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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…

贝叶斯 DSGE 模型的识别问题AI 解读

This article is concerned with local identification of individual parameters of dynamic stochastic general equilibrium (DSGE) models estimated by Bayesian methods. Identification is often judged by a comparison of the posterior distribution of a parameter with its prior. However, these can differ even when the parameter is not identified. Instead, we propose two Bayesian indicators of identification. The first follows a suggestion by Poirier of comparing the posterior…

非连续价格调整：微观计量分析AI 解读

Based on a reduced-form state-dependent pricing model with random thresholds, we specify and estimate a nonlinear panel data model with an unobserved factor representing the common cost or demand components of the unobserved optimal price. Using this model, we are able to assess the relative importance of common and idiosyncratic shocks in explaining the frequency and magnitude of price changes in the case of a wide variety of consumer products in Belgium and France. We find…

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.…

具有共同因子与空间相关的大型面板AI 解读

This paper considers methods for estimating the slope coefficients in large panel data models that are robust to the presence of various forms of error cross-section dependence. It introduces a general framework where error cross-section dependence may arise because of unobserved common effects and/or error spill-over effects due to spatial or other forms of local dependencies. Initially, this paper focuses on a panel regression model where the idiosyncratic errors are…

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…

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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)…

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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…

This paper proposes a bias-adjusted version of Breusch and Pagan (1980) Lagrange multiplier (LM) test statistic of error cross-section independence, in the case of panel models with strictly exogenous regressors and normal errors. The exact mean and variance of the test indicator of the LM test statistic are provided for the purpose of the bias-adjustments. It is shown that the centring of the LM statistic is correct for fixed T and N. Importantly, the proposed bias-adjusted…

大规模面板中的斜率同质性检验AI 解读

This paper proposes a modified version of Swamy’s test of slope homogeneity for panel data models where the cross section dimension (N) could be large relative to the time series dimension (T). We exploit the cross section dispersion of individual slopes weighted by their relative precision. Using Monte Carlo experiments, we show that the test has the correct size and satisfactory power in panels with strictly exogenous regressors for various combinations of N and T. For…

《应用计量经济学杂志》博士论文奖AI 解读

The JAE Data Archive directory is available at http://qed.econ.queensu.ca/jae/datasets/ . Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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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…

面板数据模型中的异质性与截面依赖：理论与应用专题导言AI 解读

Abstract The papers included in this special issue are primarily concerned with the problem of cross section dependence and heterogeneity in the analysis of panel data models and their relevance in applied econometric research. Cross section dependence can arise due to spatial or spill over effects, or could be due to unobserved (or unobservable) common factors. Much of the recent research on non‐stationary panel data have focussed on this problem. It was clear that the…

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…

具有多因子误差结构的大规模异质面板：估计与推断AI 解读

This paper presents a new approach to estimation and inference in panel data models with a general multifactor error structure. The unobserved factors and the individual-specific errors are allowed to follow arbitrary stationary processes, and the number of unobserved factors need not be estimated. The basic idea is to filter the individual-specific regressors by means of cross-section averages such that asymptotically as the cross-section dimension (N) tends to infinity,…

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