Identification-robust moment-based tests for Markov switching in autoregressive models

ABSTRACT

This paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of normal mixtures implied by the regime-switching process and uses Monte Carlo test techniques to deal with the presence of an autoregressive component in the model specification. The proposed tests have very respectable power in comparison with the optimal tests for Markov-switching parameters of Carrasco et al. (2014 Carrasco, M., Hu, L., Ploberger, W. (2014). Optimal test for Markov switching parameters. Econometrica 82(2):765–784.[Crossref], [Web of Science ®] , [Google Scholar]), and they are also quite attractive owing to their computational simplicity. The new tests are illustrated with an empirical application to an autoregressive model of USA output growth.     

The Resistible Rise of Sarah Palin: Continuity and Paradox in the American Right Wing1

Sarah Palin clearly holds a powerful appeal for a right‐wing constituency although even her most fervent followers may doubt that she is electable. This article examines the basis of her appeal. We argue that it can be summed up as an appeal to American authenticity, which consists of several elements: the valorization of small town ordinary life takes second place to faith and religious commitment; an equal faith in American exceptionality implies that Americans should never be apologetic about their country and never waver in their belief in its special greatness; the final elements are a foundational libertarian creed and a peculiar brand of right‐wing populism. However, for all her affinities with classical right‐wing sentiment, Sarah Palin, we argue, brings something new to the mix: a genuinely maverick right‐wing feminism. That is why in the end, we call the Palin phenomenon a paradoxical one.     

Globalization and Political Change in the Women's Movement: The Politics of Scale and Political Empowerment in the World March of Women*

Objective. The objective of the article is to show that in order to understand the ongoing transnational mobilizations of the European wing of the World March of Women (WMW) between 2000 and 2006 we also need to consider the politics of scale of the transnational social movements' mobilizations. The WMW is a transnational collective action that integrates women from grassroots organizations, labor unions, and leftist political parties in over 150 countries (approximately 6,000 groups) into a process of transnationalization of solidarities. Method. The method is based on the analysis of internal documents of the international and European wings of the movement, interviews with key actors and militants, and direct observations over the years 1998–2005. Results. The results are twofold: we investigate the shift in the politics of scale of the movement, from using the same scale as the political authorities with which they interact to the creation of its own scales of action (first part); we focus on the articulation of different scales of protest, showing how, by constructing networks and coalitions, actions, and demands under the WMW umbrella, grassroots women's groups are becoming empowered and are regaining political power over the definition, dissemination, and resolution of gender issues (second part). Conclusion. The conclusion is that this specific process of empowerment helps to explain why feminist activists pursue transnationalization actions despite all the material, ideological, and relational difficulties that accompany such actions.     

Finite-sample Resampling-based Combined Hypothesis Tests,with Applications to Serial Correlation and Predictability

This article suggests Monte Carlo multiple test procedures which are provably valid in finite samples. These include combination methods originally proposed for independent statistics and further improvements which formalize statistical practice. We also adopt the Monte Carlo test method to noncontinuous combined statistics. The methods suggested are applied to test serial dependence and predictability. In particular, we introduce and analyze new procedures that account for endogenous lag selection. A simulation study illustrates the properties of the proposed methods. Results show that concrete and nonspurious power gains (over standard combination methods) can be achieved through the combined Monte Carlo test approach, and confirm arguments in favor of variance-ratio type criteria.     

Projection‐Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments

It is well known that standard asymptotic theory is not applicable or is very unreliable in models with identification problems or weak instruments. One possible way out consists of using a variant of the Anderson–Rubin ((1949), AR) procedure. The latter allows one to build exact tests and confidence sets only for the full vector of the coefficients of the endogenous explanatory variables in a structural equation, but not for individual coefficients. This problem may in principle be overcome by using projection methods (Dufour (1997), Dufour and Jasiak (2001)). At first sight, however, this technique requires the application of costly numerical algorithms. In this paper, we give a general necessary and sufficient condition that allows one to check whether an AR‐type confidence set is bounded. Furthermore, we provide an analytic solution to the problem of building projection‐based confidence sets from AR‐type confidence sets. The latter involves the geometric properties of “quadrics” and can be viewed as an extension of usual confidence intervals and ellipsoids. Only least squares techniques are needed to build the confidence intervals.     

Nested patterns in urban butterfly species assemblages: respective roles of plot management,park layout and landscape features

Increasing numbers of cities are currently developing sustainable policies aimed at promoting urban biodiversity and ecological dynamics through the planning of green networks and the implementation of more sustainable management practices. These human activities can strongly influence environmental factors on which the organization of ecological communities at different scales depends. Thus, it is of fundamental importance to understand the relative impact of local management, green space design and landscape features on the distribution and the abundance of species in urban areas. On the basis of 2 years of butterfly surveys in urban public parks within an extensive Mediterranean metropolitan area, Marseille (South-East France), the aim of this paper is to provide a better understanding of the effect of these three environmental scales (plot, park, landscape) on the composition and organization of species assemblages. Using variation partitioning and nestedness analysis on ecological data aggregated at plot-level and park-level respectively, we demonstrate the preponderant effect of landscape scale features on urban butterfly assemblages. Our results also highlight an important co-variation of plot management, park layout and urban landscape features, in their interaction with the community structure of urban butterflies. Although there is no significant species-area relationship, significantly nested patterns arise in species composition. Selective colonization appears as a driving force constraining the constitution of species assemblages within the city. However, a prospective study on adjacent more natural areas suggests that biotic limitations, interspecific competition and habitat filtering may play an important role if a larger portion of the urbanization gradient is explored, which remains to be investigated.     

Period‐Based Mortality Change: Turning Points in Trends since 1950

We investigate a major turning point in mortality trends at adult ages that occurred for many low‐mortality countries in the late 1960s or early 1970s. We analyze patterns of total and cause‐specific mortality over the past 60 years using data from the Human Mortality Database and the World Health Organization. We focus on four broad categories of causes of death: heart diseases, cerebrovascular diseases, smoking‐related cancers, and all other cancers. We use a two‐slope regression model to assess the timing and magnitude of turning points in mortality trends over this era, making separate analyses by sex, age, and cause of death. The age pattern of temporal changes is given particular attention. Our results demonstrate convincingly that period‐based factors were very significant in the onset of the “cardiovascular revolution” in the years around 1970. In general, although cohort processes cannot be ruled out as a driver of mortality change in recent decades (especially for mortality due to smoking‐related cancers), the evidence reviewed here suggests that period factors have been the dominant force behind the mortality trends of high‐income countries during this era.     

Instrument endogeneity and identification-robust tests: Some analytical results

When some explanatory variables in a regression are correlated with the disturbance term, instrumental variable methods are typically employed to make reliable inferences. Furthermore, to avoid difficulties associated with weak instruments, identification-robust methods are often proposed. However, it is hard to assess whether an instrumental variable is valid in practice because instrument validity is based on the questionable assumption that some of them are exogenous. In this paper, we focus on structural models and analyze the effects of instrument endogeneity on two identification-robust procedures, the Anderson–Rubin (1949, AR) and the Kleibergen (2002, K) tests, with or without weak instruments. Two main setups are considered: (1) the level of “instrument” endogeneity is fixed (does not depend on the sample size) and (2) the instruments are locally exogenous, i.e. the parameter which controls instrument endogeneity approaches zero as the sample size increases. In the first setup, we show that both test procedures are in general consistent against the presence of invalid instruments (hence asymptotically invalid for the hypothesis of interest), whether the instruments are “strong” or “weak”. We also describe cases where test consistency may not hold, but the asymptotic distribution is modified in a way that would lead to size distortions in large samples. These include, in particular, cases where the 2SLS estimator remains consistent, but the AR and K tests are asymptotically invalid. In the second setup, we find (non-degenerate) asymptotic non-central chi-square distributions in all cases, and describe cases where the non-centrality parameter is zero and the asymptotic distribution remains the same as in the case of valid instruments (despite the presence of invalid instruments). Overall, our results underscore the importance of checking for the presence of possibly invalid instruments when applying “identification-robust” tests.