Spectral analysis of Markov switching GARCH models with statistical inference

We derive matrix expressions in closed form for the autocovariance function and the spectral density of Markov switching GARCH models and their powers. For this, we apply the Riesz–Fischer theorem which defines the spectral representation as the Fourier transform of the autocovariance function. Under suitable assumptions, we prove that the sample estimator of the spectral density is consistent and asymptotically normally distributed. Further statistical implications in terms of order identification and parameter estimation are discussed. A simulation study confirms the validity of the asymptotic properties. These methods are also well suited for financial market applications, and in particular for the analysis of time series in the frequency domain, as shown in some proposed real-world examples.     

Fiscal Foresight and Information Flows

News—or foresight—about future economic fundamentals can create rational expectations equilibria with non‐fundamental representations that pose substantial challenges to econometric efforts to recover the structural shocks to which economic agents react. Using tax policies as a leading example of foresight, simple theory makes transparent the economic behavior and information structures that generate non‐fundamental equilibria. Econometric analyses that fail to model foresight will obtain biased estimates of output multipliers for taxes; biases are quantitatively important when two canonical theoretical models are taken as data generating processes. Both the nature of equilibria and the inferences about the effects of anticipated tax changes hinge critically on hypothesized information flows. Different methods for extracting or hypothesizing the information flows are discussed and shown to be alternative techniques for resolving a non‐uniqueness problem endemic to moving average representations.     

Multiply robust matching estimators of average and quantile treatment effects

Propensity score matching has been a long-standing tradition for handling confounding in causal inference, however, requiring stringent model assumptions. In this article, we propose novel double score matching (DSM) utilizing both the propensity score and prognostic score. To gain the protection of possible model misspecification, we posit multiple candidate models for each score. We show that the debiasing DSM estimator achieves the multiple robustness property in that it is consistent if any one of the score models is correctly specified. We characterize the asymptotic distribution for the DSM estimator requiring only one correct model specification based on the martingale representations of the matching estimators and theory for local normal experiments. We also provide a two-stage replication method for variance estimation and extend DSM for quantile estimation. Simulation demonstrates DSM outperforms single-score matching and prevailing multiply robust weighting estimators in the presence of extreme propensity scores.     

Locking down adolescents: Victims,irresponsible subjects,or just social actors

The article presents the results of a qualitative study investigating the experiences of a sample of Italian adolescents during the COVID-19 pandemic. Data were analysed in the light of the two social representations (adolescents as criminals and as victims) that permeated the Italian public debate throughout the outbreak. The findings showed the ability of boys and girls in dealing with the relationships altered by the pandemic constraints and in adapting to different regulations. This demonstrated their competence as social actors, neither criminals nor victims, who were also able to cope with the coronavirus risk in safeguarding their significant others.     

影像代偿:好莱坞电影中“精英黑人”的影像表征

影像转向的语境下，影像开始替代口头语言和文字成为受众认知世界的主要媒介。但即便是影像，也并非一定是对现实的客观再现。作为“造梦”装置的电影，以影像作为主要的表现手段，极具逼真效果，极易被误认为“现实”。在部分好莱坞电影中，黑人形象已经摆脱了早期的缺位状态或者刻板形象，更多以“精英黑人”的形象出现。但这种精英化的影像表征不一定是真实的。事实上，电影通过指涉现实等方式，消解了虚拟影像与现实之间的边界，使“精英黑人”的电影形象变得逼真和普遍，掩盖了黑人群体在现实中的真实社会地位。进而，电影作为一种影像层面的代偿，通过涵化观众的认知，在一定程度上戏剧性地消解了现实中的社会和阶级矛盾。