[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119702-en":3,"doc-seo-119702-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119702,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Reevaluating the Taylor Rule with Machine Learning","Reevaluating the Taylor Rule with a linear OLS approach and a nonlinear machine-learning model aims to produce federal funds rate estimates that closely match rates actually implemented by the Federal Reserve Bank. The linear method refits Taylor-type coefficients using inflation, inflation gap, and output gap, interpreting the intercept as the equilibrium real interest rate target. The nonlinear method learns the mapping from inflation rate and output gap to the funds rate via gradient-descent error minimization, improving accuracy through realistic nonlinear relationships. Results show near-identical actual versus estimated rates, except around three recession episodes tied to bubble bursts, discussed in concluding remarks.","A.D. Karakas 1  \nReevaluating the Taylor Rule with Machine Learning  \nAlper D. Karakas  \nApril 2021  \nAbstract  \nThis paper aims to reevaluate the Taylor Rule, through a linear and a nonlinear method, such that its estimated federal funds rates match those actually previously implemented by the Federal Reserve Bank. In the linear method, this paper uses an OLS regression model to find more accurate coeficients within the same Taylor Rule equation in which the dependent variable is the federal funds rate, and the independent variables are the inflation rate, the inflation gap, and the output gap. The intercept in the OLS regression model would capture the constant equilibrium target real interest rate set at 2. The linear OLS method suggests that the Taylor Rule overestimates the output gap and standalone inflation rate s’ coeficients for the Taylor Rule. The coeficients this paper suggests are shown in equation (2). In the nonlinear method, this paper uses a machine learning system in which the two inputs are the inflation rate and the output gap and the output is the federal funds rate. This system utilizes gradient descent error minimization to create a model that minimizes the error between the estimated federal funds rate and the actualpreviously implemented federal funds rate. Since the machine learning system allows the model to capture the more realistic nonlinear relationship between the variables, it significantly increases the estimation accuracy as a result. The actual and estimated federal funds rates are almost identical besides three recessions caused by bubble bursts, which the paper addresses in the concluding remarks. Overall, the first method provides theoretical insight while the second suggests a model with improved applicability.  \nA.D. Karakas 2  \nI. Introduction  \nThe Taylor Rule is a monetary policy tool to assess the target interest rate for a central bank by using monetary policy indicators. These are the real interest rate, the inflation rate gap, and output growth gap. The equation is shown below in equation (1) .  \n(1) it = πt + r* + 􁶔π (πt- πt*) + 􁶔y (yt -yt *)  \nAccording to John Taylor ’s papers in 1993 and then in 1999, the target federal funds rate is estimated by setting the 􁶔 coefficients to 0.5. In 1999, he published a paper that states the coefficient in front of the output gap is merely greater than or equal to zero. However, as shown in Figure A, it is clear that the implemented federal funds rate has rarely been even approximately the same as the one estimated by the Taylor Rule—an issue noted in Woodford (2001). There is, then, a question as to whether the federal funds rates the Taylor Rule suggests can be improved. Federal funds rates are a tool to mitigating the financial markets, combating economic growth that is too slow or too fast. Thus, this paper explores if those coefficients  \nshould be adjusted in the Taylor Rule equation to better match what has really been implemented by the Federal Reserve Bank (FRB) to maintain macroeconomic equilibrium in the United States.  \nThis paper interprets these coefficients as insight into which aspect of the economy has a greater influence on picking the target interest rate. In the 1993 model both coefficients are 0.5, so this implies that the inflation gap and the output gap are equally important parameters taken into consideration for picking the target rate. However, with the knowledge that the target rate actually set by the FRB is not identical to that as predicted by the Taylor Rule, exploring the possibility of different coefficients, and thus decision influence, seems necessary. With an  \nA.D. Karakas 3  \nunderstanding of new coefficients, insight, based on the inflation gap and output gap, into where the FRB has allocated focus for setting rates since the 1960s is furthered. Additionally, the Taylor Rule assumes the coefficient in front of the inflation rate term is 1. However, the inflation rate is still a variable, and therefore i","cbCaiqir9huPD2Aa","https://ap.wps.com/l/cbCaiqir9huPD2Aa","pdf",822564,1,23,"English","en",105,"# Introduction\n## Taylor Rule overview and motivation\n## Linear OLS coefficient reevaluation\n## Nonlinear machine-learning approach\n## Model results and recession-related deviations","[{\"question\":\"What is the main goal of reevaluating the Taylor Rule in this paper?\",\"answer\":\"To adjust the Taylor Rule so that its estimated federal funds rates match the federal funds rates previously implemented by the Federal Reserve.\"},{\"question\":\"How does the paper use a linear method to reevaluate the Taylor Rule?\",\"answer\":\"It applies an OLS regression model where the dependent variable is the federal funds rate and the independent variables include the inflation rate, inflation gap, and output gap, to obtain more accurate coefficients.\"},{\"question\":\"What does the nonlinear machine-learning method do differently?\",\"answer\":\"It trains a model where inputs are the inflation rate and output gap and the output is the federal funds rate, using gradient descent to minimize the error between estimated and actual previously implemented rates.\"}]","Reevaluating the Taylor Rule with Machine Learning | PDF",1785725855,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"reevaluating-the-taylor-rule-with-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/reevaluating-the-taylor-rule-with-machine-learning/119702/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of reevaluating the Taylor Rule in this paper?","Question",{"text":75,"@type":76},"To adjust the Taylor Rule so that its estimated federal funds rates match the federal funds rates previously implemented by the Federal Reserve.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper use a linear method to reevaluate the Taylor Rule?",{"text":80,"@type":76},"It applies an OLS regression model where the dependent variable is the federal funds rate and the independent variables include the inflation rate, inflation gap, and output gap, to obtain more accurate coefficients.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the nonlinear machine-learning method do differently?",{"text":84,"@type":76},"It trains a model where inputs are the inflation rate and output gap and the output is the federal funds rate, using gradient descent to minimize the error between estimated and actual previously implemented rates.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]