About
48
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Introduction
My research interests lie at the intersection of efficiency and productivity analysis, machine learning, and nonparametric econometrics and their applications to energy, resources, and environmental fields.
Current institution
Additional affiliations
June 2022 - June 2024
September 2018 - October 2022
Position
- Doctoral Researcher
Description
- Doctoral studies and research in Quantitative Methods
Education
September 2018 - October 2022
September 2014 - June 2017
September 2010 - June 2014
Publications
Publications (48)
Explaining the secular stagnation of productivity growth is a widely recognized challenge to economists and policymakers. One potentially important explanation without much attention concerns the ongoing low‐carbon transition. This paper explores whether considering greenhouse gas emissions can explain productivity stagnation in OECD countries. We...
Explaining the secular stagnation of productivity growth is a widely recognized challenge to economists and policymakers. One potentially important explanation without much attention concerns the ongoing low-carbon transition. This paper explores whether considering greenhouse gas emissions can explain productivity stagnation in OECD countries. We...
Optimal allocation of resources across sub-units in the context of centralized decision-making systems such as bank branches or supermarket chains is a classical application of operations research and management science. In this paper, we develop quantile allocation models to examine how much the output and productivity could potentially increase i...
Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning, and related fields. In the field of productivity and efficiency analysis, recent developments in multivariate convex regression and related techniques such as convex quantile regression and convex expectile regression hav...
Modeling of joint production has proved a vexing problem. This paper develops a radial convex nonparametric least squares (CNLS) approach to estimate the input distance function with multiple outputs. We document the correct input distance function transformation and prove that the necessary orthogonality conditions can be satisfied in radial CNLS....
We study how efficient resource reallocation across cities affects potential aggregate growth. Using optimal resource allocation models and data on 284 China's prefecture-level cities in the years 2003-2019, we quantitatively measure the cost of misallocation of resources. We show that average aggregate output gains from reallocating resources acro...
Renewable energy policies are recognized as the cornerstone of the low-carbon energy transition. However, they may cause resource misallocation between firms due to information asymmetries and differences in productivity, marginal costs, and revenues. This paper investigates the direct effects of heterogeneous renewable energy policies on resource...
This publication is part of the implementation of the Finnish Government Plan for Analysis, Assessment and Research. (tietokayttoon.fi) The content is the responsibility of the producers of the information and does not necessarily represent the view of the Government.
Abstract: Bottlenecks in transport systems can cause frictions in the labor mark...
Convex regression is a method for estimating an unknown function f0 from a data set of n noisy observations when f0 is known to be convex. This method has played an important role in operations research, economics, machine learning, and many other areas. It has been empirically observed that the convex regression estimator produces inconsistent est...
Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A direct approach to address this problem is to impose non-crossing constraints to convex quantile regression. However, the non-crossing constraints may violate an intrinsic quantile property. This paper proposes a penalized convex quantile regression a...
Modeling of joint production has proved a vexing problem. This paper develops a radial convex nonparametric least squares (CNLS) approach to estimate the input distance function with multiple outputs. We document the correct input distance function transformation and prove that the necessary orthogonality conditions can be satisfied in radial CNLS....
Optimal allocation of resources across sub-units in the context of centralized decision-making systems such as bank branches or supermarket chains is a classical application of operations research and management science. In this paper, we develop quantile allocation models to examine how much the output and productivity could potentially increase i...
Rapid urban expansion has profoundly affected the structure and function of ecosystem services. However, reliable empirical evidence of the spatiotemporal impact of urban sprawl on ecosystem services remains scant. In this paper, we employ the dynamic spatial Durbin model to explore the relationship between urban sprawl and ecosystem services using...
Convex quantile regression (CQR) is a fully nonparametric approach to estimating quantile functions, which has proved useful in many applications of productivity and efficiency analysis. Importantly, CQR satisfies the quantile property, which states that the observed data is split into proportions by the CQR frontier for any weight in the unit inte...
Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers. This paper proposes to address these two issues...
The coronavirus infection COVID-19 killed millions of people around the world in 2019-2022. Hospitals were in the forefront in the battle against the pandemic. This paper proposes a novel approach to assess the effectiveness of hospitals in saving lives. We empirically estimate the production function of COVID-19 deaths among hospital inpatients, a...
Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers. This paper proposes to address these two issues...
Convex regression is increasingly popular in economics, finance, operations research, machine learning, and statistics. In the productivity and efficiency analysis field, convex regression and its latest development have bridged the long-standing gap between the conventional deterministic nonparametric and stochastic-parametric methods. This disser...
Tämän selvityksen on laatinut ECKTA Oy Energiaviraston toimeksiannosta. Hankkeen toteutuksesta vastaa taloustieteen asiantuntijoista koostuva työryhmä, jonka jäsenet ovat Professori Timo Kuosmanen (Turun kauppakorkeakoulu, hankkeesta vastaava johtaja), MMT Natalia Kuosmanen (Elinkeinoelämän tutkimuslaitos ETLA) ja KTT Sheng Dai (Aalto yliopiston ka...
Misallocation of labor and capital has attracted considerable interest in economics, how-ever, there is little empirical evidence from Finland’s business sector. This project examined misallocation by applying modern methods of economics and statistics to the register data of Statistics Finland on business enterprises in Finland.
The main results...
In the competitive market of price-taking firms, the profit-maximizing firms demand labor and capital inputs such that their marginal products equal the corresponding marginal costs. This chapter compares empirically whether this first-order condition is satisfied in 16 selected industries in Finland. To account for heterogeneity of firms even in n...
The question of optimal allocation of resources across sub-units has attracted considerable interest in the context of centralized decision-making systems such as bank branches or super-market chains. Drawing insight from these studies, in this chapter we examine how much the output and productivity of an industry could potentially increase if the...
Quantile regression and partial frontier are two distinct approaches to nonparametric quantile frontier estimation. In this article, we demonstrate that partial frontiers are not quantiles. Both convex and nonconvex technologies are considered. To this end, we propose convexified order-$\alpha$ as an alternative to convex quantile regression (CQR)...
The curse of dimensionality is a recognized challenge in nonparametric estimation. This paper develops a new L0-norm regularization approach to the convex quantile and expectile regressions for subset selection. We show how to use mixed-integer programming to solve the proposed L0-norm regularization approach in practice and build a link to the com...
Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A recent study by Wang et al. (2014) has proposed to address this problem by imposing non-crossing constraints to convex quantile regression. However, the non-crossing constraints may violate an intrinsic quantile property. This paper proposes a penaliz...
Redistribution of revenue in European football has attracted considerable interest in economics. However, empirical research on the redistributive role of the transfer fees remains scant. This article examines the ripple effect in the football transfer markets. Firstly, we adapt the theoretical explanations for the ripple effect found in the litera...
Shape-constrained nonparametric regression is a growing area in econometrics, statistics , operations research, machine learning and related fields. In the field of productivity and efficiency analysis, recent developments in the multivariate convex regression and related techniques such as convex quantile regression and convex expectile regression...
Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning and related fields. In the field of productivity and efficiency analysis, recent developments in the multivariate convex regression and related techniques such as convex quantile regression and convex expectile regression...
The curse of dimensionality is a recognized challenge in nonparametric estimation. This paper develops a new L0-norm regularization approach to the convex quantile and expectile regressions for subset variable selection. We show how to use mixed integer programming to solve the proposed L0-norm regularization approach in practice and build a link t...
Effective evaluation facilitates the implementation of production quota policy and establishment of future policy directions. However, reliable evaluations of the long-term impacts of production quota policies on critical minerals remain scant. In this study, we first derive hypotheses about the production quota policy and sustainable supply capaci...
COVID-19 virus has killed more than 2 million people around the world by January 2021. Hospitals are in the forefront in the battle against the pandemic. In this paper we empirically estimate the production function of COVID-19 caused death among hospital inpatients, incorporating contextual variables to the convex quantile regression approach. We...
Effective evaluation facilitates the implementation of production quota policy and establishment of future policy directions. However, reliable evaluations of the long-and short-term impacts of production quota policies on critical minerals remain scant. In this study, we first derive hypotheses about the production quota policy and sustainable sup...
COVID-19 is an unprecedent virus that had killed more than 1.2 million people around the world by November 2020. Thus far there has been little effort to examine performance of hospitals that are in the forefront in the battle against the pandemic. In this paper we propose a novel approach to assess the effectiveness of hospitals in saving lives. M...
Evaluation of abatement costs is critical in setting reduction goals and devising climate policy. However, reliable forward-looking assessment of the short-term effects of climate policy remains a major challenge. Using panel data of 30 Chinese provinces during 1997–2015, we first estimate the marginal CO2 abatement costs using a novel data-driven...
High economic cost of climate policy has attracted critical debate since the Kyoto Protocol. However, reliable empirical evidence of the abatement cost of green-house gases across countries remains scant. In this study we estimate the average yearly green-house gas abatement costs per capita for a panel of 28 OECD countries in years 1990–2015. The...
Seminar at JRC, Seville, Spain, 21st November 2019.
Evaluating CO2 emissions efficiency and estimating marginal abatement cost of CO2 emissions is critically essential for cost-efficient sustainable development and green mining. However, most empirical studies using frontier estimation methods grossly overestimate the efficiency and marginal abatement cost. Using China's mining industry data during...
Faced with the predicament of sustainable development in traditional cities, the low-carbon city, as a novel urban development mode, provides a feasible idea for resolving the tensions among urban development, resource conservation and environmental protection. Using prefecture-level panel data during 2007–2016, we adopt the difference-in-differenc...
Faced with the predicament of sustainable development in traditional cities, the low-carbon city, as a novel urban development mode, provides a feasible idea for resolving the tensions among urban development, resource conservation and environmental protection. According to the panel data of prefecture-level cities from 2007 to 2016, we adopt the D...
High economic cost of climate policy has attracted critical debate since the Kyoto Protocol. However, reliable empirical evidence of the abatement cost of greenhouse gases across countries remains scant. In this study we estimate the average yearly greenhouse gas abatement costs per capita for a panel of 28 OECD countries in years 1990-2015. The ma...
Green total factor productivity (GTFP) improvement is both an important engine driving sustainable economic growth and a critical path to handle resource constraints, environmental pollution, and ecological degradation. Using stochastic non-parametric envelopment of data (StoNED), this paper measures regional GTFP in China during 2000-2015, analyze...
This paper analyzes regional inequality in Western China through a case study of Guangxi Zhuang Autonomous Region from 1989 to 2012. We have found a recent trend of increasing county-level inequality, in which the distribution of regional inequality has changed from a single peak to a bimodal pattern. Based on the multi-mechanism framework, we have...
