(Since July 2016)
Jiang, P., Yamagata, T., 2025, "An alternative bootstrap procedure for factor-augmented regression models", arXiv:2510.00947
Chen, J., Cui, G., Sarafidis, V., Yamagata, T., 2025, "IV estimation of heterogeneous spatial dynamic panel models with interactive effects", arXiv:2501.18467
Jiang, P., Uematsu, Y., Yamagata, T., 2024, "Bias Correction in Factor-Augmented Regression Models with Weak Factors", arXiv:2509.02066
Jiang, P., Uematsu, Y., Yamagata, T., 2023, "Revisiting Asymptotic Theory for Principal Component Estimators of Approximate Factor Models", arXiv:2311.00625.
Hayakawa, K., Yamagata, T., 2022, "Linear Panel Regression Models with Non-Classical Measurement Errors: An Application to Investment Equations", SSRN 4161393
Lin, R. and Shin, Y., 2022. "Generalised Canonical Correlation Estimation of the Multilevel Factor Model", Available at SSRN 4295429.
JS Cho, M Greenwood-Nimmo, Y Shin, 2019, "Two-Step Estimation of the Nonlinear Autoregressive Distributed Lag Model", Yonsei Economics Research Institute Working papers.
G Kapetanios, L Serlenga, Y Shin, 2019, ''Testing for Correlated Factor Loadings in Cross Sectionally Dependent Panels,'' SERIES Working papers N. 02/2019.
Bravo, F., 2026. A uniform model selection test for semiparametric models. Statistics & Probability Letters, 232, p.110658.
Xie, T., Chaudhuri, K., Jin, H. and Shin, Y., 2025. Systematic common components in ESG ratings across legal origins. International Review of Financial Analysis, p.104814.
Bravo, F., 2025. Estimation and inference for quantile partially linear varying coefficients models with missing observations. Journal of Nonparametric Statistics, pp.1-44.
Bravo, F., 2025. Estimation of non‐smooth non‐parametric estimating equations models with dependent data. Journal of Time Series Analysis, 46(1), pp.59-80.
Uematsu, Y. and Yamagata, T., 2025. Discovering the network Granger causality in large vector autoregressive models. Journal of the American Statistical Association, 120(552), pp.2385-2396.
Chen, J., Li, D., Li, Y.N. and Linton, O., 2025. Estimating time-varying networks for high-dimensional time series. Journal of Econometrics, 249, p.105941.
Pesaran, M.H. and Yamagata, T., 2024, Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities, Journal of Financial Econometrics, 22(2), pp.407-460.
Ikefuji, M., Magnus, J.R. and Yamagata, T., 2024. Revealing priors from posteriors with an application to inflation forecasting in the UK. The Econometrics Journal, 27(1), pp.151-170.
Li, J., Chen, L., Wang, W. and Wu, W.B., 2024. ℓ 2 inference for change points in high-dimensional time series via a Two-Way MOSUM. The Annals of Statistics, 52(2), pp.602-627.
Xu, X., Wang, W., Shin, Y. and Zheng, C., 2024. Dynamic network quantile regression model. Journal of Business & Economic Statistics, 42(2), pp.407-421.
Kapetanios, G., Serlenga, L. and Shin, Y., 2024. An LM test for the conditional independence between regressors and factor loadings in panel data models with interactive effects. Journal of Business & Economic Statistics, 42(2), pp.743-761.
Cui, G., Sarafidis, V. and Yamagata, T., 2023. IV estimation of spatial dynamic panels with interactive effects: large sample theory and an application on bank attitude towards risk. The Econometrics Journal, 26(2), pp.124-146.
Cui, G., Hayakawa, K., Nagata, S., Yamagata, T., 2023, A robust approach to heteroskedasticity, error serial correlation and slope heterogeneity in linear models with interactive effects for large panel data, Journal of Business & Economic Statistics, 41(3), 862-875.
Hayakawa, K., Pesaran, M.H. and Smith, LV., 2023. Short T dynamic panel data models with individual, time and interactive effects. Journal of Applied Econometrics, 38(6), pp.940-967.
Li, Y.N., Chen, J. and Linton, O., 2023. Estimation of common factors for microstructure noise and efficient price in a high-frequency dual factor model. Journal of Econometrics, p.105382.
Choi, I., Lin, R. and Shin, Y., 2023. Canonical correlation-based model selection for the multilevel factors. Journal of Econometrics, 233(1), pp.22-44.
Im, K.S., Pesaran, M.H. and Shin, Y., 2023. Reflections on Testing for unit roots in heterogeneous panels. Journal of Econometrics, 234, pp.111-114.
Shin, Y., Kapetanios, G. and Serlenga, L., 2023. Testing for Correlation between the Regressors and Factor Loadings in Heterogeneous Panels with Interactive Effects. Empirical Economics, 64(6), pp.2611-2659.
Cho, J.S., Greenwood-Nimmo, M. and Shin, Y., 2023. The asymmetric response of dividends to earnings news. Finance Research Letters, 54, p.103792.
Greenwood-Nimmo, M., Nguyen, V.H. and Shin, Y., 2023. What is mine is yours: Sovereign risk transmission during the European debt crisis. Journal of Financial Stability, 65, p.101103.
Bravo, F., 2023. Local polynomial estimation of nonparametric general estimating equations. Statistics & Probability Letters, 197, p.109805.
Chen, L., Wang, W. and Wu, W.B., 2022. Inference of breakpoints in high-dimensional time series. Journal of the American Statistical Association, 117(540), pp.1951-1963.
Bravo, F., 2022. Second order expansions of estimators in nonparametric moment conditions models with weakly dependent data. Econometric Reviews, 41(6), pp.583-606.
Bravo, F., 2022. Misspecified semiparametric model selection with weakly dependent observations. Journal of Time Series Analysis, 43(4), pp.558-586.
Ando, T., Greenwood-Nimmo, M. and Shin, Y., 2022. Quantile connectedness: modeling tail behavior in the topology of financial networks. Management Science, 68(4), pp.2401-2431.
Keilbar, G. and Wang, W., 2022. Modelling systemic risk using neural network quantile regression. Empirical Economics, 62(1), pp.93-118.
Uematsu, Y. and Yamagata, T., 2022. Inference in sparsity-induced weak factor models. Journal of Business & Economic Statistics, 41(1), pp.126-139.
Uematsu, Y. and Yamagata, T., 2022. Estimation of sparsity-induced weak factor models. Journal of Business & Economic Statistics, 41(1), pp.213-227.
Cui, G., Norkutė, M., Sarafidis, V. and Yamagata, T., 2022. Two-stage instrumental variable estimation of linear panel data models with interactive effects. The Econometrics Journal, 25(2), pp.340-361.
Chen, J., Shin, Y. and Zheng, C., 2022. Estimation and inference in heterogeneous spatial panels with a multifactor error structure. Journal of Econometrics, 229(1), pp.55-79.
Ando, T., Greenwood-Nimmo, M. and Shin, Y., 2022. Quantile connectedness: modeling tail behavior in the topology of financial networks. Management Science, 68(4), pp.2401-2431.
Chen, J., Cai, C.X., Faff, R. and Shin, Y., 2022. Nonlinear limits to arbitrage. Journal of Futures Markets, 42(6), pp.1084-1113.
Jang, H. and Shin, Y., 2022. Spatial Attendance Spillover in Football Leagues. International Journal of Empirical Economics, 1(03), p.2250010.
Chen, J., Li D., Wei, L. and Zhang, W., 2021. Nonparametric homogeneity pursuit in functional-coefficient models. Journal of Nonparametric Statistics, 33(3-4), pp.387-416.
Smith, L.V., Tarui, N. and Yamagata, T., 2021. Assessing the impact of COVID-19 on global fossil fuel consumption and CO2 emissions. Energy economics, 97, p.105170.
Chernozhukov, V., Karl Härdle, W., Huang, C. and Wang, W., 2021. Lasso-driven inference in time and space. The Annals of Statistics, 49(3), pp.1702-1735.
Kapetanios, G., Serlenga, L. and Shin, Y., 2021. Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure. Journal of Econometrics, 220(2), pp.504-531.
Greenwood-Nimmo, M., Nguyen, V.H. and Shin, Y., 2021. Measuring the connectedness of the global economy. International Journal of Forecasting, 37(2), pp.899-919.
Serlenga, L. and Shin, Y., 2021. Gravity models of interprovincial migration flows in Canada with hierarchical multifactor structure. Empirical Economics, 60(1), pp.365-390.
Bravo, F., Li D. and Tjøstheim, D., 2021. Robust nonlinear regression estimation in null recurrent time series. Journal of Econometrics, 224(2), pp.416-438.
Norkute, M., Sarafidis, V., Yamagata, T., Cui, G., 2021 , Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure. Journal of Econometrics 220, 416-446.
Coroneo, L. and Iacone, F., 2020. Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics. Journal of Applied Econometrics, 35(4), pp.391-409.
Chen, L., Wang, W. and Wu, W.B., 2021. Dynamic semiparametric factor model with structural breaks. Journal of Business & Economic Statistics, 39(3), pp.757-771.
Hou, A.J., Wang, W., Chen, C.Y. and Härdle, W.K., 2020. Pricing cryptocurrency options. Journal of Financial Econometrics, 18(2), pp.250-279.
M Greenwood-Nimmo, V Nguyen, Y Shin, 2019, ''4 Quantifying informational linkages in a global model of currency spot markets ,'' International Financial Markets, Volume 1.
Chen, J., 2019, Estimating Latent Group Structure in Time-Varying Coefficient Panel Data Models, Econometrics Journal, 22(3) 223-240.
Cai, C.X., Kim, M., Shin, Y.. and Zhang, Q., 2019. FARVaR: functional autoregressive value-at-risk. Journal of Financial Econometrics, 17(2), pp.284-337.
Chen, J., Li, D., Linton, O., 2018, A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables, Journal of Econometrics, 212(1), 155-176.
Bailey, N., Pesaran, M.H., Smith, L.V., 2018, A multiple testing approach to the regularisation of large sample correlation matrices, Journal of Econometrics, 208(2), 507-534.
T Omay, M Hasanov, Y Shin, 2018, Testing for Unit Roots in Dynamic Panels with Smooth Breaks and Cross-Sectionally Dependent Errors, Computational Economics, 52 (1), 167-193.
Shin, Y., Greenwood-Nimmo, M. & Nguyen, V. , 2018, Quantifying Informational Linkages in a Global Model of Currency Spot Markets, Advances in Applied Financial Econometrics. Routledge.
Chen, J., Li, D., Linton, O., Lu, Z., 2018, Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series, Journal of the American Statistical Association, 1-30.
Omay, T., Hasanov, M. and Shin, Y., 2018. Testing for unit roots in dynamic panels with smooth breaks and cross-sectionally dependent errors. Computational Economics, 52(1), pp.167-193.
Park, H., Shin, Y., 2018, The Effects of Oil Price on the Korean Economy: A Global VAR Approach, Emerging Markets Finance and Trade, 54, 981-991.
Kapetanios, G., Mastromarco, C., Serlenga, L., Shin Y., 2017, Multi-dimensional Panel Data Modelling in the Presence of Cross Sectional Error Dependence , The Econometrics of Multi-dimensional Panels, 291-322.
Park, H., Shin, Y., 2017, Exploring international linkages using generalised connectedness measures: The case of Korea, International Review of Economics and Finance, 50, 49-64.
Shin, Y., Kapetanios, G., Mastromarco, C., Serlenga, L., 2017, Modelling in the presence of cross sectional error dependence, The Econometrics of Multi-dimensional Panels: Theory and Applications. Matyas, L. (ed.). Springer, 31 p.
Chen, J., Li, D., Linton, O., Lu, Z., 2018, Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series, Journal of the American Statistical Association, 1-30.
Halunga, A., Orme, C.D., Yamagata, T., 2017, A Heteroskedasticity Robust Breusch-Pagan Test for Contemporaneous Correlation in Dynamic Panel Data Models, Journal of Econometrics, 198, 209-230.
Seo, M.H. and Shin, Y., 2016. Dynamic panels with threshold effect and endogeneity. Journal of econometrics, 195(2), pp.169-186.
Shin, Y. & Seo, M. H., 2016, Inference for Dynamic Panels with Threshold Effect and Endogeneity, Journal of Econometrics, 195, 169-186
Mastromarco, C., Serlenga, L. and Shin, Y., 2016. Modelling technical efficiency in cross sectionally dependent stochastic frontier panels. Journal of Applied Econometrics, 31(1), pp.281-297.
Chaudhuri, K., Kim, M. and Shin, Y., 2016. Forecasting distributions of inflation rates: the functional auto-regressive approach. Journal of the Royal Statistical Society Series A: Statistics in Society, 179(1), pp.65-102.
Chen, J., Li D., Linton, O., Lu, Z., 2016, Semiparametric dynamic portfolio choice with multiple conditioning variables, Journal of Econometrics, 194, 309-318.