A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data
We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE) to address challenges posed by high-dimensional socioeconomic data. Unlike classical linear dimensionality reduction methods, such as PCA and Lasso regression, the proposed SVAE effectively captures complex no...
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| Veröffentlicht in: | Journal of Economy and Technology Jg. 4; S. 9 - 19 |
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Elsevier B.V
2026
KeAi Communications Co., Ltd |
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| Abstract | We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE) to address challenges posed by high-dimensional socioeconomic data. Unlike classical linear dimensionality reduction methods, such as PCA and Lasso regression, the proposed SVAE effectively captures complex nonlinear interactions through supervised latent representation learning. Empirical analyses using comprehensive cross-country data from the World Bank (196 countries, 1997–2023) demonstrate the SVAE framework’s superior predictive accuracy, interpretability, and robustness in forecasting GDP growth, highlighting its potential for policy evaluation and macroeconomic forecasting. |
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| AbstractList | We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE) to address challenges posed by high-dimensional socioeconomic data. Unlike classical linear dimensionality reduction methods, such as PCA and Lasso regression, the proposed SVAE effectively captures complex nonlinear interactions through supervised latent representation learning. Empirical analyses using comprehensive cross-country data from the World Bank (196 countries, 1997–2023) demonstrate the SVAE framework’s superior predictive accuracy, interpretability, and robustness in forecasting GDP growth, highlighting its potential for policy evaluation and macroeconomic forecasting. |
| Author | Xue, Pei Li, Tianshun |
| Author_xml | – sequence: 1 givenname: Pei orcidid: 0009-0003-8543-8772 surname: Xue fullname: Xue, Pei email: peix324@yahoo.com organization: Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213, USA – sequence: 2 givenname: Tianshun surname: Li fullname: Li, Tianshun email: tianshunli88888@gmail.com organization: Katz School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA |
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| Cites_doi | 10.1111/rssb.12268 10.1561/2200000056 10.1080/14786440109462720 10.1214/11-EJS631 10.1214/13-AOS1106 10.1016/j.jedc.2022.104530 10.1080/07350015.2015.1102733 10.1080/07350015.2019.1609974 10.1016/S0893-6080(00)00026-5 10.1080/10618600.2023.2256502 10.1198/016214506000000735 10.1111/1468-0262.00273 10.1126/science.1243089 10.1002/wics.101 10.1037/h0071325 10.1016/j.jeconom.2017.08.009 10.1016/bs.hesmac.2016.04.002 10.1093/biomet/90.4.809 10.1080/01621459.2012.734168 10.1016/j.ejor.2016.08.058 10.1609/aaai.v39i19.34204 10.1109/TPAMI.2005.244 10.1257/jep.28.2.3 10.1257/jep.28.2.29 10.1073/pnas.1017031108 10.1111/j.1467-9868.2005.00503.x 10.1101/2024.02.05.578988 10.1016/j.jspi.2009.01.003 10.1016/S0169-7439(01)00155-1 10.1257/aer.104.5.394 10.1038/44565 10.1038/s43586-022-00184-w 10.1080/07350015.2024.2310022 10.1111/j.2517-6161.1996.tb02080.x 10.1257/jep.31.2.87 10.1002/wics.51 10.1093/restud/rdt044 |
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| Keywords | Dimensionality reduction High-dimensional data Socioeconomic forecasting Predictive modeling Nonlinear representation learning Supervised variational autoencoder |
| Language | English |
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| References | Ravikumar, P., Wainwright, M.J., Raskutti, G., Yu, B., 2011.High-dimensional covariance estimation by minimizing 1-penalized log-determinant divergence. Chen, Rohe (bib11) 2024; 33 Greenacre, Groenen, Hastie, d’Enza, Markos, Tuzhilina (bib17) 2022; 2 Cremer, C., Li, X., Duvenaud, D., 2018.Inference suboptimality in variational autoencoders, In: International conference on machine learning, PMLR.1078-1086. Schockaert, C., Macher, V., Schmitz, A., 2020.Vae-lime: deep generative model based approach for local data-driven model interpretability applied to the ironmaking industry.arXiv preprint arXiv:2007.10256. Varian (bib43) 2014; 28 Chen, Nordhaus (bib12) 2011; 108 Huang, Kou, Peng (bib21) 2017; 258 Abdi, Williams (bib2) 2010; 2 Lee, Seung (bib25) 1999; 401 Hoyer (bib20) 2004; 5 Qiao, D., Ma, X., Fan, J., 2025.Federated t-sne and umap for distributed data visualization, In: Proceedings of the AAAI Conference on Artificial Intelligence, 20014-20023. Zou (bib49) 2006; 101 Stock, Watson (bib38) 2016 Rezende, D.J., Mohamed, S., Wierstra, D., 2014.Stochastic backpropagation and approximate inference in deep generative models, In: International conference on machine learning, PMLR.1278-1286. Pötscher, Schneider (bib32) 2009; 139 Wong, Carter, Kohn (bib46) 2003; 90 Pearson (bib31) 1901; 2 Abdi (bib1) 2010; 2 Belloni, Chernozhukov, Hansen (bib7) 2014; 28 Einav, Levin (bib14) 2014; 346 Papailiopoulos, D., Dimakis, A., Korokythakis, S., 2013.Sparse pca through low-rank approximations, In: International Conference on Machine Learning, PMLR.747-755. Luo, Zhu (bib27) 2020; 38 Chatterjee, A., Lahiri, S.N., 2013.Rates of convergence of the adaptive lasso estimators to the oracle distribution and higher order refinements by the bootstrap. Tran, D., Hoffman, M.D., Saurous, R.A., Brevdo, E., Murphy, K., Blei, D.M., 2017.Deep probabilistic programming.arXiv preprint arXiv:1701.03757. Bernanke, Boivin, Eliasz (bib9) 2005; 120 Belloni, Chernozhukov, Hansen (bib6) 2014; 81 Zimnik, A.J., Ames, K.C., An, X., Driscoll, L., Lara, A.H., Russo, A.A., Susoy, V., Cunningham, J.P., Paninski, L., Churchland, M.M., et al., 2024.Identifying interpretable latent factors with sparse component analysis.bioRxiv. Wold, Sjöström, Eriksson (bib45) 2001; 58 Refinetti, M., Goldt, S., 2022.The dynamics of representation learning in shallow, non-linear autoencoders, In: International Conference on Machine Learning, PMLR.18499-18519. Filmer, Pritchett (bib16) 2001; 38 Jin, Lin, Zhang (bib23) 2024; 42 Bai, Ng (bib5) 2002; 70 Louizos, Shalit, Mooij, Sontag, Zemel, Welling (bib26) 2017 Vidal, Ma, Sastry (bib44) 2005; 27 Hyvärinen, Oja (bib22) 2000; 13 Takane (bib40) 2013; 129 Kingma, Welling (bib24) 2019; 12 Mullainathan, Spiess (bib29) 2017; 31 Athey, Imbens, Wager (bib4) 2018; 80 Zou, Hastie (bib50) 2005; 67 Tibshirani (bib41) 1996; 58 Hartford, J., Lewis, G., Leyton-Brown, K., Taddy, M., 2017.Deep iv: A flexible approach for counterfactual prediction, In: International Conference on Machine Learning, PMLR.1414-1423. Belloni, Chernozhukov, Hansen, Kozbur (bib8) 2016; 34 Hotelling (bib19) 1933; 24 Fan, Xue, Yao (bib15) 2017; 201 Moneta, Pallante (bib28) 2022; 144 Taddy (bib39) 2013; 108 Acemoglu, Autor, Dorn, Hanson, Price (bib3) 2014; 104 Zhou, S., van de Geer, S., Bühlmann, P., 2009.Adaptive lasso for high dimensional regression and gaussian graphical modeling.arXiv preprint arXiv:0903.2515. 10.1016/j.ject.2025.06.001_bib10 Fan (10.1016/j.ject.2025.06.001_bib15) 2017; 201 Hyvärinen (10.1016/j.ject.2025.06.001_bib22) 2000; 13 Stock (10.1016/j.ject.2025.06.001_bib38) 2016 Kingma (10.1016/j.ject.2025.06.001_bib24) 2019; 12 Taddy (10.1016/j.ject.2025.06.001_bib39) 2013; 108 Louizos (10.1016/j.ject.2025.06.001_bib26) 2017 Acemoglu (10.1016/j.ject.2025.06.001_bib3) 2014; 104 10.1016/j.ject.2025.06.001_bib18 Abdi (10.1016/j.ject.2025.06.001_bib2) 2010; 2 10.1016/j.ject.2025.06.001_bib13 Pötscher (10.1016/j.ject.2025.06.001_bib32) 2009; 139 Vidal (10.1016/j.ject.2025.06.001_bib44) 2005; 27 Wong (10.1016/j.ject.2025.06.001_bib46) 2003; 90 Pearson (10.1016/j.ject.2025.06.001_bib31) 1901; 2 Belloni (10.1016/j.ject.2025.06.001_bib6) 2014; 81 Chen (10.1016/j.ject.2025.06.001_bib11) 2024; 33 Jin (10.1016/j.ject.2025.06.001_bib23) 2024; 42 Varian (10.1016/j.ject.2025.06.001_bib43) 2014; 28 Moneta (10.1016/j.ject.2025.06.001_bib28) 2022; 144 Hoyer (10.1016/j.ject.2025.06.001_bib20) 2004; 5 Zou (10.1016/j.ject.2025.06.001_bib50) 2005; 67 10.1016/j.ject.2025.06.001_bib30 Zou (10.1016/j.ject.2025.06.001_bib49) 2006; 101 Hotelling (10.1016/j.ject.2025.06.001_bib19) 1933; 24 Wold (10.1016/j.ject.2025.06.001_bib45) 2001; 58 Filmer (10.1016/j.ject.2025.06.001_bib16) 2001; 38 Luo (10.1016/j.ject.2025.06.001_bib27) 2020; 38 Greenacre (10.1016/j.ject.2025.06.001_bib17) 2022; 2 Bai (10.1016/j.ject.2025.06.001_bib5) 2002; 70 10.1016/j.ject.2025.06.001_bib37 Tibshirani (10.1016/j.ject.2025.06.001_bib41) 1996; 58 10.1016/j.ject.2025.06.001_bib35 Chen (10.1016/j.ject.2025.06.001_bib12) 2011; 108 10.1016/j.ject.2025.06.001_bib36 Belloni (10.1016/j.ject.2025.06.001_bib7) 2014; 28 10.1016/j.ject.2025.06.001_bib33 Bernanke (10.1016/j.ject.2025.06.001_bib9) 2005; 120 Einav (10.1016/j.ject.2025.06.001_bib14) 2014; 346 10.1016/j.ject.2025.06.001_bib34 10.1016/j.ject.2025.06.001_bib42 Takane (10.1016/j.ject.2025.06.001_bib40) 2013; 129 Athey (10.1016/j.ject.2025.06.001_bib4) 2018; 80 Mullainathan (10.1016/j.ject.2025.06.001_bib29) 2017; 31 Lee (10.1016/j.ject.2025.06.001_bib25) 1999; 401 Abdi (10.1016/j.ject.2025.06.001_bib1) 2010; 2 Huang (10.1016/j.ject.2025.06.001_bib21) 2017; 258 10.1016/j.ject.2025.06.001_bib48 10.1016/j.ject.2025.06.001_bib47 Belloni (10.1016/j.ject.2025.06.001_bib8) 2016; 34 |
| References_xml | – reference: Zhou, S., van de Geer, S., Bühlmann, P., 2009.Adaptive lasso for high dimensional regression and gaussian graphical modeling.arXiv preprint arXiv:0903.2515. – volume: 58 start-page: 109 year: 2001 end-page: 130 ident: bib45 article-title: Pls-regression: a basic tool of chemometrics publication-title: Chemom. Intell. Lab. Syst. – volume: 139 start-page: 2775 year: 2009 end-page: 2790 ident: bib32 article-title: On the distribution of the adaptive lasso estimator publication-title: J. Stat. Plan. Inference – volume: 38 start-page: 115 year: 2001 end-page: 132 ident: bib16 article-title: Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in states of india publication-title: Demography – reference: Zimnik, A.J., Ames, K.C., An, X., Driscoll, L., Lara, A.H., Russo, A.A., Susoy, V., Cunningham, J.P., Paninski, L., Churchland, M.M., et al., 2024.Identifying interpretable latent factors with sparse component analysis.bioRxiv. – volume: 101 start-page: 1418 year: 2006 end-page: 1429 ident: bib49 article-title: The adaptive lasso and its oracle properties publication-title: J. Am. Stat. Assoc. – volume: 120 start-page: 387 year: 2005 end-page: 422 ident: bib9 article-title: Measuring the effects of monetary policy: a factor-augmented vector autoregressive (favar) approach publication-title: Q. J. Econ. – volume: 104 start-page: 394 year: 2014 end-page: 399 ident: bib3 article-title: Return of the solow paradox? it, productivity, and employment in us manufacturing publication-title: Am. Econ. Rev. – volume: 70 start-page: 191 year: 2002 end-page: 221 ident: bib5 article-title: Determining the number of factors in approximate factor models publication-title: Econometrica – volume: 2 start-page: 559 year: 1901 end-page: 572 ident: bib31 article-title: Liii. on lines and planes of closest fit to systems of points in space publication-title: Lond. Edinb. Dublin Philos. Mag. J. Sci. – reference: Tran, D., Hoffman, M.D., Saurous, R.A., Brevdo, E., Murphy, K., Blei, D.M., 2017.Deep probabilistic programming.arXiv preprint arXiv:1701.03757. – volume: 27 start-page: 1945 year: 2005 end-page: 1959 ident: bib44 article-title: Generalized principal component analysis (gpca) publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 2 start-page: 97 year: 2010 end-page: 106 ident: bib1 article-title: Partial least squares regression and projection on latent structure regression (pls regression) publication-title: Wiley Interdiscip. Rev. Comput. Stat. – volume: 90 start-page: 809 year: 2003 end-page: 830 ident: bib46 article-title: Efficient estimation of covariance selection models publication-title: Biometrika – volume: 129 start-page: 129 year: 2013 ident: bib40 article-title: Constrained principal component analysis and related techniques publication-title: Monogr. Stat. Appl. Probab. – reference: Ravikumar, P., Wainwright, M.J., Raskutti, G., Yu, B., 2011.High-dimensional covariance estimation by minimizing 1-penalized log-determinant divergence. – volume: 33 start-page: 421 year: 2024 end-page: 434 ident: bib11 article-title: A new basis for sparse principal component analysis publication-title: J. Comput. Graph. Stat. – volume: 346 year: 2014 ident: bib14 article-title: Economics in the age of big data publication-title: Science – volume: 108 start-page: 755 year: 2013 end-page: 770 ident: bib39 article-title: Multinomial inverse regression for text analysis publication-title: J. Am. Stat. Assoc. – volume: 58 start-page: 267 year: 1996 end-page: 288 ident: bib41 article-title: Regression shrinkage and selection via the lasso publication-title: J. R. Stat. Soc. Ser. B Stat. Methodol. – reference: Rezende, D.J., Mohamed, S., Wierstra, D., 2014.Stochastic backpropagation and approximate inference in deep generative models, In: International conference on machine learning, PMLR.1278-1286. – volume: 31 start-page: 87 year: 2017 end-page: 106 ident: bib29 article-title: Machine learning: an applied econometric approach publication-title: J. Econ. Perspect. – volume: 34 start-page: 590 year: 2016 end-page: 605 ident: bib8 article-title: Inference in high-dimensional panel models with an application to gun control publication-title: J. Bus. Econ. Stat. – volume: 201 start-page: 292 year: 2017 end-page: 306 ident: bib15 article-title: Sufficient forecasting using factor models publication-title: J. Econ. – reference: Cremer, C., Li, X., Duvenaud, D., 2018.Inference suboptimality in variational autoencoders, In: International conference on machine learning, PMLR.1078-1086. – volume: 67 start-page: 301 year: 2005 end-page: 320 ident: bib50 article-title: Regularization and variable selection via the elastic net publication-title: J. R. Stat. Soc. Ser. B Stat. Methodol. – volume: 2 start-page: 100 year: 2022 ident: bib17 article-title: Principal component analysis publication-title: Nat. Rev. Methods Prim. – volume: 144 year: 2022 ident: bib28 article-title: Identification of structural var models via independent component analysis: A performance evaluation study publication-title: J. Econ. Dyn. Control – reference: Refinetti, M., Goldt, S., 2022.The dynamics of representation learning in shallow, non-linear autoencoders, In: International Conference on Machine Learning, PMLR.18499-18519. – volume: 24 start-page: 417 year: 1933 ident: bib19 article-title: Analysis of a complex of statistical variables into principal components publication-title: J. Educ. Psychol. – volume: 38 start-page: 888 year: 2020 end-page: 900 ident: bib27 article-title: Matching using sufficient dimension reduction for causal inference publication-title: J. Bus. Econ. Stat. – reference: Papailiopoulos, D., Dimakis, A., Korokythakis, S., 2013.Sparse pca through low-rank approximations, In: International Conference on Machine Learning, PMLR.747-755. – start-page: 415 year: 2016 end-page: 525 ident: bib38 article-title: Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics publication-title: Handbook of macroeconomics – volume: 28 start-page: 3 year: 2014 end-page: 28 ident: bib43 article-title: Big data: New tricks for econometrics publication-title: J. Econ. Perspect. – volume: 108 start-page: 8589 year: 2011 end-page: 8594 ident: bib12 article-title: Using luminosity data as a proxy for economic statistics publication-title: Proc. Natl. Acad. Sci. – reference: Schockaert, C., Macher, V., Schmitz, A., 2020.Vae-lime: deep generative model based approach for local data-driven model interpretability applied to the ironmaking industry.arXiv preprint arXiv:2007.10256. – reference: Qiao, D., Ma, X., Fan, J., 2025.Federated t-sne and umap for distributed data visualization, In: Proceedings of the AAAI Conference on Artificial Intelligence, 20014-20023. – start-page: 30 year: 2017 ident: bib26 article-title: Causal effect inference with deep latent-variable models publication-title: Adv. Neural Inf. Process. Syst. – volume: 258 start-page: 692 year: 2017 end-page: 702 ident: bib21 article-title: Nonlinear manifold learning for early warnings in financial markets publication-title: Eur. J. Oper. Res. – reference: Chatterjee, A., Lahiri, S.N., 2013.Rates of convergence of the adaptive lasso estimators to the oracle distribution and higher order refinements by the bootstrap. – volume: 5 start-page: 1457 year: 2004 end-page: 1469 ident: bib20 article-title: Non-negative matrix factorization with sparseness constraints publication-title: J. Mach. Learn. Res. – volume: 401 start-page: 788 year: 1999 end-page: 791 ident: bib25 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: nature – volume: 12 start-page: 307 year: 2019 end-page: 392 ident: bib24 article-title: An introduction to variational autoencoders publication-title: Found. Trends® Mach. Learn. – reference: Hartford, J., Lewis, G., Leyton-Brown, K., Taddy, M., 2017.Deep iv: A flexible approach for counterfactual prediction, In: International Conference on Machine Learning, PMLR.1414-1423. – volume: 81 start-page: 608 year: 2014 end-page: 650 ident: bib6 article-title: Inference on treatment effects after selection among high-dimensional controls publication-title: Rev. Econ. Stud. – volume: 13 start-page: 411 year: 2000 end-page: 430 ident: bib22 article-title: Independent component analysis: algorithms and applications publication-title: Neural Netw. – volume: 80 start-page: 597 year: 2018 end-page: 623 ident: bib4 article-title: Approximate residual balancing: debiased inference of average treatment effects in high dimensions publication-title: J. R. Stat. Soc. Ser. B Stat. Methodol. – volume: 2 start-page: 433 year: 2010 end-page: 459 ident: bib2 article-title: Principal component analysis publication-title: Wiley Interdiscip. Rev. Comput. Stat. – volume: 28 start-page: 29 year: 2014 end-page: 50 ident: bib7 article-title: High-dimensional methods and inference on structural and treatment effects publication-title: J. Econ. Perspect. – volume: 42 start-page: 1318 year: 2024 end-page: 1330 ident: bib23 article-title: Identification and auto-debiased machine learning for outcome-conditioned average structural derivatives publication-title: J. Bus. Econ. Stat. – volume: 80 start-page: 597 year: 2018 ident: 10.1016/j.ject.2025.06.001_bib4 article-title: Approximate residual balancing: debiased inference of average treatment effects in high dimensions publication-title: J. R. Stat. Soc. Ser. B Stat. Methodol. doi: 10.1111/rssb.12268 – ident: 10.1016/j.ject.2025.06.001_bib42 – ident: 10.1016/j.ject.2025.06.001_bib13 – start-page: 30 year: 2017 ident: 10.1016/j.ject.2025.06.001_bib26 article-title: Causal effect inference with deep latent-variable models publication-title: Adv. Neural Inf. Process. Syst. – volume: 12 start-page: 307 year: 2019 ident: 10.1016/j.ject.2025.06.001_bib24 article-title: An introduction to variational autoencoders publication-title: Found. Trends® Mach. Learn. doi: 10.1561/2200000056 – volume: 2 start-page: 559 year: 1901 ident: 10.1016/j.ject.2025.06.001_bib31 article-title: Liii. on lines and planes of closest fit to systems of points in space publication-title: Lond. Edinb. Dublin Philos. Mag. J. Sci. doi: 10.1080/14786440109462720 – volume: 120 start-page: 387 year: 2005 ident: 10.1016/j.ject.2025.06.001_bib9 article-title: Measuring the effects of monetary policy: a factor-augmented vector autoregressive (favar) approach publication-title: Q. J. Econ. – ident: 10.1016/j.ject.2025.06.001_bib34 doi: 10.1214/11-EJS631 – ident: 10.1016/j.ject.2025.06.001_bib36 – ident: 10.1016/j.ject.2025.06.001_bib10 doi: 10.1214/13-AOS1106 – volume: 144 year: 2022 ident: 10.1016/j.ject.2025.06.001_bib28 article-title: Identification of structural var models via independent component analysis: A performance evaluation study publication-title: J. Econ. Dyn. Control doi: 10.1016/j.jedc.2022.104530 – ident: 10.1016/j.ject.2025.06.001_bib47 – ident: 10.1016/j.ject.2025.06.001_bib30 – volume: 34 start-page: 590 year: 2016 ident: 10.1016/j.ject.2025.06.001_bib8 article-title: Inference in high-dimensional panel models with an application to gun control publication-title: J. Bus. Econ. Stat. doi: 10.1080/07350015.2015.1102733 – volume: 38 start-page: 888 year: 2020 ident: 10.1016/j.ject.2025.06.001_bib27 article-title: Matching using sufficient dimension reduction for causal inference publication-title: J. Bus. Econ. Stat. doi: 10.1080/07350015.2019.1609974 – volume: 38 start-page: 115 year: 2001 ident: 10.1016/j.ject.2025.06.001_bib16 article-title: Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in states of india publication-title: Demography – volume: 13 start-page: 411 year: 2000 ident: 10.1016/j.ject.2025.06.001_bib22 article-title: Independent component analysis: algorithms and applications publication-title: Neural Netw. doi: 10.1016/S0893-6080(00)00026-5 – ident: 10.1016/j.ject.2025.06.001_bib37 – volume: 33 start-page: 421 year: 2024 ident: 10.1016/j.ject.2025.06.001_bib11 article-title: A new basis for sparse principal component analysis publication-title: J. Comput. Graph. Stat. doi: 10.1080/10618600.2023.2256502 – volume: 101 start-page: 1418 year: 2006 ident: 10.1016/j.ject.2025.06.001_bib49 article-title: The adaptive lasso and its oracle properties publication-title: J. Am. Stat. Assoc. doi: 10.1198/016214506000000735 – volume: 70 start-page: 191 year: 2002 ident: 10.1016/j.ject.2025.06.001_bib5 article-title: Determining the number of factors in approximate factor models publication-title: Econometrica doi: 10.1111/1468-0262.00273 – volume: 346 year: 2014 ident: 10.1016/j.ject.2025.06.001_bib14 article-title: Economics in the age of big data publication-title: Science doi: 10.1126/science.1243089 – volume: 2 start-page: 433 year: 2010 ident: 10.1016/j.ject.2025.06.001_bib2 article-title: Principal component analysis publication-title: Wiley Interdiscip. Rev. Comput. Stat. doi: 10.1002/wics.101 – volume: 24 start-page: 417 year: 1933 ident: 10.1016/j.ject.2025.06.001_bib19 article-title: Analysis of a complex of statistical variables into principal components publication-title: J. Educ. Psychol. doi: 10.1037/h0071325 – volume: 201 start-page: 292 year: 2017 ident: 10.1016/j.ject.2025.06.001_bib15 article-title: Sufficient forecasting using factor models publication-title: J. Econ. doi: 10.1016/j.jeconom.2017.08.009 – start-page: 415 year: 2016 ident: 10.1016/j.ject.2025.06.001_bib38 article-title: Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics doi: 10.1016/bs.hesmac.2016.04.002 – volume: 90 start-page: 809 year: 2003 ident: 10.1016/j.ject.2025.06.001_bib46 article-title: Efficient estimation of covariance selection models publication-title: Biometrika doi: 10.1093/biomet/90.4.809 – volume: 108 start-page: 755 year: 2013 ident: 10.1016/j.ject.2025.06.001_bib39 article-title: Multinomial inverse regression for text analysis publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.2012.734168 – volume: 258 start-page: 692 year: 2017 ident: 10.1016/j.ject.2025.06.001_bib21 article-title: Nonlinear manifold learning for early warnings in financial markets publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2016.08.058 – ident: 10.1016/j.ject.2025.06.001_bib33 doi: 10.1609/aaai.v39i19.34204 – volume: 27 start-page: 1945 year: 2005 ident: 10.1016/j.ject.2025.06.001_bib44 article-title: Generalized principal component analysis (gpca) publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.244 – volume: 28 start-page: 3 year: 2014 ident: 10.1016/j.ject.2025.06.001_bib43 article-title: Big data: New tricks for econometrics publication-title: J. Econ. Perspect. doi: 10.1257/jep.28.2.3 – volume: 28 start-page: 29 year: 2014 ident: 10.1016/j.ject.2025.06.001_bib7 article-title: High-dimensional methods and inference on structural and treatment effects publication-title: J. Econ. Perspect. doi: 10.1257/jep.28.2.29 – volume: 108 start-page: 8589 year: 2011 ident: 10.1016/j.ject.2025.06.001_bib12 article-title: Using luminosity data as a proxy for economic statistics publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.1017031108 – volume: 67 start-page: 301 year: 2005 ident: 10.1016/j.ject.2025.06.001_bib50 article-title: Regularization and variable selection via the elastic net publication-title: J. R. Stat. Soc. Ser. B Stat. Methodol. doi: 10.1111/j.1467-9868.2005.00503.x – ident: 10.1016/j.ject.2025.06.001_bib48 doi: 10.1101/2024.02.05.578988 – ident: 10.1016/j.ject.2025.06.001_bib18 – volume: 129 start-page: 129 year: 2013 ident: 10.1016/j.ject.2025.06.001_bib40 article-title: Constrained principal component analysis and related techniques publication-title: Monogr. Stat. Appl. Probab. – volume: 139 start-page: 2775 year: 2009 ident: 10.1016/j.ject.2025.06.001_bib32 article-title: On the distribution of the adaptive lasso estimator publication-title: J. Stat. Plan. Inference doi: 10.1016/j.jspi.2009.01.003 – volume: 58 start-page: 109 year: 2001 ident: 10.1016/j.ject.2025.06.001_bib45 article-title: Pls-regression: a basic tool of chemometrics publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/S0169-7439(01)00155-1 – volume: 104 start-page: 394 year: 2014 ident: 10.1016/j.ject.2025.06.001_bib3 article-title: Return of the solow paradox? it, productivity, and employment in us manufacturing publication-title: Am. Econ. Rev. doi: 10.1257/aer.104.5.394 – volume: 401 start-page: 788 year: 1999 ident: 10.1016/j.ject.2025.06.001_bib25 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: nature doi: 10.1038/44565 – volume: 2 start-page: 100 year: 2022 ident: 10.1016/j.ject.2025.06.001_bib17 article-title: Principal component analysis publication-title: Nat. Rev. Methods Prim. doi: 10.1038/s43586-022-00184-w – volume: 5 start-page: 1457 year: 2004 ident: 10.1016/j.ject.2025.06.001_bib20 article-title: Non-negative matrix factorization with sparseness constraints publication-title: J. Mach. Learn. Res. – volume: 42 start-page: 1318 year: 2024 ident: 10.1016/j.ject.2025.06.001_bib23 article-title: Identification and auto-debiased machine learning for outcome-conditioned average structural derivatives publication-title: J. Bus. Econ. Stat. doi: 10.1080/07350015.2024.2310022 – ident: 10.1016/j.ject.2025.06.001_bib35 – volume: 58 start-page: 267 year: 1996 ident: 10.1016/j.ject.2025.06.001_bib41 article-title: Regression shrinkage and selection via the lasso publication-title: J. R. Stat. Soc. Ser. B Stat. Methodol. doi: 10.1111/j.2517-6161.1996.tb02080.x – volume: 31 start-page: 87 year: 2017 ident: 10.1016/j.ject.2025.06.001_bib29 article-title: Machine learning: an applied econometric approach publication-title: J. Econ. Perspect. doi: 10.1257/jep.31.2.87 – volume: 2 start-page: 97 year: 2010 ident: 10.1016/j.ject.2025.06.001_bib1 article-title: Partial least squares regression and projection on latent structure regression (pls regression) publication-title: Wiley Interdiscip. Rev. Comput. Stat. doi: 10.1002/wics.51 – volume: 81 start-page: 608 year: 2014 ident: 10.1016/j.ject.2025.06.001_bib6 article-title: Inference on treatment effects after selection among high-dimensional controls publication-title: Rev. Econ. Stud. doi: 10.1093/restud/rdt044 |
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| SubjectTerms | Dimensionality reduction High-dimensional data Nonlinear representation learning Predictive modeling Socioeconomic forecasting Supervised variational autoencoder |
| Title | A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data |
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