How To Calculate Mean Absolute Error In Stata
error & count function Tweet Welcome to Talk Stats! Join the discussion today by registering your FREE account. Membership benefits: • Get your questions answered by community gurus and expert researchers. • Exchange your learning and research experience among mape stata peers and get advice and insight. Join Today! + Reply to Thread Results 1 to rmse stata 3 of 3 Thread: Mean absolute error & count function Thread Tools Show Printable Version Email this Page… Subscribe to this Thread… out of sample forecast stata Display Linear Mode Switch to Hybrid Mode Switch to Threaded Mode 10-27-201204:58 PM #1 FinanceStud View Profile View Forum Posts Give Away Points Posts 6 Thanks 0 Thanked 0 Times in 0 Posts Mean absolute error & count function Dear Stata helpers, I'm a 3rd year Business Administration student working on a finance project about analyst forecast accuracy. My supervisor requires me to work with Stata which I never used before and haven't gotten any instructions whatsoever on how to work with this program. He has made it clear that I have to figure it out myself or with help from outside, so that's why I came to this forum for some help. I am using the IBES database from WRDS which lists the following data: Ticker (abbreviated firm name) Activation date (date of forecast) Year (derived from Activation date) Analyst code Estimated value (EPS forecast) Actual value (EPS) Absolute error (derived from Actual - Estimated) I've got two problems at hand: 1. Mean absolute forecast error for a specific firm. I have the individual absolute forecast error of a specific analyst for a specific firm in a given year, but I also need a column right next to it that will show the mean absolute forecast error for the firm in a given year, this is to compute the measure for the dependent variable 'forecast accuracy'. >> What I tried is "mean abserror, over(ticker year)" but it returned me the 'no observations r(2000);' error? 2. a. Counting number of years for which a analyst i made a forecast through year t. b. Counting number of years for which a analyst i made a forecast through year t for firm j. c. Counting number of firms for which a analyst i made a forecast through year t. What I tried is using the 'count observations using satisfying condition' in the menu which gave me the following commands: "by analys year, sort : count if abserror>=0" for a. "by analys year ticker, sort : count if a
may be challenged and removed. (December 2009) (Learn how and when to remove this template message) The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting method in statistics, for example in trend estimation. It usually expresses accuracy as a percentage, and is defined by the formula: M = 100 n ∑ t = 1 n | A t − F t A t | , {\displaystyle {\mbox{M}}={\frac {100}{n}}\sum _{t=1}^{n}\left|{\frac {A_{t}-F_{t}}{A_{t}}}\right|,} where At is the actual value and Ft http://www.talkstats.com/showthread.php/29740-Mean-absolute-error-amp-count-function is the forecast value. The difference between At and Ft is divided by the Actual value At again. The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted pointsn. Multiplying by 100 makes it a percentage error. Although the concept of MAPE sounds very simple and convincing, it has major drawbacks https://en.wikipedia.org/wiki/Mean_absolute_percentage_error in practical application [1] It cannot be used if there are zero values (which sometimes happens for example in demand data) because there would be a division by zero. For forecasts which are too low the percentage error cannot exceed 100%, but for forecasts which are too high there is no upper limit to the percentage error. When MAPE is used to compare the accuracy of prediction methods it is biased in that it will systematically select a method whose forecasts are too low. This little-known but serious issue can be overcome by using an accuracy measure based on the ratio of the predicted to actual value (called the Accuracy Ratio), this approach leads to superior statistical properties and leads to predictions which can be interpreted in terms of the geometric mean.[1] Contents 1 Alternative MAPE definitions 2 Issues 3 See also 4 External links 5 References Alternative MAPE definitions[edit] Problems can occur when calculating the MAPE value with a series of small denominators. A singularity problem of the form 'one divided by zero' and/or the creation of very large changes in the Absolute
material JEL Classification NEP reports Subscribe to new research Search Pub compilations Reading lists MyIDEAS More options are now at bottom of page IDEAS is a service hosted by the Research Division of https://ideas.repec.org/c/boc/bocode/s433001.html the Federal Reserve Bank of St. Louis IDEAS also indexes books. Printed from https://ideas.repec.org/ Share: MyIDEAS: Log in (now much improved!) to save this software component DMARIANO: Stata module to calculate Diebold-Mariano comparison of forecast accuracy Contents:Author info Abstract Bibliographic info Download info Related research References Citations Lists Statistics Corrections Author Info Christopher F Baum() (Boston College)
Registered author(s): Christopher F Baum how to Abstractdmariano calculates a measure of predictive accuracy proposed by Diebold and Mariano (1995). Given an actual series and two competing predictions, one may apply a loss criterion (such as squared error, mean absolute error, or mean absolute percentage error) and then calculate a number of measures of predictive accuracy that allow the null hypothesis of equal accuracy to be tested. The S(1) measure, how to calculate calculated in this routine, tests that the mean difference between the loss criteria for the two predictions is zero, using a long-run estimate of the variance of the difference series. Download Info If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large. File URL: http://fmwww.bc.edu/repec/bocode/d/dmariano.adoFile Function: program codeDownload Restriction: no File URL: http://fmwww.bc.edu/repec/bocode/d/dmariano.hlpFile Function: help fileDownload Restriction: no Bibliographic Info Software component provided by Boston College Department of Economics in its series Statistical Software Components with number S433001. as HTML HTML with abstract plain text plain text with abstract BibTeX RIS (EndNote, RefMan, ProCite) ReDIF JSON in new window Size: Programming language: Stata Requires: Stata version 9.2 Date of creation: 09 Jun 2003 Date of revision: 26 Apr 2011 Handle: RePEc:boc:bocode:s433001 Note: This module may be installed from within Stata by typing "ssc install dmariano". Windows users should not attempt to download these files with a web browser. Contact detail