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題名 高維度自我向量迴歸於零售業行銷與銷售績效分析
High-Dimensional VAR for Retail Marketing & Sales Performance Analysis作者 朱家輝
Ju, Jia-Huei貢獻者 莊皓鈞<br>周彥君
Chuang, Hao-Chun<br>Chou, Yen-Chun
朱家輝
Ju, Jia-Huei關鍵詞 行銷
向量自我迴歸
Lasso
高維度資料
模擬
最佳化
Marketing
Vector Autoregression
Lasso
High-dimensional Data
Simulation
Optimization日期 2021 上傳時間 1-Oct-2021 10:02:57 (UTC+8) 摘要 零售業者在規劃商品行銷策略時，多仰賴各種資料分析技術作為輔助，而近年來許多零售與行銷研究，常採用計量模型評估行銷效果、預測銷售表現，然而在零售業中，因商品數量多、行銷手法多元，資料呈現高維度時間序列樣態，其待估計參數量多，導致模型估計不易。在本研究中採用向量自我迴歸模型建立估計系統，結合Lasso方法實證於真實零售業資料，並自估計結果解讀行銷效果，也發展各種零售業資料分析技術，提升實務應用價值。此外，本研究中也設計蒙地卡羅模擬實驗，評估模型是否能確實反應行銷效果，並提出基於Cross-Entropy方法的隨機最佳化演算法，與基於Lasso方法的迭代估計演算法進行比較，探討在高維度零售資料樣態時兩種方法的特性，於零售與行銷計量研究方法論發展貢獻。
Many retailers rely on various data analysis techniques to help planning marketing actions.In recent years, many retailing and marketing research often applied mathematical models to quantify the marketing effects and forecast the sales performances.However, in the retailing industry, the large number of products with the diversified marketing mix strategies lead to the inherent high-dimensional issue. Specifically, the amount of model parameters would be extremely large, which makes it difficult to estimate.In this study, we develop the estimation system based on Vector AutoRegression model combined with the Lasso method. Besides the explainable marketing results, we also aim to develop the additional tools which can provide more applicable information.To assess whether the estimation procedure can recover the true marketing effects outside the analyzed samples, we conduct Monte-Carlo simulation experiments and proposed the stochastic optimization algorithm based on Cross-Entropy method. We further compare it to iterative Lasso estimation procedure and figure out the characteristics of two methods in the case of high dimensional estimation. Making contribution on the theoretical development of retailing and marketing research.參考文獻 Aaker, D. A. and Keller, K. L. (1990). Consumer evaluations of brand extensions.Journal of Marketing, 54(1):27–41.Ailawadi, K. L., Harlam, B. A., C ́esar, J., and Trounce, D. (2006). Promotion profitability for a retailer: The role of promotion, brand, category, and store characteristics.Journal of Marketing Research, 43(4):518–535.Benham, T., Duan, Q., Kroese, D. P., and Liquet, B. (2017). Ceoptim: Cross-entropy r package for optimization.Journal of Statistical Software, Articles, 76(8):1–29.Bertsimas, D., King, A., and Mazumder, R. (2016). Best subset selection via a modern optimization lens.The Annals of Statistics, 44(2):813 – 852.Bertsimas, D., Lamperski, J., and Pauphilet, J. (2019). Certifiably optimal sparse inverse co-variance estimation.Mathematical Programming, 184(1-2):491–530.Bertsimas, D. and Parys, B. V. (2020). Sparse high-dimensional regression: Exact scalable algorithms and phase transitions.The Annals of Statistics, 48(1):300 – 323.Bronnenberg, B. J., Kruger, M. W., and Mela, C. F. (2008). Database paper—the iri marketing data set.Marketing Science, 27(4):745–748.Bronnenberg, B. J., Mahajan, V., and Vanhonacker, W. R. (2000). The emergence of market structure in new repeat-purchase categories: The interplay of market share and retailer distribution.Journal of Marketing Research, 37(1):16–31.Dekimpe, M. G. and Hanssens, D. M. (1995). The persistence of marketing effects on sales.Marketing Science, 14(1):1–21.Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linearmodels via coordinate descent.Journal of Statistical Software, 33(1):1–22.Friedman, J., Hastie, T., and Tibshirani, R. (2019).glasso: Graphical Lasso: Estimation of Gaussian Graphical Models. R package version 1.11.Gelper, S., Wilms, I., and Croux, C. (2016). Identifying demand effects in a large network of product categories.Journal of Retailing, 92(1):25–39.Hsu, N.-J., Hung, H.-L., and Chang, Y.-M. (2008). Subset selection for vector auto regressive processes using lasso.Computational Statistics Data Analysis, 52(7):3645–3657.Jedidi, K., Mela, C., and Gupta, S. (1999). Managing advertising and promotion for long-run profitability. Marketing Science, 18:1–22.Kapoor, S. G., Madhok, P., and Wu, S. M. (1981). Modeling and forecasting sales data by time series analysis.Journal of Marketing Research, 18(1):94–100.Ma, S. and Fildes, R. (2017). A retail store sku promotions optimization model for category multi-period profit maximization.Eur. J. Oper. Res., 260:680–692.Nicholson, W. B., Matteson, D. S., and Bien, J. (2017). VARX-L: Structured regularization for large vector autoregressions with exogenous variables.International Journal of Forecasting,33(3):627–651.Nicholson, W. B., Wilms, I., Bien, J., and Matteson, D. S. (2020). High dimensional forecasting via interpretable vector autoregression.Nijs, V., Dekimpe, M., Steenkamp, J.-B., and Hanssens, D. (2000). The category-demand effects of price promotions.Marketing Science, 20:1–22.Pauwels, K. (2018). Modeling dynamic relations among marketing and performance metrics.Foundations and Trends® in Marketing, 11:215–301.Pauwels, K., Hanssens, D. M., and Siddarth, S. (2002). The long-term effects of price pro-motions on category incidence, brand choice, and purchase quantity.Journal of MarketingResearch, 39(4):421–439.Rothman, A. J., Levina, E., and Zhu, J. (2010). Sparse multivariate regression with covariance estimation.Journal of Computational and Graphical Statistics, 19(4):947–962. PMID:24963268.Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization.Methodology And Computing In Applied Probability, 1:127–190.Rubinstein, R. Y. (1997). Optimization of computer simulation models with rare events.European Journal of Operational Research, 99(1):89–112.Rubinstein, R. Y. and Kroese, D. P. (2004).The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-Carlo Simulation (Information Science and Statistics).Springer-Verlag, Berlin, Heidelberg.Said, S. E. and Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order.Biometrika, 71(3):599–607.Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1):1–48.Song, I. and Chintagunta, P. K. (2007). A discrete: Continuous model for multicategory purchase behavior of households.Journal of Marketing Research, 44(4):595–612.Tibshirani, R. (1996). Regression shrinkage and selection via the lasso.Journal of the Royal Statistical Society. Series B (Methodological), 58(1):267–288.Vinod, H. D. and de Lacalle, J. L. (2009). Maximum entropy bootstrap for time series: Themeboot r package.Journal of Statistical Software, Articles, 29(5):1–19.Wilms, I., Barbaglia, L., and Croux, C. (2018). Multiclass vector auto-regressive models for multistore sales data.Journal of the Royal Statistical Society: Series C (Applied Statistics),67(2):435–452.Wilms, I. and Croux, C. (2018). An algorithm for the multivariate group lasso with covariance estimation.Journal of Applied Statistics, 45(4):668–681.Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables.Journal of the Royal Statistical Society Series B, 68(1):49–67 描述 碩士
國立政治大學
資訊管理學系
108356024資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108356024 資料類型 thesis dc.contributor.advisor 莊皓鈞<br>周彥君 zh_TW dc.contributor.advisor Chuang, Hao-Chun<br>Chou, Yen-Chun en_US dc.contributor.author (Authors) 朱家輝 zh_TW dc.contributor.author (Authors) Ju, Jia-Huei en_US dc.creator (作者) 朱家輝 zh_TW dc.creator (作者) Ju, Jia-Huei en_US dc.date (日期) 2021 en_US dc.date.accessioned 1-Oct-2021 10:02:57 (UTC+8) - dc.date.available 1-Oct-2021 10:02:57 (UTC+8) - dc.date.issued (上傳時間) 1-Oct-2021 10:02:57 (UTC+8) - dc.identifier (Other Identifiers) G0108356024 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/137283 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 108356024 zh_TW dc.description.abstract (摘要) 零售業者在規劃商品行銷策略時，多仰賴各種資料分析技術作為輔助，而近年來許多零售與行銷研究，常採用計量模型評估行銷效果、預測銷售表現，然而在零售業中，因商品數量多、行銷手法多元，資料呈現高維度時間序列樣態，其待估計參數量多，導致模型估計不易。在本研究中採用向量自我迴歸模型建立估計系統，結合Lasso方法實證於真實零售業資料，並自估計結果解讀行銷效果，也發展各種零售業資料分析技術，提升實務應用價值。此外，本研究中也設計蒙地卡羅模擬實驗，評估模型是否能確實反應行銷效果，並提出基於Cross-Entropy方法的隨機最佳化演算法，與基於Lasso方法的迭代估計演算法進行比較，探討在高維度零售資料樣態時兩種方法的特性，於零售與行銷計量研究方法論發展貢獻。 zh_TW dc.description.abstract (摘要) Many retailers rely on various data analysis techniques to help planning marketing actions.In recent years, many retailing and marketing research often applied mathematical models to quantify the marketing effects and forecast the sales performances.However, in the retailing industry, the large number of products with the diversified marketing mix strategies lead to the inherent high-dimensional issue. Specifically, the amount of model parameters would be extremely large, which makes it difficult to estimate.In this study, we develop the estimation system based on Vector AutoRegression model combined with the Lasso method. Besides the explainable marketing results, we also aim to develop the additional tools which can provide more applicable information.To assess whether the estimation procedure can recover the true marketing effects outside the analyzed samples, we conduct Monte-Carlo simulation experiments and proposed the stochastic optimization algorithm based on Cross-Entropy method. We further compare it to iterative Lasso estimation procedure and figure out the characteristics of two methods in the case of high dimensional estimation. Making contribution on the theoretical development of retailing and marketing research. en_US dc.description.tableofcontents 第一章 緒論 1第二章 文獻探討 3第一節 VAR向量自我迴歸模型 3第二節 VAR-Lasso稀疏向量自我迴歸 4第三章 資料與模型 8第一節 資料集 8第二節 分析模型 12第三節 稀疏估計 15第四章 實證分析 20第一節 品類銷售成長效果分析 20第五章 隨機最佳化與模擬實驗 26第一節 高維度隨機最佳化 27第二節 模擬實驗 31第六章 結論 39參考文獻 40 zh_TW dc.format.extent 3695249 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108356024 en_US dc.subject (關鍵詞) 行銷 zh_TW dc.subject (關鍵詞) 向量自我迴歸 zh_TW dc.subject (關鍵詞) Lasso zh_TW dc.subject (關鍵詞) 高維度資料 zh_TW dc.subject (關鍵詞) 模擬 zh_TW dc.subject (關鍵詞) 最佳化 zh_TW dc.subject (關鍵詞) Marketing en_US dc.subject (關鍵詞) Vector Autoregression en_US dc.subject (關鍵詞) Lasso en_US dc.subject (關鍵詞) High-dimensional Data en_US dc.subject (關鍵詞) Simulation en_US dc.subject (關鍵詞) Optimization en_US dc.title (題名) 高維度自我向量迴歸於零售業行銷與銷售績效分析 zh_TW dc.title (題名) High-Dimensional VAR for Retail Marketing & Sales Performance Analysis en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Aaker, D. A. and Keller, K. L. (1990). Consumer evaluations of brand extensions.Journal of Marketing, 54(1):27–41.Ailawadi, K. L., Harlam, B. A., C ́esar, J., and Trounce, D. (2006). Promotion profitability for a retailer: The role of promotion, brand, category, and store characteristics.Journal of Marketing Research, 43(4):518–535.Benham, T., Duan, Q., Kroese, D. P., and Liquet, B. (2017). Ceoptim: Cross-entropy r package for optimization.Journal of Statistical Software, Articles, 76(8):1–29.Bertsimas, D., King, A., and Mazumder, R. (2016). Best subset selection via a modern optimization lens.The Annals of Statistics, 44(2):813 – 852.Bertsimas, D., Lamperski, J., and Pauphilet, J. (2019). Certifiably optimal sparse inverse co-variance estimation.Mathematical Programming, 184(1-2):491–530.Bertsimas, D. and Parys, B. V. (2020). Sparse high-dimensional regression: Exact scalable algorithms and phase transitions.The Annals of Statistics, 48(1):300 – 323.Bronnenberg, B. J., Kruger, M. W., and Mela, C. F. (2008). Database paper—the iri marketing data set.Marketing Science, 27(4):745–748.Bronnenberg, B. J., Mahajan, V., and Vanhonacker, W. R. (2000). The emergence of market structure in new repeat-purchase categories: The interplay of market share and retailer distribution.Journal of Marketing Research, 37(1):16–31.Dekimpe, M. G. and Hanssens, D. M. (1995). The persistence of marketing effects on sales.Marketing Science, 14(1):1–21.Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linearmodels via coordinate descent.Journal of Statistical Software, 33(1):1–22.Friedman, J., Hastie, T., and Tibshirani, R. (2019).glasso: Graphical Lasso: Estimation of Gaussian Graphical Models. R package version 1.11.Gelper, S., Wilms, I., and Croux, C. (2016). Identifying demand effects in a large network of product categories.Journal of Retailing, 92(1):25–39.Hsu, N.-J., Hung, H.-L., and Chang, Y.-M. (2008). Subset selection for vector auto regressive processes using lasso.Computational Statistics Data Analysis, 52(7):3645–3657.Jedidi, K., Mela, C., and Gupta, S. (1999). Managing advertising and promotion for long-run profitability. Marketing Science, 18:1–22.Kapoor, S. G., Madhok, P., and Wu, S. M. (1981). Modeling and forecasting sales data by time series analysis.Journal of Marketing Research, 18(1):94–100.Ma, S. and Fildes, R. (2017). A retail store sku promotions optimization model for category multi-period profit maximization.Eur. J. Oper. Res., 260:680–692.Nicholson, W. B., Matteson, D. S., and Bien, J. (2017). VARX-L: Structured regularization for large vector autoregressions with exogenous variables.International Journal of Forecasting,33(3):627–651.Nicholson, W. B., Wilms, I., Bien, J., and Matteson, D. S. (2020). High dimensional forecasting via interpretable vector autoregression.Nijs, V., Dekimpe, M., Steenkamp, J.-B., and Hanssens, D. (2000). The category-demand effects of price promotions.Marketing Science, 20:1–22.Pauwels, K. (2018). Modeling dynamic relations among marketing and performance metrics.Foundations and Trends® in Marketing, 11:215–301.Pauwels, K., Hanssens, D. M., and Siddarth, S. (2002). The long-term effects of price pro-motions on category incidence, brand choice, and purchase quantity.Journal of MarketingResearch, 39(4):421–439.Rothman, A. J., Levina, E., and Zhu, J. (2010). Sparse multivariate regression with covariance estimation.Journal of Computational and Graphical Statistics, 19(4):947–962. PMID:24963268.Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization.Methodology And Computing In Applied Probability, 1:127–190.Rubinstein, R. Y. (1997). Optimization of computer simulation models with rare events.European Journal of Operational Research, 99(1):89–112.Rubinstein, R. Y. and Kroese, D. P. (2004).The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-Carlo Simulation (Information Science and Statistics).Springer-Verlag, Berlin, Heidelberg.Said, S. E. and Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order.Biometrika, 71(3):599–607.Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1):1–48.Song, I. and Chintagunta, P. K. (2007). A discrete: Continuous model for multicategory purchase behavior of households.Journal of Marketing Research, 44(4):595–612.Tibshirani, R. (1996). Regression shrinkage and selection via the lasso.Journal of the Royal Statistical Society. Series B (Methodological), 58(1):267–288.Vinod, H. D. and de Lacalle, J. L. (2009). Maximum entropy bootstrap for time series: Themeboot r package.Journal of Statistical Software, Articles, 29(5):1–19.Wilms, I., Barbaglia, L., and Croux, C. (2018). Multiclass vector auto-regressive models for multistore sales data.Journal of the Royal Statistical Society: Series C (Applied Statistics),67(2):435–452.Wilms, I. and Croux, C. (2018). An algorithm for the multivariate group lasso with covariance estimation.Journal of Applied Statistics, 45(4):668–681.Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables.Journal of the Royal Statistical Society Series B, 68(1):49–67 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202101578 en_US
