Redistributive Inflation and Optimal Monetary Policy

Inflation has heterogeneous impacts on households, which then affects optimal monetary policy design. I study optimal monetary policy rules in a quantitative heterogeneous agent New Keynesian (HANK) model where inflation has redistributive effects on households through their different (1) consumption baskets, (2) nominal wealth positions, and (3) earnings elasticities to business cycles. I parameterize the model based on the empirical analysis of these channels using the most recent data. Unlike in representative agent models, a utilitarian central bank should adopt an asymmetric monetary policy rule that is accommodative towards inflation and aggressive towards deflation. Specifically, by accommodating stronger demand and higher inflation, the central bank benefits low-income and low-wealth households through nominal debt devaluation and higher earnings growth

Networks and Business Cycles

The speed at which the US economy has recovered from recessions ranges from months to years. We propose a model incorporating the innovation network, the production network, and cross-sectional shocks and show that their interactions jointly explain large variations in the recovery speed across recessions in the US. In the model, besides the production linkages, firms learn insights on production from each other through the innovation network. We show when the innovation network takes a low-rank structure, there exists one key direction: the impact a shock becomes persistent only if the shock is parallel to this key direction; in contrast, the impact declines quickly if the shock follows other directions. Empirically, we estimate the model in a state-space form and document a set of new stylized facts of the US economy. First, the innovation network among sectors takes a low-rank structure. Second, the innovation network has non-negligible overlap with the production network. Third, recessions with slow recovery are those witnessing sizable negative shock to sectors in the center of the innovation network. Such network structures and the time-varying sectoral distribution of the shocks can well explain the large variation in the recovery speed across recessions in the US. Finally, to emphasize the prevalence of the channel, we explore the application of the theory in asset pricing

Palladium-catalyzed difluoromethylation of heteroaryl chlorides, bromides and iodides.

A palladium-catalyzed difluoromethylation of a series of heteroaryl chlorides, bromides and iodides under mild conditions is described. A wide range of heteroaryl halides such as pyridyl, pyrimidyl, pyrazyl, funanyl, thienyl, pyazolyl, imidazolyl, thiazolyl, and oxazolyl halides were efficiently difluoromethylated, thus providing medicinal chemists an alternative choice for the preparation of drug candidates with the difluoromethylated heteroarene unit

Text Augmented Spatial-aware Zero-shot Referring Image Segmentation

In this paper, we study a challenging task of zero-shot referring image segmentation. This task aims to identify the instance mask that is most related to a referring expression without training on pixel-level annotations. Previous research takes advantage of pre-trained cross-modal models, e.g., CLIP, to align instance-level masks with referring expressions. %Yet, CLIP only considers image-text pair level alignment, which neglects fine-grained image region and complex sentence matching. Yet, CLIP only considers the global-level alignment of image-text pairs, neglecting fine-grained matching between the referring sentence and local image regions. To address this challenge, we introduce a Text Augmented Spatial-aware (TAS) zero-shot referring image segmentation framework that is training-free and robust to various visual encoders. TAS incorporates a mask proposal network for instance-level mask extraction, a text-augmented visual-text matching score for mining the image-text correlation, and a spatial rectifier for mask post-processing. Notably, the text-augmented visual-text matching score leverages a $P$ score and an $N$-score in addition to the typical visual-text matching score. The $P$-score is utilized to close the visual-text domain gap through a surrogate captioning model, where the score is computed between the surrogate model-generated texts and the referring expression. The $N$-score considers the fine-grained alignment of region-text pairs via negative phrase mining, encouraging the masked image to be repelled from the mined distracting phrases. Extensive experiments are conducted on various datasets, including RefCOCO, RefCOCO+, and RefCOCOg. The proposed method clearly outperforms state-of-the-art zero-shot referring image segmentation methods.Comment: Findings of EMNLP202