Package tonymisc. R topics documented: February 15, Title Functions for Econometrics Output. Version Date

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1 Title Functions for Econometrics Output Version Date Author J. Anthony Cookson Package tonymisc February 15, 2013 Maintainer J. Anthony Cookson This package provides several methods and functions that simplify output from regression packages. Depends R (>= ), memisc, AER, car, nlme Suggests gmm, plm, Ecdat License GPL-2 Repository CRAN Date/Publication :00:48 NeedsCompilation no R topics documented: tonymisc-package AK getsummary.robust iv lagger laptop mfx mfx_me mktshare robust settabdefault summaryr

2 2 AK91 sum_iv tony.vars tony.vars Index 17 tonymisc-package Functions and Extensions for Econometrics Output Details This package contains a list of commands that make summarizing regression output simpler. Much of this package is an extension of the mtable() command in the memisc library, but there are a variety of other methods specifically, those for reporting robust standard errors and IV regression output that are available in this package. Package: tonymisc Type: Package Version: Date: License: GPL-2 Maintainer: J. Anthony Cookson <tony.cookson@gmail.com> ivreg mtable AK91 Angrist and Krueger (1991) s Compulsory Schooling Data (1970 Census Cohort Only) This data set is a reformatted version of Angrist and Krueger s compulsory schooling data. data(ak91)

3 AK91 3 Format A data frame with observations on the following 11 variables. age Age in years ageq Age in quarter-years educ Years of education lwklywge logged-weekly wage married indicator variable == 1 if married census 1970 census (left over from original data set). This is a constant ==70 qob Quarter of Birth, a factor with levels race an indicator variable == 1 if black smsa an indicator variable == 1 if center city yob Year of birth, a factor with levels Technically, there are other levels, but those don t occur within this subsample of Angrist and Krueger (1991) region Region of Residence, a factor with levels enocent esocent midatl mt neweng other soatl wnocent wsocent Source References Does Compulsory School Attendance Affect Schooling and Earnings? Joshua D. Angrist and Alan B. Krueger. The Quarterly Journal of Economics. Vol. 106, No. 4 (Nov., 1991), pp ## Reproduce the First Two Columns of Table IV, Angrist and Krueger (1991) ## Not Run. Takes ~ 15 seconds # library(tonymisc) # library(memisc) # data(ak91) # ak.lm1 = lm(lwklywge~yob+educ, data=ak91) # ak.iv1 = iv(lwklywge~yob+educ,educ~yob:qob, data=ak91) # settabdefault() # mtable(ak.lm1, ak.iv1)

4 4 getsummary.robust getsummary.robust getsummary Methods for Extending mtable() These functions are getsummary methods for extending the mtable() to report robust standard errors (.robust), IV regression estimates (.ivreg and.tonyiv), summary output from GLS fits (.gls), marginal effects for probit and logit (.mfx), and panel models output (.plm and.pgmm). The robust() command allows the user not only to specify the type of robust standard error correction, but also to specify the set of variables to be included in the summary output. ## S3 method for class robust getsummary(obj, alpha = 0.05,...) obj alpha An object of type that matches the method. These functions provide support for ivreg, plm, pgmm, gls and tonyiv objects as well as objects coerced to be of class "robust" and of class "mfx." Significance Level... passed to getsummary Returns a list of objects to be referenced by mtable. Do not use these methods directly. Use mtable, which will automatically call these methods for class objects that have been implemented. J. Anthony Cookson tolatex mtable robust settabdefault ## ## ## Example 1: Extensions for Robust ## ## ## library(tonymisc) data(mktshare) mkt.lm = lm(y~x1+x2+p+z1, data=mktshare)

5 getsummary.robust 5 mkt.rob = robust(mkt.lm) ## Default is hc3 heteroskedasticity corrrection mkt.rob2 = robust(mkt.lm, type = "hc1") ## Can specify hc0, hc1, hc2, hc3 mkt.rob3 = robust(mkt.lm, type = "no") ## Can specify type = "no" for not corrected SEs mtable(mkt.rob, mkt.rob2, mkt.rob3) ## Coding Kludge: no correction is reported as type = hc9 mkt.rob4= robust(mkt.lm, keep="p") ## keeps intercept by default mkt.rob5= robust(mkt.lm, keep=c("x1","p")) ## can keep more than one mkt.rob6= robust(mkt.lm, keep=c("x1", "p"), drop="(intercept)") ## Drop intercept. mtable(mkt.rob4, mkt.rob5, mkt.rob6) mkt.rob7= robust(mkt.lm, type="no", drop=c("x1","z1")) ## type="no": extends selective output to lm mkt.rob8= robust(mkt.lm, drop="(intercept)", keep=c("x1", "x2")) ## Can drop intercept and specify keep mtable(mkt.rob7, mkt.rob8) ## ## ## Example 2: Extensions for ivreg ## ## ## library(tonymisc) data(mktshare) mkt.aer mkt.aer2 = ivreg(y~x1+x2+p x1+x2+z1+z2,data=mktshare) = ivreg(y~x1+x2+p x1+z1+z2,data=mktshare) mtable(mkt.aer,mkt.aer2) ## ## ## Example 3: Extensions for gls ## ## ## library(tonymisc) data(mktshare) mkt.gls1 mkt.gls2 mkt.gls3 = gls(y~x1, data=mktshare,correlation=corar1(form=~1)) = gls(y~x1+x2, data=mktshare,correlation=corar1(form=~1)) = gls(y~x1, data=mktshare,correlation=corarma(p=2,q=2,form=~1)) mtable(mkt.aer,mkt.aer2) ## ## ## Example 4: Extensions for mfx ## ## Note: SEs are approximate ## ## ## library(tonymisc) library(ecdat) data(yogurt)

6 6 iv yop <- glm(i(choice=="yoplait")~price.yoplait+feat.yoplait, family=binomial, data=yogurt) yo_me <- mfx_me(yop) ## Create "mfx" object to trick mtable() mtable(yop, yo_me) ## produces a table with nice output tolatex(mtable(yop, yo_me)) ## Produces LaTeX code ## ## ## Example 5: Extensions for plm ## ## and pgmm ## ## ## library(tonymisc) library(plm) ## plm Example ## data("produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state","year")) mtable(zz) ## pgmm Example ## data("empluk", package = "plm") ## Arellano and Bond (1991), table 4b z1 <- pgmm(log(emp) ~ lag(log(emp), 1:2) + lag(log(wage), 0:1) + log(capital) + lag(log(output), 0:1) lag(log(emp), 2:99), data = EmplUK, effect = "twoways", model = "twosteps") mtable(z1) iv Another Two-Stage Least Squares Command This command performs 2SLS. Relative to other IV regression commands (like ivreg() in AER or tsls() in sem), this function automatically conducts a test for instrument relevance and provides easy access to the first stage regression estimates. iv(second, first, data) second first data A formula specifying the second stage regression. This should include all regressors (endogenous and exogenous). A formula statement specifying the first stage regression(s). The LHS of this formula is the set of endogenous regressors separated by + signs. The RHS of the formula contains the excluded instruments; the function automatically adds exogenous regressors to the RHS of the first st age. A data frame object.

7 iv 7 Details After creating an IV regression object with iv(), you have two options to produce output: (1) Apply mtable() to the iv object, (2) Apply sum_iv() to the iv object. The former method makes it slightly easier to port the results into TeX tables while the latter method behaves more like a summary command (and provides more detail). second first ftest cc sargan A summary.lm object that contains the second stage estimation results with the appropriately-adjusted standard errors A summary.lm object that contains the first stage of the IV regression An anova object containing the result of an F-test for instrument relevance in the IV regression First-stage test of relevance that can handle testing for relevance with multiple endogenous regressors. The null hypothesis of this test is that the minimum canonical correlation equals zero (see Anderson 2003). Sargan test for exogeneity of instruments. Returns NA if there are not more instruments than endogenous regressors. J. Anthony Cookson References T. W. Anderson (2003). An Introduction to Multivariate Statistical Analysis, Third Edition, Wiley. tsls, ivreg, mtable library(tonymisc) data(mktshare) ## Create the object ## myiv = iv(y~x1+x2+p, p~z1+z2, data=mktshare) sum_iv(myiv) mtable(myiv) ## Summarize Organically ## mtable() summary ## An example with multiple instruments ## myiv2 = iv(y~x1+x2+p, x2+p~z1+z2, data=mktshare) sum_iv(myiv2, cc=true) mtable(myiv, myiv2)

8 8 lagger lagger Lagger Function for Lagging Time Series data This command lags (or leads) variables within a data frame that contains variable named "date" that has class "Date". lagger(data, by.var, lag.vars, num.lags, direction, freq = "daily") data by.var lag.vars num.lags direction freq A data.frame object with a panel structure. character string with a unique identifier for each time period. character vector of variable names (must be defined within data) of variables to lag or lead. numeric vector that indicates the number of lags to generate for each variable within lag.vars. Specify "lag" or "lead" to indicate whether to generate lags or leads. Specify "daily", "monthly" or "quartly" to indicate the frequency of the data. The function returns a data frame with new columns for the lags and leads generated. Sean Golden and Ian Muir (with adaptations by J. Anthony Cookson) as.date,date

9 laptop 9 laptop Laptop Data / Observational Format Details Source This is an example data set from an online price comparison website on laptop prices. I selected the top 10 brands according to the website and I extracted the first 20 prices in the list sorted by "relevance." data(laptop) A data frame with 200 observations on the following 3 variables. Brand a factor with levels Acer Apple ASUS Dell HP Lenovo MSI Panasonic Sony Toshiba Product a numeric vector indicating the position in the search by "relevance." Price a numeric vector indicating the price in US dollars for the laptop computer. This data set is a convenience sample of laptop prices. It should not be used for any purpose other than demonstrating methods and trying out functions in R. data(laptop) ## maybe str(laptop) ; plot(laptop)... mfx A Marginal Effects Post Estimation Command for glm This function returns Stata-like probit and logit marginal effects when applied to a glm object that was created using a probit or logit binomial link function. This function and its S3 print method are for printing summary()-like output to the screen as one might do in interactive mode. If you wish to port marginal effects output to a LaTeX table, use mfx_me to coerce the glm object to "mfx" and then apply mtable() directly.

10 10 mfx_me mfx(x) x A.glm fitted object created using a binomial family with a probit or logit link function. Returns a standard glm list (see glm for details), but with marginal effects in place of ecoefficients and an object of class "mfx." J. Anthony Cookson (adaptation of code from a blog post by TimeSeriesIreland) mfx_me glm getsummary.mfx library(ecdat) data(yogurt) yop <- glm(i(choice=="yoplait")~price.yoplait+feat.yoplait, family=binomial, data=yogurt) yo_mfx <-mfx(yop) ## Creates "mfx" object yo_mfx ## Calls print.mfx to print to screen ## Alternative Method to use with mtable() ## # yo_me <- mfx_me(yop) ## Create "mfx" object to trick mtable() # mtable(yop, yo_me) ## produces a table with nice output mfx_me Marginal Effects This function coerces glm objects to type mfx. mfx_me(x)

11 mktshare 11 x A glm object using binomial link function probit or logit. The getsummary.mfx method doesn t know what to do with other types of glm objects. Returns an object of type "mfx." J. Anthony Cookson getsummary.mfx mtable library(tonymisc) library(ecdat) data(yogurt) yop <- glm(i(choice=="yoplait")~price.yoplait+feat.yoplait, family=binomial, data=yogurt) yo_me <- mfx_me(yop) ## Create "mfx" object to trick mtable() mtable(yop, yo_me) ## produces a table with nice output tolatex(mtable(yop, yo_me)) ## Produces LaTeX code mktshare Synthetic Market Share Data Format This data set is a synthetic data set generated from a random utility model with linear preferences for attributes of a product. data(mktshare) A data frame with 100 observations on the following 10 variables. product a numeric vector whose values indicate the product number within market market a numeric vector whose values indicate the market number (should be a factor) y a numeric vector equal to the log(share/outshare) to be used as the response variable (as suggested by Berry 1994).

12 12 robust share a numeric vector equal to the market share that product i has in market m. outshare a numeric vector equal to the fraction of the individuals who buy the outside good. x1 a numeric vector whose values represent attribute one. x2 a numeric vector whose values represent attribute two. p a numeric vector whose values represent the product s price. z1 a numeric vector that is a potential instrument z2 a numeric vector that is another potential instrument Details This is a simulated data set that I use to show how to estimate IV regression. data(mktshare) robust Robust Objects / Selective Variable Reporting for mtable() This function coerces lm objects to type robust and allows the user to specify the type of heteroskedasticity correction. The function is intended to be used with summaryr to interface with mtable() to produce nicely-formatted output. robust(model, type = "hc3", keep=null, drop=null, vcov=null, se = NULL) model type keep drop vcov se A linear models object. The type of heteroskedasticity correction you want to specify. Options include "no", "hc0", "hc1", "hc2", "hc3" and "hc4" with "hc3" as the default. For more information on these corrections see the help file for hccm. A user would specify "no" to selectively display output from a lm() fit using mtable(). A vector of variable names you would like to display using mtable(). Defaults to NULL. A vector of variable names you would like to not display using mtable(). Defaults to NULL. If both keep and drop are NULL, the default is to print output for all variables in the model. If keep and drop are specified, keep takes precedence, except in the case of dropping an intercept. See examples below. User-supplied variance-covariance matrix. User-supplied vector of standard errors.

13 settabdefault 13 Returns an object of type "robust." J. Anthony Cookson hccm summaryr getsummary.robust mtable library(tonymisc) data(mktshare) mkt.lm = lm(y~x1+x2+p+z1, data=mktshare) mkt.rob = robust(mkt.lm) ## Default is hc3 heteroskedasticity corrrection mkt.rob2 = robust(mkt.lm, type = "hc1") ## Can specify hc0, hc1, hc2, hc3 mkt.rob3 = robust(mkt.lm, type = "no") ## Can specify type = "no" for not corrected SEs mtable(mkt.rob, mkt.rob2, mkt.rob3) ## Coding Kludge: no correction is reported as type = hc9 mkt.rob4= robust(mkt.lm, keep="p") ## keeps intercept by default mkt.rob5= robust(mkt.lm, keep=c("x1","p")) ## can keep more than one mkt.rob6= robust(mkt.lm, keep=c("x1", "p"), drop="(intercept)") ## Drop intercept. mtable(mkt.rob4, mkt.rob5, mkt.rob6) mkt.rob7= robust(mkt.lm, type="no", drop=c("x1","z1")) ## type="no": extends selective output to lm mkt.rob8= robust(mkt.lm, drop="(intercept)", keep=c("x1", "x2")) ## Can drop intercept and specify keep mtable(mkt.rob7, mkt.rob8) settabdefault Setting Summary Defaults for Extending mtable() This function resets the summary template for mtable() as opposed to using the setsummarytemplate() command directly. It is called directly from within each getsummary method in tonymisc. settabdefault()

14 14 summaryr J. Anthony Cookson mtable gls ivreg summaryr Summary Function for Robust Standard Errors This function allows the user to summarize regression output using heteroskedasticity-consistent standard errors. The function is just an adaptation of summaryhccm.lm written by John Fox, but some details have been changed to allow the function to interface with mtable through getsummary.robust. summaryr(model, type = c("hc3", "hc2", "hc1", "hc0", "hc4"),...) model type... A linear models object. The type of heteroskedasticity correction you want to specify. Options include hc0, hc1, hc2, hc3 and hc4. The default is hc3. For more information on these corrections see the help file for hccm. The function returns a linear models object with heteroskedasticity-consistent standard errors. J. Anthony Cookson (adapted the code from John Fox) hccm linearhypothesis data(mktshare) ## Synthetic Market Share Data mkt.lm = lm(y~x1+x2, data=mktshare) summary(mkt.lm) ## For comparison summaryr(mkt.lm) ## For demonstration (SEs are slightly different)

15 sum_iv 15 sum_iv Summary Printing Command for iv() objects This command is the equivalent of a summary command for IV regression objects created using iv() from the tonymisc library. sum_iv(reg_iv, first = FALSE, ftest = FALSE, second = TRUE, cc = FALSE, sargan = FALSE) reg_iv first ftest second cc sargan A iv()-created IV regression object. If TRUE, display the first stage regression output. Default FALSE. If TRUE, display the F-test for relevance. Default FALSE. If TRUE, display the second-stage regression output. Default TRUE. If TRUE, display the Anderson (2003) Canonical Correlation test for first-stage relevance. Default FALSE. If TRUE, display the Sargan test for instrument exogeneity. Default FALSE. The method just prints output to the screen. J. Anthony Cookson iv,mtable data(mktshare) myiv = iv(y~x1+x2+p, p~z1+z2, data=mktshare) sum_iv(myiv) sum_iv(myiv, ftest=true) sum_iv(myiv, ftest=true, first=true) sum_iv(myiv, ftest=true, first=true, cc=true) myiv = iv(y~x1+x2+p, x2+p~z1+z2, data=mktshare) sum_iv(myiv) sum_iv(myiv, ftest=true) sum_iv(myiv, ftest=true, first=true) sum_iv(myiv, ftest=true, first=true, cc=true)

16 16 tony.vars2 tony.vars An alternative all.vars() command This is a utility command used by iv() that provides a slightly easier-to-use version of all.vars() when the formula statement contains transformations using I(). tony.vars(x) x The only argument is a formula statement. Returns a list of variable names. J. Anthony Cookson tony.vars2 Another alternative all.vars() command This is a utility command used by iv() that provides a slightly easier-to-use version of all.vars() when the formula statement contains transformations using I(). It also makes it possible to specify/extract multiple LHS variables in the first stage. tony.vars2(x) x The only argument is a formula statement. Returns a list of variable names. J. Anthony Cookson

17 Index Topic datasets AK91, 2 laptop, 9 mktshare, 11 Topic package tonymisc-package, 2 AK91, 2 as.date, 8 Date, 8 settabdefault, 4, 13 sum_iv, 15 summaryr, 13, 14 tolatex, 4 tony.vars, 16 tony.vars2, 16 tonymisc (tonymisc-package), 2 tonymisc-package, 2 tsls, 7 getsummary.coeftest (getsummary.robust), 4 getsummary.gls (getsummary.robust), 4 getsummary.ivreg (getsummary.robust), 4 getsummary.mfx, 10, 11 getsummary.mfx (getsummary.robust), 4 getsummary.pgmm (getsummary.robust), 4 getsummary.plm (getsummary.robust), 4 getsummary.robust, 4, 13 getsummary.tonyiv (getsummary.robust), 4 glm, 10 gls, 14 hccm, 13, 14 iv, 6, 15 ivreg, 2, 7, 14 lagger, 8 laptop, 9 linearhypothesis, 14 mfx, 9 mfx_me, 9, 10, 10 mktshare, 11 mtable, 2, 4, 7, 11, print.mfx (mfx), 9 robust, 4, 12 17