arXiv subjects

Jin Xi

Publications and source records attributed to Jin Xi.

6 recordsLinked to original sources

arXiv2026

Principal Component Analysis for a Mix of Stationary and Nonstationary Variables

This paper develops a procedure for uncovering the common cyclical factors that drive a mix of stationary and nonstationary variables. The method does not require knowing which variables are nonstationary or the nature of the nonstationarity. An application to the FRED-MD macroeconomic dataset demonstrates that the approach offers similar benefits to those of traditional principal component analysis with some added advantages.

econ.EM↗

arXiv2025

A Synthetic Business Cycle Approach to Counterfactual Analysis with Nonstationary Macroeconomic Data

This paper investigates the use of synthetic control methods for causal inference in macroeconomic settings when dealing with possibly nonstationary data. While the synthetic control approach has gained popularity for estimating counterfactual outcomes, we caution researchers against assuming a common nonstationary trend factor across units for macroeconomic outcomes, as doing so may result in misleading causal estimation-a pitfall we refer to as the spurious synthetic control problem. To address this issue, we propose a synthetic business cycle framework that explicitly separates trend and cyclical components. By leveraging the treated unit's historical data to forecast its trend and using control units only for cyclical fluctuations, our divide-and-conquer strategy eliminates spurious correlations and improves the robustness of counterfactual prediction in macroeconomic applications. As empirical illustrations, we examine the cases of German reunification and the handover of Hong Kong, demonstrating the advantages of the proposed approach.

econ.EM↗

arXiv2021

Strength in Numbers: Robust Mechanisms for Public Goods with Many Agents

This study examines the mechanism design problem for public goods provision in a large economy with $n$ independent agents. We propose a class of dominant-strategy incentive compatible and ex-post individually rational mechanisms, which we call the adjusted mean-thresholding (AMT) mechanisms. We show that when the cost of provision grows slower than the $\sqrt{n}$-rate, the AMT mechanisms are both eventually ex-ante budget balanced and asymptotically efficient. When the cost grows faster than the $\sqrt{n}$-rate, in contrast, we show that any incentive compatible, individually rational, and eventually ex-ante budget balanced mechanism must have provision probability converging to zero and hence cannot be asymptotically efficient. The AMT mechanisms have a simple form and are more informationally robust when compared to, for example, the second-best mechanism. This is because the construction of an AMT mechanism depends only on the first moment of the valuation distribution.

econ.TH↗

arXiv2019

Neumann Data Mass on Perturbed Triangles

Based on a previous paper [Chr17] on Neumann data for Dirichlet eigenfunctions on triangles, we extend the study in two ways. First, we investigate the (semi-classical) Neumann data mass on perturbed triangles. Specifically, we replace one side of a triangle by adding a smooth perturbation, and assume that the disparity between the perturbation and the original side is bounded by a small value $ε$. Second, we add a small $ε$ sized potential to the (semi-classical) Laplacian and see how the results change on triangles. In both cases, we find that the $L^2$ norm of Neumann data on each side is close to the length of the side divided by the area of the triangle, and the difference is dominated by $ε$.

math.AP↗

arXiv2017

Quantifying the effect of ionic screening with protein-decorated graphene transistors

Liquid-based applications of biomolecule-decorated field-effect transistors (FETs) range from biosensors to in vivo implants. A critical scientific challenge is to develop a quantitative understanding of the gating effect of charged biomolecules in ionic solution and how this influences the readout of the FETs. To address this issue, we fabricated protein-decorated graphene FETs and measured their electrical properties, specifically the shift in Dirac voltage, in solutions of varying ionic strength. We found excellent quantitative agreement with a model that accounts for both the graphene polarization charge and ionic screening of ions adsorbed on the graphene as well as charged amino acids associated with the immobilized protein. The technique and analysis presented here directly couple the charging status of bound biomolecules to readout of liquid-phase FETs fabricated with graphene or other two-dimensional materials.

cond-mat.mes-hall↗

arXiv2014

Scalable Production of Highly-Sensitive Nanosensors Based on Graphene Functionalized with a Designed G Protein-Coupled Receptor

We have developed a novel, all-electronic biosensor for opioids that consists of an engineered mu opioid receptor protein, with high binding affinity for opioids, chemically bonded to a graphene field-effect transistor to read out ligand binding. A variant of the receptor protein that provided chemical recognition was computationally redesigned to enhance its solubility and stability in an aqueous environment. A shadow mask process was developed to fabricate arrays of hundreds of graphene transistors with average mobility of ~1500 cm2 V-1 s-1 and yield exceeding 98%. The biosensor exhibits high sensitivity and selectivity for the target naltrexone, an opioid receptor antagonist, with a detection limit of 10 pg/mL.

cond-mat.mtrl-sci↗