Taking a new contour: A novel approach to panel unit root tests

The paper introduces a novel approach to testing for unit roots in panels, which takes a new contour that is drawn along the line given by the equi-squared-sum instead of the traditional one given by the equi-sample-size. We show in the paper that the distributions of the unit root tests are asymptotically normal along the new contour under both the null and the local-to-unity alternatives. Subsequently, we demonstrate that this startling finding may be exploited constructively to invent tools and methodologies for effective inferences in panel unit root models. Simulations show that our approach works quite well in finite samples.     

Pushing the limit? Fiscal policy in the European Monetary Union

Governments are confronted with the growing realization that they face fiscal limits on the size of debt and deficits relative to GDP. These fiscal limits invalidate Bohn's criterion for fiscal sustainability, which allows explosive debt relative to GDP, eventually violating any fiscal limit. We derive restrictions on a fiscal rule, necessary for the government to eliminate explosive behavior. These restrictions require that the response of the primary surplus to debt be relatively strong, and that the primary surplus be cointegrated with both debt and output. We test these empirical implications for a panel of eleven EMU countries, and find that they are satisfied, implying that fiscal policy does not create explosive behavior.     

Who Benefits from Marriage?*

The phenomenon that married men earn higher average wages than unmarried men – the marriage premium – is well known. However, the robustness of the premium across the wage distribution and the underlying causes of the marriage premium are unclear. Focusing on the entire wage distribution and employing recently developed semi‐non‐parametric tests for quantile treatment effects, our findings cast doubt on the robustness of the premium. We find that the premium is explained by selection above the median, whereas a positive premium is obtained only at very low wages. The causal effect at low wages may be attributable to employer discrimination.     

A generalized nonlinear IV unit root test for panel data with cross-sectional dependence

This paper proposes a unit root test for panel data with cross-sectional dependence. The test generalizes the nonlinear IV unit root test of Chang (2002) to the case where there exist some common factors in panels. The main idea is to eliminate the cross-sectional dependence through the method of principal components as in Bai and Ng (2004) and then apply Chang’s test to the treated data. Under certain conditions, the proposed test is consistent and has a standard normal limiting distribution under the null hypothesis. Simulation results show that the proposed test compares favorably to other alternative tests.     

Inference on factor structures in heterogeneous panels

This paper develops an estimation and testing framework for a stationary large panel model with observable regressors and unobservable common factors. We allow for slope heterogeneity and for correlation between the common factors and the regressors. We propose a two stage estimation procedure for the unobservable common factors and their loadings, based on Common Correlated Effects estimator and the Principal Component estimator. We also develop two tests for the null of no factor structure: one for the null that loadings are cross sectionally homogeneous, and one for the null that common factors are homogeneous over time. Our tests are based on using extremes of the estimated loadings and common factors. The test statistics have an asymptotic Gumbel distribution under the null, and have power versus alternatives where only one loading or common factor differs from the others. Monte Carlo evidence shows that the tests have the correct size and good power.     

Testing for structural breaks in dynamic factor models

In this paper we investigate the consequences of structural breaks in the factor loadings for the specification and estimation of factor models based on principal components and suggest procedures for testing for structural breaks. It is shown that structural breaks severely inflate the number of factors identified by the usual information criteria. The hypothesis of a structural break is tested by using LR, LM and Wald statistics. The LM test (which performs best in our Monte Carlo simulations) is generalized to test for structural breaks in factor models where the break date is unknown and the common factors and idiosyncratic components are serially correlated. The proposed test procedures are applied to datasets from the US and the euro area.     

Detecting big structural breaks in large factor models

Time invariance of factor loadings is a standard assumption in the analysis of large factor models. Yet, this assumption may be restrictive unless parameter shifts are mild (i.e., local to zero). In this paper we develop a new testing procedure to detect big breaks in these loadings at either known or unknown dates. It relies upon testing for parameter breaks in a regression of one of the factors estimated by Principal Components analysis on the remaining estimated factors, where the number of factors is chosen according to Bai and Ng’s (2002) information criteria. The test fares well in terms of power relative to other recently proposed tests on this issue, and can be easily implemented to avoid forecasting failures in standard factor-augmented (FAR, FAVAR) models where the number of factors is a priori imposed on the basis of theoretical considerations.     

A two-step estimator for large approximate dynamic factor models based on Kalman filtering

This paper shows consistency of a two-step estimation of the factors in a dynamic approximate factor model when the panel of time series is large (n large). In the first step, the parameters of the model are estimated from an OLS on principal components. In the second step, the factors are estimated via the Kalman smoother. The analysis develops the theory for the estimator considered in Giannone et al. (2004) and Giannone et al. (2008) and for the many empirical papers using this framework for nowcasting.     

Beyond panel unit root tests: Using multiple testing to determine the nonstationarity properties of individual series in a panel

Most panel unit root tests are designed to test the joint null hypothesis of a unit root for each individual series in a panel. After a rejection, it will often be of interest to identify which series can be deemed to be stationary and which series can be deemed nonstationary. Researchers will sometimes carry out this classification on the basis of n$n$ individual (univariate) unit root tests based on some ad hoc significance level. In this paper, we suggest and demonstrate how to use the false discovery rate (FDR)$\left(FDR\right)$ in evaluating I(1)/I(0)$I\left(1\right)/I\left(0\right)$ classifications.     

Modeling the diffusion of scientific publications

This paper illustrates that salient features of a panel of time series of annual citations can be captured by a Bass type diffusion model. We put forward an extended version of this diffusion model, where we consider the relation between key characteristics of the diffusion process and features of the articles. More specifically, parameters measuring citations’ ceiling and the timing of peak citations are correlated with specific features of the articles like the number of pages and the number of authors. Our approach amounts to a multi-level non-linear regression for a panel of time series. We illustrate our model for citations to articles that were published in Econometrica and the Journal of Econometrics. Amongst other things, we find that more references lead to more citations and that for the Journal of Econometrics peak citations of more recent articles tend to occur later.