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资源丰富经济体的财政政策状态AI 解读

ABSTRACT We analyse fiscal policy in resource‐rich economies using a novel Bayesian regime‐switching panel model. The identified regimes capture pro‐ or countercyclical fiscal behaviour by allowing regime‐specific shifts in the average fiscal stance, while the switches between the regimes have the interpretation of changes in fiscal policy. Applying the model to a panel of sixteen oil‐producing economies, we show that fiscal policy has alternated between a procyclical and…

交互固定效应面板中潜在组结构的收缩估计与识别AI 解读

This paper provides a framework for joint shrinkage estimation and identification of latent group structures in panel data models with interactive fixed effects and large number of explanatory variables. The latent structure of the model allows individuals to be classified into a number of groups where the number of groups and/or each individual’s group identity are unknown. A doubly penalized principal component estimation procedure using a pairwise fusion penalty and an…

一般未知因子下共同相关效应回归的横截面自助法AI 解读

The Common Correlated Effects (CCE) approach enjoys considerable popularity for estimating factor-augmented panel data models. A key benefit is that by orthogonalizing the data on the available cross-section averages, the unobserved components are eliminated from the data, regardless of their order(s) of integration. This obviates the need for such knowledge, and makes CCE particularly attractive for macroeconomic applications, where the set of unobservables might contain…

经济增长建模的贝叶斯方法：变量选择与截面依赖AI 解读

This paper introduces a Bayesian framework designed to address two critical econometric challenges in empirical growth studies: variable selection and cross-sectional dependence. We first propose a growth regression model that incorporates cross-sectional dependence through a multi-factor structure and accounts for variable selection with sparse coefficients. We then introduce a new Bayesian Adaptive LASSO estimator specifically designed for this model, capable of performing…

大型贝叶斯矩阵自回归模型AI 解读

High-dimensional matrix-valued time-series are increasingly common in economics and finance. Prominent examples include large cross-region panels and dynamic economic networks. As the dimensions of the matrix grow, conventional approaches based on vector autoregressions—implemented by vectoring the matrix-valued data—become computationally infeasible. We introduce a class of large Bayesian matrix autoregressions (BMARs) that can accommodate time-varying volatility,…

什么解释了国际利率的共同变动？AI 解读

ABSTRACT The international co‐movement of interest rates reflects correlated business‐cycle fluctuations, largely driven by demand shocks. Monetary policy in advanced economies follows domestic mandates—inflation and the output gap—and does not respond to foreign policy shocks. We derive this result from a Bayesian structural panel vector autoregression with informative priors, homogeneity restrictions on contemporaneous relations, a hierarchical Minnesota prior with…

气候敏感度是否因地区而异？变系数方法AI 解读

Global mean surface temperature has been increasing in response to growing greenhouse gas concentrations (IPCC, 2021). While Earth is getting warmer overall, regions that have different local geographical features experience unequal increases in temperature. In this paper, we develop a varying-coefficient dynamic panel data model and use it to measure local climate sensitivity, defined as the increase in temperature in a given location following a doubling of CO2…

共同相关效应估计二十年：给应用研究者的五条经验AI 解读

This article distills the vast literature on Common Correlated Effects (CCE), initiated by the seminal contribution of Pesaran (2006), into five practical lessons. We provide a concise overview of the CCE framework and describe the reasons for its popularity in empirical (macro-) panel data research. The lessons we draw focus on aspects that have received substantial methodological attention, but remain underappreciated in empirical work.

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…

多重结构突变与多因子误差下空间面板模型的收缩估计AI 解读

This study investigates spatial panel data models with a multifactor error structure and multiple structural breaks occurring in the coefficients of both spatial lagged and explanatory variables. While extensive research has addressed cross-sectional dependence in panel data, including approaches that integrate spatial and factor structures within a single framework, few studies account for time-varying model parameters and achieving consistent estimation remains a…

具有时变交互固定效应的时变面板数据模型AI 解读

This paper introduces a time-varying (TV) panel data model with interactive fixed effects where both the coefficients and factor loadings are allowed to change smoothly over time. We propose a local version of the least squares and principal component method to estimate the TV coefficients, TV factor loadings, and common factors simultaneously. We provide a bias-corrected local least squares estimator for the TV coefficients and establish the limiting distributions and…

菲利普斯曲线的断点：来自面板数据的证据AI 解读

ABSTRACT We revisit the Phillips curve, applying new Bayesian panel methods with structural breaks to US and EU disaggregate data. Our approach lets us estimate both the number and timing of breaks and to determine the existence of clusters of industries, cities, or countries whose Phillips curves display similar patterns. We find evidence of a flattening for US sectoral data and among EU countries, particularly poorer ones. Evidence of flattening is weaker for MSA‐level…

结构不稳定下的面板数据协整检验AI 解读

Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the article.The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components.We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran's common correlated effects approach.This…

利用多国非参数分位数因子模型研究增长风险AI 解读

We develop a nonparametric quantile panel regression model.Within each quantile, the quantile function is a combination of linear and nonlinear parts, which we approximate using Bayesian Additive Regression Trees (BART).Cross-sectional information is captured through a conditionally heteroscedastic latent factor.The nonparametric feature enhances flexibility, while the panel feature increases the number of observations in the tails.We develop Bayesian methods for inference…

共同因子生成横截面依赖的高斯面板中渐近一致最强的单位根检验AI 解读

This paper considers testing for unit roots in Gaussian panels with cross-sectional dependence generated by common factors. Within our setup, we can analyze restricted versions of the two prevalent approaches in the literature, that of Moon and Perron (2004, Journal of Econometrics 122, 81–126), who specify a factor model for the innovations, and the PANIC setup proposed in Bai and Ng (2004, Econometrica 72, 1127–1177), who test common factors and idiosyncratic deviations…

目标主成分分析：高维面板数据的迁移学习AI 解读

This paper develops a novel method to estimate a latent factor model for a large target panel with missing observations by optimally using the information from auxiliary panel data sets. We refer to our estimator as target-PCA. Transfer learning from auxiliary panel data allows us to deal with a large fraction of missing observations and weak signals in the target panel. We show that our estimator is more efficient and can consistently estimate weak factors, which are not…

基于独立性准则的面板结构 VAR 统计识别AI 解读

Summary This paper introduces a novel panel approach to structural vector autoregressive analysis. For identification, we impose independence of structural innovations at the pooled level. We demonstrate robustness of the method under cross‐sectional correlation and heterogeneity through simulation experiments. In an empirical application on monetary policy transmission in the Euro area, we find that bond spreads rise significantly after an unexpected monetary tightening.…

共同相关效应回归的横截面 BootstrapAI 解读

The Common Correlated Effects (CCE) methodology is now well established for the analysis of factor-augmented panel data models. Yet, it is often neglected that the pooled variant is biased unless the cross-section dimension (N) of the dataset dominates the time series length (T). This is problematic for inference with typical macroeconomic datasets, where T is often equal or larger than N. In response, we establish in this paper the theoretical foundation of the…

具有因子结构的大型面板的稳健估计AI 解读

This article studies estimation of linear panel regression models with heterogeneous coefficients using a class of weighted least squares estimators, when both the regressors and the error possibly contain a common latent factor structure. Our theory is robust to the specification of such a factor structure because it does not require any information on the number of factors or estimation of the factor structure itself. Moreover, our theory is efficient, in certain…

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…

基于截面相关面板的即时预测AI 解读

Summary This paper builds a mixed‐frequency panel data model for nowcasting economic variables across many countries. The model extends the mixed‐frequency panel vector autoregression (MF‐PVAR) to allow for heterogeneous coefficients and a multifactor error structure to model cross‐sectional dependence. We propose a modified common correlated effects (CCE) estimation technique which performs well in simulations. The model is applied in two distinct settings: nowcasting gross…

动态面板数据模型的间接推断估计AI 解读

This paper proposes an estimator for higher-order dynamic panel models based on the idea of indirect inference by matching the simple within-group estimator with its analytical approximate expectation. The resulting estimator is shown to be consistent and asymptotically normal. For the special case of first-order dynamic panel, the estimator yields numerically the same result from an existing procedure in the literature, but the inference to follow differs and this paper…

分组异质性中的结构突变AI 解读

Generating accurate forecasts in the presence of structural breaks requires careful management of bias-variance tradeoffs. Forecasting panel data under breaks offers the possibility to reduce parameter estimation error without inducing any bias if there exists a regime-specific pattern of grouped heterogeneity. To this end, we develop a new Bayesian methodology to estimate and formally test panel regression models in the presence of multiple breaks and unobserved…

空间面板数据模型的鞍点近似AI 解读

We develop new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator in a spatial panel data model, with fixed effects, time-varying covariates, and spatially correlated errors. Our saddlepoint density and tail area approximation feature relative error of order O(1/(n(T−1))) with n being the cross-sectional dimension and T the time-series dimension. The main theoretical tool is the tilted-Edgeworth technique in a nonidentically distributed setting.…