arXiv subjects

Yuya Sasaki

Publications and source records attributed to Yuya Sasaki.

At least 73 records · Page 4Linked to original sources

arXiv2022

Capital and Labor Income Pareto Exponents in the United States, 1916-2019

Accurately estimating income Pareto exponents is challenging due to limitations in data availability and the applicability of statistical methods. Using tabulated summaries of incomes from tax authorities and a recent estimation method, we estimate income Pareto exponents in U.S. for 1916-2019. We find that during the past three decades, the capital and labor income Pareto exponents have been stable at around 1.2 and 2. Our findings suggest that the top tail income and wealth inequality is higher and wealthy agents have twice as large an impact on the aggregate economy than previously thought but there is no clear trend post-1985.

econ.GN↗

arXiv2022

Nonparametric Difference-in-Differences in Repeated Cross-Sections with Continuous Treatments

This paper studies the identification of causal effects of a continuous treatment using a new difference-in-difference strategy. Our approach allows for endogeneity of the treatment, and employs repeated cross-sections. It requires an exogenous change over time which affects the treatment in a heterogeneous way, stationarity of the distribution of unobservables and a rank invariance condition on the time trend. On the other hand, we do not impose any functional form restrictions or an additive time trend, and we are invariant to the scaling of the dependent variable. Under our conditions, the time trend can be identified using a control group, as in the binary difference-in-differences literature. In our scenario, however, this control group is defined by the data. We then identify average and quantile treatment effect parameters. We develop corresponding nonparametric estimators and study their asymptotic properties. Finally, we apply our results to the effect of disposable income on consumption.

econ.EM↗

arXiv2022

Unconditional Quantile Regression with High Dimensional Data

This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel robust score for debiased estimation of the unconditional quantile regression (Firpo, Fortin, and Lemieux, 2009) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference and develop asymptotic theories to guarantee the size control in large sample. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that a policy which counterfactually extends the duration of exposures to the Job Corps training program will be effective especially for the targeted subpopulations of lower potential wage earners.

econ.EM↗

arXiv2022

Similarity Search on Computational Notebooks

Computational notebook software such as Jupyter Notebook is popular for data science tasks. Numerous computational notebooks are available on the Web and reusable; however, searching for computational notebooks manually is a tedious task, and so far, there are no tools to search for computational notebooks effectively and efficiently. In this paper, we propose a similarity search on computational notebooks and develop a new framework for the similarity search. Given contents (i.e., source codes, tabular data, libraries, and outputs formats) in computational notebooks as a query, the similarity search problem aims to find top-k computational notebooks with the most similar contents. We define two similarity measures; set-based and graph-based similarities. Set-based similarity handles each content independently, while graph-based similarity captures the relationships between contents. Our framework can effectively prune the candidates of computational notebooks that should not be in the top-k results. Furthermore, we develop optimization techniques such as caching and indexing to accelerate the search. Experiments using Kaggle notebooks show that our method, in particular graph-based similarity, can achieve high accuracy and high efficiency.

cs.IR↗

arXiv2021

Language-aware Indexing for Conjunctive Path Queries

Conjunctive path queries (CPQ) are one of the most frequently used queries for complex graph analysis. However, current graph indexes are not tailored to fully support the power of query languages to express CPQs. Consequently, current methods do not take advantage of significant pruning opportunities during \cpq{} evaluation, resulting in poor query processing performance. We propose the CPQ-aware path index CPQx, the first path index tailored to the expressivity of CPQ. CPQx is built on the partition of the set of source-target vertex pairs of paths in a graph based on the structural notion of path-bisimulation. Path-bisimulation is an equivalence relation on paths such that each partition block induced by the relation consists of paths in the graph indistinguishable with respect to CPQs. This language-aware partitioning of the graph can significantly reduce the cost of query evaluation. We present methods to support the full index life cycle: index construction, maintenance, and query processing with our index. We also develop interest-aware CPQx to reduce index size and index construction overhead while accelerating query evaluation for queries of interest. We demonstrate through extensive experiments on 14 real graphs that our methods accelerate query processing by up to multiple orders of magnitude over the state-of-the-art methods, with smaller index sizes. Our complete C++ codebase is available as open source for further research.

cs.DB↗

arXiv2021

Slow Movers in Panel Data

Panel data often contain stayers (units with no within-variations) and slow movers (units with little within-variations). In the presence of many slow movers, conventional econometric methods can fail to work. We propose a novel method of robust inference for the average partial effects in correlated random coefficient models robustly across various distributions of within-variations, including the cases with many stayers and/or many slow movers in a unified manner. In addition to this robustness property, our proposed method entails smaller biases and hence improves accuracy in inference compared to existing alternatives. Simulation studies demonstrate our theoretical claims about these properties: the conventional 95% confidence interval covers the true parameter value with 37-93% frequencies, whereas our proposed one achieves 93-96% coverage frequencies.

econ.EM↗

arXiv2021

FedMe: Federated Learning via Model Exchange

Federated learning is a distributed machine learning method in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients. Numerous methods have been proposed to cope with the data heterogeneity issue in federated learning. Existing solutions require a model architecture tuned by the central server, yet a major technical challenge is that it is difficult to tune the model architecture due to the absence of local data on the central server. In this paper, we propose Federated learning via Model exchange (FedMe), which personalizes models with automatic model architecture tuning during the learning process. The novelty of FedMe lies in its learning process: clients exchange their models for model architecture tuning and model training. First, to optimize the model architectures for local data, clients tune their own personalized models by comparing to exchanged models and picking the one that yields the best performance. Second, clients train both personalized models and exchanged models by using deep mutual learning, in spite of different model architectures across the clients. We perform experiments on three real datasets and show that FedMe outperforms state-of-the-art federated learning methods while tuning model architectures automatically.

cs.LG↗

arXiv2021

AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities

Urban air pollution is a major environmental problem affecting human health and quality of life. Monitoring stations have been established to continuously obtain air quality information, but they do not cover all areas. Thus, there are numerous methods for spatially fine-grained air quality inference. Since existing methods aim to infer air quality of locations only in monitored cities, they do not assume inferring air quality in unmonitored cities. In this paper, we first study the air quality inference in unmonitored cities. To accurately infer air quality in unmonitored cities, we propose a neural network-based approach AIREX. The novelty of AIREX is employing a mixture-of-experts approach, which is a machine learning technique based on the divide-and-conquer principle, to learn correlations of air quality between multiple cities. To further boost the performance, it employs attention mechanisms to compute impacts of air quality inference from the monitored cities to the locations in the unmonitored city. We show, through experiments on a real-world air quality dataset, that AIREX achieves higher accuracy than state-of-the-art methods.

cs.LG↗

arXiv2021

Inference for high-dimensional exchangeable arrays

We consider inference for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes. For both exchangeable arrays, we first derive high-dimensional central limit theorems over the rectangles and subsequently develop novel multiplier bootstraps with theoretical guarantees. These theoretical results rely on new technical tools such as Hoeffding-type decomposition and maximal inequalities for the degenerate components in the Hoeffiding-type decomposition for the exchangeable arrays. We exhibit applications of our methods to uniform confidence bands for density estimation under joint exchangeability and penalty choice for $\ell_1$-penalized regression under separate exchangeability. Extensive simulations demonstrate precise uniform coverage rates. We illustrate by constructing uniform confidence bands for international trade network densities.

econ.EM↗

arXiv2021

Cost-constrained Minimal Steiner Tree Enumeration by Binary Decision Diagram

The Steiner tree enumeration problem is a well known problem that asks for enumerating Steiner trees. Numerous theoretical works proposed algorithms for the problem and analyzed their complexity, but there are no practical algorithms and experimental studies. In this paper, we study the Steiner tree enumeration problem practically. We define a problem cost-constrained minimal Steiner tree enumeration problem, which enumerates minimal Steiner trees with costs not larger than a given threshold. To solve the problem, we propose a binary decision diagram (BDD)-based algorithm. The BDD-based algorithm constructs a BDD that compactly represents the set of minimal Steiner trees and then traverses the BDD for enumeration. We develop a novel {\it frontier-based algorithm} to efficiently construct BDDs. Our BDD traverse algorithm prunes Steiner trees with costs larger than the threshold. We also extend our algorithm by preprocessing the given graph and controlling the number of generated Steiner trees in order to reduce the memory and computation costs. The extension makes our algorithm scalable by generating a subset of the minimal Steiner trees. We validate that our algorithm can enumerate Steiner trees in real-world graphs more efficiently than existing methods.

cs.DS↗

arXiv2021

Smart City Data Analysis via Visualization of Correlated Attribute Patterns

Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute pattern (CAP) mining discovers correlations among multiple attributes from the sets of sensors spatially close to each other and temporally correlated in their measurements. In this paper, we develop a visualization system for CAP mining and demonstrate analysis of smart city data. Our visualization system supports an intuitive understanding of mining results via sensor locations on maps and temporal changes of their measurements. In our demonstration scenarios, we provide four smart city datasets collected from China and Santander, Spain. We demonstrate that our system helps interactive analysis of smart city data.

cs.DB↗

arXiv2021

Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs

We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints. This method can be implemented via a linear programming, and it is thus computationally appealing. We illustrate a usage of our proposed method with an application to the regression kink design (RKD). Econometric analyses based on the RKD often suffer from wide confidence intervals due to slow convergence rates of nonparametric derivative estimators. We demonstrate that economic models and structures motivate shape restrictions, which in turn contribute to shrinking the confidence interval for an analysis of the causal effects of unemployment insurance benefits on unemployment durations.

econ.EM↗

arXiv2021

Estimation and Inference for Moments of Ratios with Robustness against Large Trimming Bias

Empirical researchers often trim observations with small denominator A when they estimate moments of the form E[B/A]. Large trimming is a common practice to mitigate variance, but it incurs large trimming bias. This paper provides a novel method of correcting large trimming bias. If a researcher is willing to assume that the joint distribution between A and B is smooth, then a large trimming bias may be estimated well. With the bias correction, we also develop a valid and robust inference result for E[B/A].

stat.ME↗

arXiv2020

Welfare Analysis via Marginal Treatment Effects

Consider a causal structure with endogeneity (i.e., unobserved confoundedness) in empirical data, where an instrumental variable is available. In this setting, we show that the mean social welfare function can be identified and represented via the marginal treatment effect (MTE, Bjorklund and Moffitt, 1987) as the operator kernel. This representation result can be applied to a variety of statistical decision rules for treatment choice, including plug-in rules, Bayes rules, and empirical welfare maximization (EWM) rules as in Hirano and Porter (2020, Section 2.3). Focusing on the application to the EWM framework of Kitagawa and Tetenov (2018), we provide convergence rates of the worst case average welfare loss (regret) in the spirit of Manski (2004).

econ.EM↗

arXiv2020

Sequenced Route Query with Semantic Hierarchy

The trip planning query searches for preferred routes starting from a given point through multiple Point-of-Interests (PoI) that match user requirements. Although previous studies have investigated trip planning queries, they lack flexibility for finding routes because all of them output routes that strictly match user requirements. We study trip planning queries that output multiple routes in a flexible manner. We propose a new type of query called skyline sequenced route (SkySR) query, which searches for all preferred sequenced routes to users by extending the shortest route search with the semantic similarity of PoIs in the route. Flexibility is achieved by the {\it semantic hierarchy} of the PoI category. We propose an efficient algorithm for the SkySR query, bulk SkySR algorithm that simultaneously searches for sequenced routes and prunes unnecessary routes effectively. Experimental evaluations show that the proposed approach significantly outperforms the existing approaches in terms of response time (up to four orders of magnitude). Moreover, we develop a prototype service that uses the SkySR query, and conduct a user test to evaluate its usefulness.

cs.DB↗

arXiv2020

Efficient Network Reliability Computation in Uncertain Graphs

Network reliability is an important metric to evaluate the connectivity among given vertices in uncertain graphs. Since the network reliability problem is known as #P-complete, existing studies have used approximation techniques. In this paper, we propose a new sampling-based approach that efficiently and accurately approximates network reliability. Our approach improves efficiency by reducing the number of samples based on stratified sampling. We theoretically guarantee that our approach improves the accuracy of approximation by using lower and upper bounds of network reliability, even though it reduces the number of samples. To efficiently compute the bounds, we develop an extended BDD, called S2BDD. During constructing the S2BDD, our approach employs dynamic programming for efficiently sampling possible graphs. Our experiment with real datasets demonstrates that our approach is up to 51.2 times faster than the existing sampling-based approach with higher accuracy.

cs.DS↗

arXiv2020

Testing Finite Moment Conditions for the Consistency and the Root-N Asymptotic Normality of the GMM and M Estimators

Common approaches to inference for structural and reduced-form parameters in empirical economic analysis are based on the consistency and the root-n asymptotic normality of the GMM and M estimators. The canonical consistency (respectively, root-n asymptotic normality) for these classes of estimators requires at least the first (respectively, second) moment of the score to be finite. In this article, we present a method of testing these conditions for the consistency and the root-n asymptotic normality of the GMM and M estimators. The proposed test controls size nearly uniformly over the set of data generating processes that are compatible with the null hypothesis. Simulation studies support this theoretical result. Applying the proposed test to the market share data from the Dominick's Finer Foods retail chain, we find that a common \textit{ad hoc} procedure to deal with zero market shares in analysis of differentiated products markets results in a failure to satisfy the conditions for both the consistency and the root-n asymptotic normality.

econ.EM↗

arXiv2020

Fixed-k Inference for Conditional Extremal Quantiles

We develop a new extreme value theory for repeated cross-sectional and panel data to construct asymptotically valid confidence intervals (CIs) for conditional extremal quantiles from a fixed number $k$ of nearest-neighbor tail observations. As a by-product, we also construct CIs for extremal quantiles of coefficients in linear random coefficient models. For any fixed $k$, the CIs are uniformly valid without parametric assumptions over a set of nonparametric data generating processes associated with various tail indices. Simulation studies show that our CIs exhibit superior small-sample coverage and length properties than alternative nonparametric methods based on asymptotic normality. Applying the proposed method to Natality Vital Statistics, we study factors of extremely low birth weights. We find that signs of major effects are the same as those found in preceding studies based on parametric models, but with different magnitudes.

econ.EM↗