dealing with the 'curse of dimensionality' in the case of infinite-dimensional vector autoregressive (IVAR) models. It is assumed that each unit or variable in the IVAR is related to a small number of neighbors and a large number of non-neighbors. The neighborhood effects are fixed and do not change with the number of units (N), but the coefficients of non-neighboring units are restricted to vanish in the limit as N tends to infinity.…

跨估计窗口的预测组合AI 解读

In this article we consider combining forecasts generated from the same model but over different estimation windows. We develop theoretical results for random walks with breaks in the drift and volatility and for a linear regression model with a break in the slope parameter. Averaging forecasts over different estimation windows leads to a lower bias and root mean square forecast error (RMSFE) compared with forecasts based on a single estimation window for all but the…

弱与强横截面依赖及大面板估计AI 解读

This paper introduces the concepts of time‐specific weak and strong cross‐section dependence, and investigates how these notions are related to the concepts of weak, strong and semi‐strong common factors, frequently used for modelling residual cross‐section correlations in panel data models. It then focuses on the problems of estimating slope coefficients in large panels, where cross‐section units are subject to possibly a large number of unobserved common factors. It is…

具有非平稳多因子误差结构的面板AI 解读

The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently, work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference in the case of stationary panel regressions with a multifactor error structure. This paper extends this work and examines the important case where the unobservable common factors follow unit root processes. The extension to I(1)…

具有序列相关的多类别变量之间的依赖检验AI 解读

The contingency table literature on tests for dependence among discrete multicategory variables is extensive. Standard tests assume, however, that draws are independent and only limited results exist on the effect of serial dependency—a problem that is important in areas such as economics, finance, medical trials, and meteorology. This article proposes new tests of independence based on canonical correlations from dynamically augmented reduced rank regressions. The tests…

存在截面依赖时的简单面板单位根检验AI 解读

Abstract A number of panel unit root tests that allow for cross‐section dependence have been proposed in the literature that use orthogonalization type procedures to asymptotically eliminate the cross‐dependence of the series before standard panel unit root tests are applied to the transformed series. In this paper we propose a simple alternative where the standard augmented Dickey–Fuller (ADF) regressions are augmented with the cross‐section averages of lagged levels and…

欧元区的国际联系：全球 VAR 分析AI 解读

Abstract This paper presents a quarterly global model combining individual country vector error‐correcting models in which the domestic variables are related to the country‐specific foreign variables. The global VAR (GVAR) model is estimated for 26 countries, the euro area being treated as a single economy, over the period 1979–2003. It advances research in this area in a number of directions. In particular, it provides a theoretical framework where the GVAR is derived as an…

摘要暂缺

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…