Econometrics

Gathering empirical evidence relying on our strong expertise in panel data, time series econometrics and forecasting.

  • Burridge, P., Iacone, F., and Lazarová, Š. (2015). Spatial effects in a common trend model of US city-level CPI. Regional Science and Urban Economics, 54, 87-98. Paper
  • Chen, J. (2019). Estimating latent group structure in time-varying coefficient panel data models. The Econometrics Journal, 22(3), pp.223-240.
  • Chen, J., Gao, J., and Li, D. (2013). Estimation in partially linear single-index panel data models with fixed effects. Journal of Business & Economic Statistics, 31(3), 315-330. Paper
  • Chen, J., Gao, J., and Li, D. (2013). Estimation in single-index panel data models with heterogeneous link functions. Econometric Reviews, 32(8), 928-955. Paper
  • Chen, J., Li, D. and Xia, Y. (2019). Estimation of a rank-reduced functional-coefficient panel data model with serial correlation. Journal of Multivariate Analysis, 173, pp.456-479.
  • Chen, J., Shin, Y. and Zheng, C. (2021). Estimation and inference in heterogeneous spatial panels with a multifactor error structure. Journal of Econometrics.
  • Cho, J.S., Greenwood‐Nimmo, M. and Shin, Y. (2021). Recent developments of the autoregressive distributed lag modelling framework. Journal of Economic Surveys.
  • Coroneo, L., Jackson, L.E. and Owyang, M.T. (2020). International stock comovements with endogenous clusters. Journal of Economic Dynamics and Control, 116, p.103904.
  • Dang, V. A., Kim, M., and Shin, Y. (2015). In search of robust methods for dynamic panel data models in empirical corporate finance. Journal of Banking & Finance, 53, 84-98. Paper
  • Dang, V. A., Kim, M., and Shin, Y. (2014). Asymmetric adjustment toward optimal capital structure: Evidence from a crisis. International Review of Financial Analysis, 33, 226-242. Paper
  • 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.
  • Halunga, A.G., Orme, C.D. and Yamagata, T. (2017). A heteroskedasticity robust Breusch–Pagan test for Contemporaneous correlation in dynamic panel data models. Journal of Econometrics, 198(2), pp.209-230.
  • Kapetanios, G., Mitchell, J., and Shin, Y. (2014). A nonlinear panel data model of cross-sectional dependence. Journal of Econometrics, 179(2), 134-157. Paper
  • 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.
  • Mastromarco, C., Serlenga, L., and Shin, Y. (2016). Modelling Technical Efficiency in Cross Sectionally Dependent Stochastic Frontier Panels. Journal of Applied Econometrics, 31(1), 281-297. Paper
  • Norkutė, M., Sarafidis, V., Yamagata, T. and Cui, G. (2021). Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure. Journal of Econometrics, 220(2), pp.416-446.
  • 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.
  • Orme, C. D., and Yamagata, T. (2014). A Heteroskedasticity-Robust F-Test Statistic for Individual Effects. Econometric Reviews, 33(5-6), 431-471. Paper
  • Park, H. and Shin, Y. (2018). The effects of oil price on the Korean economy: A global VAR approach. Emerging Markets Finance and Trade, 54(5), pp.981-991.
  • Pesaran, M. H., Smith, L. V., and Yamagata, T. (2013). Panel unit root tests in the presence of a multifactor error structure. Journal of Econometrics, 175(2), 94-115. Paper
  • Seo, M. H., and Shin, Y. (2016). Dynamic panels with threshold effect and endogeneity. Journal of Econometrics, 195(2), 169-186. Paper
  • 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.
  • 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.
  • 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), 65-102. Paper
  • Chaudhuri, K., Greenwood‐Nimmo, M., Kim, M., and Shin, Y. (2013). On the Asymmetric U‐Shaped Relationship between Inflation, Inflation Uncertainty, and Relative Price Skewness in the UK. Journal of Money, Credit and Banking, 45(7), 1431-1449. Paper
  • Chen, S., Härdle, W.K. and Wang, W. (2022). The common and specific components of inflation expectations across European countries. Empirical Economics, 62(2), pp.553-580.
  • Chen, J., Gao, J., Li, D., and Lin, Z. (2015). Specification testing in nonstationary time series models. The Econometrics Journal, 18(1), 117-136. Paper
  • Chen, L., Wang, W. and Wu, W.B. (2021). Inference of breakpoints in high-dimensional time series. Journal of the American Statistical Association, pp.1-13.
  • Cho, J. S., Kim, T. H., and Shin, Y. (2015). Quantile cointegration in the autoregressive distributed-lag modelling framework. Journal of Econometrics, 188(1), 281-300. Paper
  • Cornea-Madeira, A. (2016). The Explicit Formula for the Hodrick-Prescott Filter in a Finite Sample. Review of economics and statistics, 99(2), 314-318. Paper
  • Cornea-Madeira, A., Hommes, C. and Massaro, D. (2019). Behavioural heterogeneity in US inflation dynamics. Journal of Business & Economic Statistics, 37(2), pp.288-300. Paper
  • 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. Paper
  • Coroneo, L., Iacone, F., Paccagnini, A. and Monteiro, P.S. (2022). Testing the predictive accuracy of COVID-19 forecasts. International Journal of Forecasting, pp.1-58.
  • Dees, S., Hashem Pesaran, M., Vanessa Smith, L., and Smith, R. P. (2014). Constructing Multi‐Country Rational Expectations Models. Oxford Bulletin of Economics and Statistics, 76(6), 812-840. Paper
  • Goliński, A. and Spencer, P. (2021). Modelling the Covid‐19 epidemic using time series econometrics. Health Economics, 30(11), pp.2808-2828.
  • Goliński, A., & Zaffaroni, P. (2016). Long memory affine term structure models. Journal of Econometrics, 191(1), 33-56. Paper
  • Greenwood-Nimmo, M., and Shin, Y. (2013). Taxation and the asymmetric adjustment of selected retail energy prices in the UK. Economics Letters, 121(3), 411-416. Paper
  • Hualde, J., and Iacone, F. (2017). Revisiting inflation in the euro area allowing for long memory. Economics Letters, 156, 145-150. Paper
  • Hualde, J., and Iacone, F. (2017). Fixed bandwidth asymptotics for the studentized mean of fractionally integrated processes. Economics Letters, 150, 39-43. Paper
  • Hualde, J., and Iacone, F. (2015). Small‐b and Fixed‐b Asymptotics for Weighted Covariance Estimation in Fractional Cointegration. Journal of Time Series Analysis, 36(4), 528-540. Paper
  • Iacone, F., Leybourne, S. J., and Taylor, A. R. (2013). Testing for a break in trend when the order of integration is unknown. Journal of Econometrics, 176(1), 30-45. Paper
  • Iacone, F., Leybourne, S. J., and Taylor, A. R. (2013). On the behaviour of fixed-b trend break tests under fractional integration. Econometric Theory, 29(2), 393-418. Paper
  • Iacone, F., Leybourne, S. J., and Robert Taylor, A. M. (2017). Testing for a Change in Mean under Fractional Integration. Journal of Time Series Econometrics, 9(1). Paper
  • Park, H., and Shin, Y. (2017). Exploring international linkages using generalised connectedness measures: The case of Korea. International Review of Economics & Finance, 50, 49-64. Paper
  • Polito, V. and Spencer, P. (2016). Optimal Control of Heteroscedastic Macroeconomic Models. Journal of Applied Econometrics, 31(7), pp.1430-1444. Paper
  • Thornton, M.A. (2019). Exact discrete representations of linear continuous time models with mixed frequency data. Journal of Time Series Analysis, 40(6), pp.951-967.
  • Thornton, M. A. (2014). The aggregation of dynamic relationships caused by incomplete information. Journal of Econometrics, 178, 342-351. Paper
  • Thornton, M. A. (2013). Removing seasonality under a changing regime: Filtering new car sales. Computational Statistics & Data Analysis, 58, 4-14. Paper
  • Thornton, M. A., and Chambers, M. J. (2017). Continuous time ARMA processes: discrete time representation and likelihood evaluation. Journal of Economic Dynamics and Control, 79, 48-65. Paper
  • Thornton, M. A., and Chambers, M. J. (2016). The exact discretisation of CARMA models with applications in finance. Journal of Empirical Finance, 38, 739-761. Paper
  • Thornton, M. A., and Chambers, M. J. (2013). Continuous‐time autoregressive moving average processes in discrete time: representation and embeddability. Journal of Time Series Analysis, 34(5), 552-561. Paper
  • Wickens, M., and Polito, V. (2014). How useful are DSGE macroeconomic models for forecasting? Open Economies Review, 25(1), 171-193. Paper
  • Boldea, O., Cornea-Madeira, A. and Hall, A.R. (2019). Bootstrapping structural change tests. Journal of Econometrics, 213(2), pp.359-397. Paper
  • Chen, J., Li, D., Linton, O. and Lu, Z. (2018). Semiparametric ultra-high dimensional model averaging of nonlinear dynamic time series. Journal of the American Statistical Association, 113(522), pp.919-932. Paper
  • Chen, J., Li, D. and Linton, O. (2019). A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables. Journal of Econometrics, 212(1), pp.155-176.
  • Chen, J., Li, D., Linton, O., & Lu, Z. (2016). Semiparametric dynamic portfolio choice with multiple conditioning variables. Journal of Econometrics, 194(2), 309-318. Paper
  • Chen, J., Li, D., Liang, H., & Wang, S. (2015). Semiparametric GEE analysis in partially linear single-index models for longitudinal data. The Annals of Statistics, 43(4), 1682-1715. Paper
  • 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.
  • 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.
  • Härdle, W.K., Okhrin, Y. and Wang, W. (2015). Uniform confidence bands for pricing kernels. Journal of Financial Econometrics, 13(2), pp.376-413.
  • Härdle, W.K., Wang, W. and Yu, L. (2016). Tenet: Tail-event driven network risk. Journal of Econometrics, 192(2), pp.499-513.
  • Iacone, F. and Lazarová, Š. (2019). Semiparametric detection of changes in long range dependence. Journal of Time Series Analysis, 40(5), pp.693-706.
  • Iacone, F., Nielsen, M.Ø. and Taylor, A.R. (2021). Semiparametric tests for the order of integration in the possible presence of level breaks. Journal of Business & Economic Statistics, pp.1-17.
  • Abadir, K.M. and Cornea-Madeira, A. (2019). Link of moments before and after transformations, with an application to resampling from fat-tailed distributions. Econometric Theory, 35(3), pp.630-652. Paper
  • Ando, T., Greenwood-Nimmo, M. and Shin, Y. (2022). Quantile Connectedness: Modelling Tail Behaviour in the Topology of Financial Networks. Management Science.
  • Bailey, N., Pesaran, M.H. and Smith, L. V. (2019). A multiple testing approach to the regularisation of large sample correlation matrices. Journal of Econometrics, 208(2), pp.507-534. Paper
  • Boldea, O., Cornea-Madeira, A. and Hall, A.R. (2019). Bootstrapping structural change tests. Journal of Econometrics, 213(2), pp.359-397.
  • 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.
  • Chambers, M.J., McCrorie, J.R. and Thornton, M.A. (2018). Continuous time modelling based on an exact discrete time representation. In Continuous time modelling in the behavioural and related sciences (pp. 317-357). Springer, Cham.
  • Cho, J.S., Greenwood‐Nimmo, M. and Shin, Y. (2021). Recent developments of the autoregressive distributed lag modelling framework. Journal of Economic Surveys.
  • Chen, J. (2019). Estimating latent group structure in time-varying coefficient panel data models. The Econometrics Journal, 22(3), pp.223-240.
  • Chen, J., Li, D. and Linton, O. (2019). A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables. Journal of Econometrics, 212(1), pp.155-176.
  • Chen, J., Li, D. and Xia, Y. (2019). Estimation of a rank-reduced functional-coefficient panel data model with serial correlation. Journal of Multivariate Analysis, 173, pp.456-479.
  • 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.
  • Chen, J., Shin, Y. and Zheng, C. (2021). Estimation and inference in heterogeneous spatial panels with a multifactor error structure. Journal of Econometrics.
  • Chen, L., Wang, W. and Wu, W.B. (2021). Inference of breakpoints in high-dimensional time series. Journal of the American Statistical Association, pp.1-13.
  • Chernozhukov, V., Härdle, W.K., Huang, C. and Wang, W. (2021). Lasso-driven inference in time and space. The Annals of Statistics, 49(3), pp.1702-1735.
  • Cho, J. S., Kim, T. H., and Shin, Y. (2015). Quantile cointegration in the autoregressive distributed-lag modelling framework. Journal of Econometrics, 188(1), 281-300. Paper
  • Choi, I., Lin, R. and Shin, Y. (2021). Canonical correlation-based model selection for the multilevel factors. Journal of Econometrics.
  • Cornea-Madeira, A., & Davidson, R. (2015). A parametric bootstrap for heavy-tailed distributions. Econometric Theory, 31(3), 449-470. Paper
  • Dang, V. A., Kim, M., and Shin, Y. (2015). In search of robust methods for dynamic panel data models in empirical corporate finance. Journal of Banking and Finance, 53, 84-98. Paper
  • Norkutė, M., Sarafidis, V., Yamagata, T. and Cui, G. (2021). Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure. Journal of Econometrics, 220(2), pp.416-446.
  • Orme, C. D., and Yamagata, T. (2014). A Heteroskedasticity-Robust F-Test Statistic for Individual Effects. Econometric Reviews, 33(5-6), 431-471. Paper
  • Pesaran, M. H., Smith, L. V., and Yamagata, T. (2013). Panel unit root tests in the presence of a multifactor error structure. Journal of Econometrics, 175(2), 94-115. Paper
  • Uematsu, Y. and Yamagata, T. (2022). Estimation of sparsity-induced weak factor models. Journal of Business & Economic Statistics, pp.1-15.
  • Uematsu, Y. and Yamagata, T. (2021). Inference in sparsity-induced weak factor models. Journal of Business & Economic Statistics, pp.1-34.
  • Zhu, X., Wang, W., Wang, H. and Härdle, W.K. (2019). Network quantile autoregression. Journal of Econometrics, 212(1), pp.345-358.