[Eventstudies-commits] r405 - pkg/inst/tests
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Feb 26 09:07:05 CET 2015
Author: sargam_jain
Date: 2015-02-26 09:07:05 +0100 (Thu, 26 Feb 2015)
New Revision: 405
Added:
pkg/inst/tests/test_compilation.txt
Removed:
pkg/inst/tests/test_INR.rda
pkg/inst/tests/test_NiftyIndex.rda
pkg/inst/tests/test_SplitDates.rda
pkg/inst/tests/test_StockPriceReturns.rda
pkg/inst/tests/test_USDINR.rda
pkg/inst/tests/test_eventstudy.R
pkg/inst/tests/test_firmExposuresData.rda
pkg/inst/tests/test_inr_inference.R
pkg/inst/tests/test_interfaces.R
pkg/inst/tests/test_lmAMM.R
pkg/inst/tests/test_marketresiduals.R
pkg/inst/tests/test_y3c3.rda
Log:
Removed old test cases. Added the draft for new test cases to test for.
Deleted: pkg/inst/tests/test_INR.rda
===================================================================
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Deleted: pkg/inst/tests/test_NiftyIndex.rda
===================================================================
(Binary files differ)
Deleted: pkg/inst/tests/test_SplitDates.rda
===================================================================
(Binary files differ)
Deleted: pkg/inst/tests/test_StockPriceReturns.rda
===================================================================
(Binary files differ)
Deleted: pkg/inst/tests/test_USDINR.rda
===================================================================
(Binary files differ)
Added: pkg/inst/tests/test_compilation.txt
===================================================================
--- pkg/inst/tests/test_compilation.txt (rev 0)
+++ pkg/inst/tests/test_compilation.txt 2015-02-26 08:07:05 UTC (rev 405)
@@ -0,0 +1,209 @@
+###### Testing functions in eventstudies package #####
+
+Tests are categorized as follows:
+I. Tests for argument inputs (context: user input)
+II. Tests for functionality (context: functionality)
+III. Tests for aggregate function (context: aggregate)
+IV. Example from book as test case (context: example)
+
+I. TESTS FOR ARGUMENT INPUTS
+
+1. Function phys2eventime
+ a) Check for following values for width:
+ # Normal values:
+ * 10
+ * 1
+ # Extreme values:
+ * zero
+ * +Inf
+ * -Inf
+ * Values greater than the no. of rows in dataframe StockPriceReturns
+ b) Check for following when zoo object with firm data and dates as index is a univariate series:
+ # If univariate series is input as vector (not matrix), then the function fails.
+ c) Check for following values for z (zoo object):
+ # Normal values:
+ * ?
+ * ?
+ # Extreme values:
+ * ?
+ * ?
+ d) Check for following values for event.list:
+ # Normal values:
+ * ?
+ * ?
+ # Extreme values:
+ * ?
+ * ?
+
+****************** Models ********************
+
+2. Function marketModel
+ a) Check whether: # of rows in df(firm returns) = # of rows in df(market returns)
+ b) Check for extreme values of no. of rows and columns i.e. 1 row or 1 col in dataset
+ c) Check for residuals = TRUE (whether data is in returns or price format)
+ d) Check for class of arguments
+
+3. Function excessReturn
+ a) Check whether: # of rows in df(firm returns) = # of rows in df(market returns)
+ b) Check for extreme values of no. of rows and columns i.e. 1 row or 1 col in dataset
+ c) Check for class of arguments
+
+4. Function lmAMM
+ a) Check for whether X is the output of makeX or not (read makeX manual)
+ b) Check for other logical arguments (those with TRUE/FALSE)
+
+5. Function None
+ a) Check that no model arguments should be specified
+
+
+************** Remap functions **************
+
+6. Function remap.cumsum
+ a) Check for is.pc = TRUE (whether data is in percentage)
+ b) Check for following values of base:
+ # Normal values:
+ * 0
+ * 100
+ # Extreme values:
+ * -Inf
+ * Inf
+7. Function remap.cumprod
+ a) Check for is.pc = TRUE (whether data is in percentage)
+ b) Check for is.pc = TRUE (whether data is in returns or price format)
+ c) Check for following values of base:
+ # Normal values:
+ * 0
+ * 100
+ # Extreme values:
+ * -Inf
+ * Inf
+
+************* Inference procedures ***********
+
+8. Function inference.bootstrap
+ a) Check for following values of boot.run:
+ # Normal values:
+ * 1000
+ * 10
+ # Extreme values:
+ * 0
+ * Inf/ -Inf
+ b) Check for following values of es.w:
+ # Extreme values:
+ * ?
+ * ?
+10. Function inference.wilcox
+ a) Check for following values of prob:
+ # Normal values:
+ * .10
+ # Extreme values:
+ * Any value less than 0 and greater than 1
+ b) Check for following values of es.w:
+ # Extreme values:
+ * ?
+ * ?
+
+
+************************************************************************************************************
+
+II. TESTS FOR FUNCTIONALITY
+
+1. Function phys2eventime
+ a) Check for list components returned by the function
+ b) Check for firm selected should not have NA in data for defined width
+ c) Check for class of arguments
+ d) Check for if the function handles following data discrepancies in events:
+ * Missing dates in eventlist
+ * Missing firm in eventlist
+ e) When zoo object with firm data and dates as index is a univariate series,
+ function must work if its a matrix of one column.
+
+
+****************** Models ********************
+
+2. Function marketModel
+ a) Check the working and results of regression analysis manually
+
+3. Function excessReturn
+ a) Check with small sample data whether the result is correct or not.
+
+4. Function lmaMM
+ a) Check for class of arguments
+ b) Check the working and results of regression analysis manually: difficult @leave this for now
+ @ remind to come up with test cases for lmAMM
+
+5. Function None
+
+************** Remap functions **************
+6. Function remap.cumsum
+ a) Cumulate by summing and tally
+
+7. Function remap.cumprod
+ a) Cumulate by multiplying and tally
+
+************* Inference procedures ***********
+
+8. Function inference.bootstrap @think and read
+
+9. Function inference.wilcox @think and read
+
+********************************************************************************
+
+III. TESTS FOR AGGREGATE FUNCTION (eventstudy)
+
+10. Check the following for argument, event.window:
+ # Normal values:
+ * Any value less than no. of rows of firm.returns and greater than zero
+ # Extreme values:
+ * 0
+ * Inf/ -Inf
+ * Any value greater than no. of rows of firm.returns and less than zero
+ b) Check that event.window does not overlap with estimation window (i.e. firm returns
+ used to estimate market returns through any of the described models.
+
+***************** Models *********************
+
+10. Check the function for all the models using small data sets:
+ a) Check for the argument, type: by default, model type (argument in eventstudy
+ function) should be market model, else specify the type as any of the following:
+ # none
+ # lmAMM
+ # excessReturn
+
+***************** Remap functions ************
+
+11. Check the function for logical argument to.remap (taking values TRUE/ FALSE):
+ a) for cumsum
+ b) for cumprod
+
+************* Inference procedures ***********
+
+12. Check the function for logical argument inference (taking values TRUE/ FALSE):
+13. Check the function for the argument inference.strategy taking following values:
+ a) bootstrap
+ b) wilcoxin
+
+
+
+
+
+Function prepare.returns(): for (model != "None") and (number of returns > 1)
+ - Merge all the returns (firm.returns, currency, and/or market)
+ into one 'zoo' object.
+ - Convert the merged object for each event separately to event
+ time.
+ - Store the outcomes as attributes of the event time object.
+ - Return a 'list' of event time objects for each event.
+ 2. prepare.returns(): for (model = "None") and (number of returns = 1)
+ - Convert the whole zoo object 'firm.returns' to event time.
+ - Returns this object as a whole.
+ 3. Check whether all the outcomes are not "success".
+ 4. If model is not "None", run the model program for each object in
+ the returned list, and return the series index only within the
+ event window.
+ 5. If model is None, then directly return the series within the
+ event window.
+ 6. Check for any NULL values.
+ 7. Handle univariate output: convert to a single column zoo object.
+ 8. Perform remapping if asked by the user.
+ 9. Perform inference if asked by the user.
Deleted: pkg/inst/tests/test_eventstudy.R
===================================================================
--- pkg/inst/tests/test_eventstudy.R 2015-01-26 14:49:09 UTC (rev 404)
+++ pkg/inst/tests/test_eventstudy.R 2015-02-26 08:07:05 UTC (rev 405)
@@ -1,78 +0,0 @@
-context("phys2eventtime")
-
-test_that("test.phys2eventtime", {
-
-# An example dataset, with 3 firms --
-p <- structure(c(33.16, 34.0967, 35.3683, 34.46, 34.17, 35.89, 36.19,
- 37.1317, 36.7033, 37.7933, 37.8533, 285.325, 292.6,
- 290.025, 286.2, 290.075, 295.05, 289.325, 285.625,
- 293.7, 298.5, 289.05, 704.5438, 708.35, 735.8375,
- 710.625, 711.65, 731.0125, 727.575, 715.0187, 724.2,
- 713.1875, 695.1812), .Dim = c(11L, 3L), .Dimnames =
- list( NULL, c("ITC", "Reliance", "Infosys")), index =
- structure(c(12418, 12419, 12422, 12423, 12424, 12425,
- 12426, 12429, 12430, 12431, 12432), class = "Date"),
- class = "zoo")
-# An example events list
-eventslist <- data.frame(name=c("ITC","Reliance","Infosys",
- "ITC","Reliance","Junk"),
- when=as.Date(c(
- "2004-01-02", "2004-01-08", "2004-01-14",
- "2005-01-15", "2004-01-01", "2005-01-01")))
-eventslist$name <- as.character(eventslist$name)
-
-# What we expect if we don't worry about width --
-rawres <- structure(list(z.e = structure(c(NA, NA, NA, NA, NA, NA,
- NA, NA, 33.16, 34.0967, 35.3683, 34.46, 34.17, 35.89, 36.19,
- 37.1317, 36.7033, 37.7933, 37.8533, NA, NA, NA, NA, 285.325, 292.6,
- 290.025, 286.2, 290.075, 295.05, 289.325, 285.625, 293.7, 298.5,
- 289.05, NA, NA, NA, NA, 704.5438, 708.35, 735.8375, 710.625, 711.65,
- 731.0125, 727.575, 715.0187, 724.2, 713.1875, 695.1812, NA, NA, NA,
- NA, NA, NA, NA, NA), .Dim = c(19L, 3L), .Dimnames = list( NULL,
- c("1", "2", "3")), index = -9:9, class = "zoo"), outcomes =
- structure(c(1L, 1L, 1L, 3L, 3L, 2L), .Label = c("success",
- "unitmissing", "wrongspan" ), class = "factor")), .Names = c("z.e",
- "outcomes"))
-
-cat("\nCheck without width handling ")
-a <- phys2eventtime(p, eventslist,width=0)
-expect_that(a, equals(rawres))
-
-cat("\nCheck with width of 1 ")
-a <- phys2eventtime(p, eventslist,width=1)
-expect_that(a, equals(rawres))
-
-# But when we go to width=2, column 1 and 3 drop off because they have
-# only 1 obs before & after the event date respectively.
-cat("\nCheck with width of 2 ")
-a <- phys2eventtime(p, eventslist,width=2)
-expect_that(a, equals(structure(list(z.e = structure(c(NA, NA, NA, NA, 285.325,
- 292.6, 290.025, 286.2, 290.075, 295.05,
- 289.325, 285.625, 293.7, 298.5, 289.05,
- NA, NA, NA, NA),
- index = -9:9,
- class = "zoo"),
- outcomes = structure(c(3L, 1L, 3L, 4L, 4L, 2L),
- .Label = c("success", "unitmissing",
- "wdatamissing", "wrongspan"),
- class = "factor")),
- .Names = c("z.e", "outcomes" ))))
-
-## Check the previous date
-cat("\nTesting handling of missing data on event date: ")
-eventdate <- as.Date("2004-01-10")
-eventdate_output <- "2004-01-09"
-eventslist <- data.frame(name = "ITC", when = eventdate,
- stringsAsFactors = FALSE)
-a <- phys2eventtime(p, eventslist, width = 2)
-expect_that(as.numeric(a$z.e["0",]),
- equals(as.numeric(p$ITC[as.Date(eventdate_output), ])))
-
-
-## events$when should be a time-based class
-cat("\nTesting class of events$when: ")
-eventdate <- "2004-01-09"
-eventslist <- data.frame(name = "ITC", when = eventdate,
- stringsAsFactors = FALSE)
-expect_error(a <- phys2eventtime(p, eventslist, width = 2), regexp = "events\\$when.*class.*$")
-})
Deleted: pkg/inst/tests/test_firmExposuresData.rda
===================================================================
(Binary files differ)
Deleted: pkg/inst/tests/test_inr_inference.R
===================================================================
--- pkg/inst/tests/test_inr_inference.R 2015-01-26 14:49:09 UTC (rev 404)
+++ pkg/inst/tests/test_inr_inference.R 2015-02-26 08:07:05 UTC (rev 405)
@@ -1,45 +0,0 @@
-context("INR Inference")
-
-test_that("test.inr.inference", {
-library(eventstudies)
-
-load("test_INR.rda")
-
-inr_returns <- diff(log(INR))[-1]
-
-eventslist <- data.frame(when=as.Date(c(
- "2010-04-20","2010-07-02","2010-07-27",
- "2010-09-16","2010-11-02","2011-01-25",
- "2011-03-17","2011-05-03","2011-06-16",
- "2011-07-26")
- ),
- name=rep("inr",10)
- )
-
-event_time_data <- phys2eventtime(inr_returns[, , drop = FALSE] , eventslist,width=10)
-w <- window(event_time_data$z.e,start=-10,end=10)
-
-expect_that(inference.bootstrap(w, to.plot=FALSE)[,2],
- equals(c(-0.000015327455,
- -0.002526819039,
- 0.001190000495,
- 0.001193534934,
- 0.001846733711,
- -0.000105473215,
- -0.001659771669,
- 0.001644517771,
- -0.001325235918,
- 0.001546368579,
- -0.000809734240,
- -0.001499191073,
- -0.000289413740,
- -0.000003273428,
- -0.000416661873,
- -0.001150000190,
- -0.000759748390,
- 0.002306711019,
- -0.0004872993296,
- 0.001122457470,
- 0.000635889955)))
-})
-
Deleted: pkg/inst/tests/test_interfaces.R
===================================================================
--- pkg/inst/tests/test_interfaces.R 2015-01-26 14:49:09 UTC (rev 404)
+++ pkg/inst/tests/test_interfaces.R 2015-02-26 08:07:05 UTC (rev 405)
@@ -1,189 +0,0 @@
-context("eventstudy")
-
-test_that("test.interfaces", {
- load("test_SplitDates.rda")
- load("test_StockPriceReturns.rda")
- load("test_NiftyIndex.rda")
- load("test_USDINR.rda")
-
-### Basic event study with default args (market residuals)
- cat("\nChecking market residuals interface: ")
- expected_mean <- c(0, 0.0393985717416213, -0.7458035091065,
- 0.457817077869512, 0.715714066835461, 2.33986420702835,
- 2.37333344340029)
- expected_outcomes <- c("success", "success")
-
- test_events <- data.frame(name = "ONGC",
- when = as.Date(c("2011-08-01", "2010-05-14")),
- stringsAsFactors = FALSE)
- test_returns<- StockPriceReturns[complete.cases(StockPriceReturns$ONGC), "ONGC",
- drop = FALSE]
- test_es <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- model.args = list(market.returns = NiftyIndex))
-
- expect_that(expected_mean, equals(test_es$result[, "Mean"]))
- expect_that(expected_outcomes, equals(test_es$outcomes))
- expect_is(test_es, "es")
-
-### None
- cat("\nChecking no model output: ")
- expected_mean <- c(0, -0.197406699931557, -0.804299958306487,
- 0.0135570496689663, -0.418062964428412,
- 0.904144365357373, -0.806779427723603)
- expected_outcomes <- c("success", "success")
-
- test_events <- data.frame(name = "ONGC",
- when = as.Date(c("2011-08-01", "2010-05-14")),
- stringsAsFactors = FALSE)
- test_returns<- StockPriceReturns[complete.cases(StockPriceReturns$ONGC), "ONGC",
- drop = FALSE]
- test_es <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "None")
-
- expect_that(expected_mean, equals(test_es$result[, "Mean"]))
- expect_that(expected_outcomes, equals(test_es$outcomes))
- expect_is(test_es, "es")
-
-### AMM interface
- cat("\nChecking AMM interface: ")
- expected_mean <- c(0, 0.135927645042554, -0.600457594252805, 0.631525565290171,
- 0.871423869901684, 2.54741102266723, 2.5989730099384)
-
- expected_outcomes <- c("success", "success")
-
- test_events <- data.frame(name = "ONGC",
- when = as.Date(c("2011-08-01", "2010-05-14")),
- stringsAsFactors = FALSE)
- test_returns<- StockPriceReturns[complete.cases(StockPriceReturns$ONGC), "ONGC",
- drop = FALSE]
- test_others <- USDINR
- test_es <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "lmAMM",
- model.args = list(market.returns = NiftyIndex[index(USDINR)],
- others = test_others))
-
- expect_that(expected_mean, equals(test_es$result[, "Mean"]))
- expect_that(expected_outcomes, equals(test_es$outcomes))
- expect_is(test_es, "es")
-
-### Excess return
- cat("\nChecking excess return interface: ")
- expected_mean <- c(0, 0.138567158395153, -0.631185954448288, 0.701644918222266,
- 1.15001275036422, 2.88646832315114, 3.32315429568726)
- expected_outcomes <- c("success", "success")
-
- test_events <- data.frame(name = "ONGC",
- when = as.Date(c("2011-08-01", "2010-05-14")),
- stringsAsFactors = FALSE)
- test_returns<- StockPriceReturns[complete.cases(StockPriceReturns$ONGC), "ONGC",
- drop = FALSE]
-
- test_es <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "excessReturn",
- model.args = list(market.returns = NiftyIndex))
-
- expect_that(expected_mean, equals(test_es$result[, "Mean"]))
- expect_that(expected_outcomes, equals(test_es$outcomes))
- expect_is(test_es, "es")
-
-### Remapping
- cat("\nChecking remapping: ")
- test_events <- data.frame(name = "ONGC",
- when = as.Date(c("2011-08-01", "2010-05-14")),
- stringsAsFactors = FALSE)
- test_returns <- StockPriceReturns[complete.cases(StockPriceReturns$ONGC), "ONGC",
- drop = FALSE]
-
- ## cumsum
- test_es <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "None",
- to.remap = FALSE,
- remap = "cumsum")
-
- test_es_remap <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "None",
- to.remap = TRUE,
- remap = "cumsum")
-
- expect_false(isTRUE(all.equal(test_es, test_es_remap)))
-
- ## cumprod
- test_es <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "None",
- to.remap = FALSE,
- remap = "cumprod")
-
- test_es_remap <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "None",
- to.remap = TRUE,
- remap = "cumprod")
-
- expect_false(isTRUE(all.equal(test_es, test_es_remap)))
-
-### Inference
- cat("\nChecking inference interface: ")
- ## bootstrap
- test_es_inference <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "None",
- inference = TRUE,
- inference.strategy = "bootstrap")
-
- test_es <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "None",
- inference = FALSE,
- inference.strategy = "bootstrap")
-
- expect_false(isTRUE(all.equal(test_es, test_es_inference)))
-
- ## wilcoxon
- test_es_inference <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "None",
- inference = TRUE,
- inference.strategy = "wilcoxon")
-
- test_es <- eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "None",
- inference = FALSE,
- inference.strategy = "wilcoxon")
-
- expect_false(isTRUE(all.equal(test_es, test_es_inference)))
-
-}) # end test_that()
-
-test_that("test.arguments", {
- load("test_StockPriceReturns.rda")
-
- cat("\nChecking single series handling: ")
- test_events <- data.frame(name = "ONGC",
- when = c("2011-08-01", "2010-05-14"),
- stringsAsFactors = FALSE)
- test_returns<- StockPriceReturns$ONGC
- expect_error(eventstudy(firm.returns = test_returns,
- event.list = test_events,
- event.window = 3,
- type = "None"))
-})
Deleted: pkg/inst/tests/test_lmAMM.R
===================================================================
--- pkg/inst/tests/test_lmAMM.R 2015-01-26 14:49:09 UTC (rev 404)
+++ pkg/inst/tests/test_lmAMM.R 2015-02-26 08:07:05 UTC (rev 405)
@@ -1,126 +0,0 @@
-context("lmAMM")
-
-test_that("test.lmAMM", {
- load("test_firmExposuresData.rda")
-
- firm.returns <- firmExposuresData$Company_A
- market.returns <- firmExposuresData$NIFTY_INDEX
- inrusd <- firmExposuresData$usdinr
- rM3 <- firmExposuresData$baa
-
- cat("\nDoing Testcase P2")
- X <- makeX(market.returns, others=inrusd,
- switch.to.innov=FALSE, market.returns.purge=FALSE, verbose=FALSE)
- a <- lmAMM(firm.returns, X, nlags=0, verbose=FALSE)
- expect_that(c(a$exposures, a$s.exposures),
- equals(structure(c(0.716160223601197,-0.673093436292401,
- 0.152101606133946,1.02143820457251),
- .Names = c("market.returns", "z", "market.returns", "z")), tolerance=1e-1))
-
- cat("\nDoing Testcase P3")
- X <- makeX(market.returns, others=inrusd,
- switch.to.innov=TRUE, market.returns.purge=FALSE, verbose=FALSE)
- a <- lmAMM(firm.returns, X, nlags=0, verbose=FALSE)
- expect_that(c(a$exposures, a$s.exposures),
- equals(structure(c(0.716160223601197,-0.673093436292401,
- 0.152100337597009,1.02146106755333),
- .Names = c("market.returns", "z", "market.returns", "z")), tolerance=1e-1))
-
- cat("\nDoing Testcase P4")
- a <- lmAMM(firm.returns, X, nlags=1, verbose=FALSE)
- expect_that(c(a$exposures, a$s.exposures),
- equals(structure(c( 0.736264286484902, -1.450805,
- 0.177929844631439, 1.646730),
- .Names = c("market.returns","z", "market.returns", "z")),tolerance=1e-1))
-
-
- cat("\nDoing Testcase P5")
- X <- makeX(market.returns, others=inrusd,
- switch.to.innov=TRUE, market.returns.purge=TRUE, nlags=1, verbose=FALSE)
- a <- lmAMM(firm.returns, X, nlags=1, verbose=FALSE)
- expect_that(c(a$exposures, a$s.exposures),
- equals(structure(c(0.7365566,-2.340171,
- 0.1653025, 1.1436666),
- .Names = c("market.returns", "z", "market.returns", "z")),tolerance=1e-1))
-
- cat("\nDoing Testcase P6")
- X <- makeX(market.returns, others=cbind(inrusd, rM3),
- switch.to.innov=c(FALSE, FALSE), market.returns.purge=FALSE, verbose=FALSE)
- a <- lmAMM(firm.returns, X, nlags=0, verbose=FALSE)
- expect_that(c(a$exposures, a$s.exposures),
- equals(structure(c(0.7230599,-0.7642377,
- 0.207374104922771,0.173380799334299,
- 1.01806122963342,0.467821650129292),
- .Names = c("market.returns", "inrusd", "rM3", "market.returns", "inrusd", "rM3")),tolerance=1e-1))
-
- cat("\nDoing Testcase P7")
- X <- makeX(market.returns, others=cbind(inrusd, rM3),
- switch.to.innov=c(TRUE, TRUE), market.returns.purge=TRUE, nlags=1, verbose=FALSE)
- a <- lmAMM(firm.returns, X, nlags=1, verbose=FALSE)
-
- expect_that(c(a$exposures, a$s.exposures),
- equals(structure(c(0.7482719,-1.9468851,-0.4802211,
- 0.1740678,1.2455112,0.6146619),
- .Names = c("market.returns", "inrusd", "rM3", "market.returns", "inrusd", "rM3")),tolerance=1e-1))
-
-################################################################################
- # #
- # THE NEXT CASES TESTS THE FUNCTION FOR THREE COMPANIES FOR THREE YEARS #
- # #
-################################################################################
-
-
- cat("\nDoing Testcases P8\n")
- load("test_y3c3.rda")
-
- NIFTY_INDEX <- y3c3$NIFTY_INDEX
- INRUSD <- y3c3$INRUSD
- Company_A <- y3c3$Company_A
- Company_B <- y3c3$Company_B
- Company_C <- y3c3$Company_C
-
- regressors <- makeX(NIFTY_INDEX, others=INRUSD,
- switch.to.innov=TRUE, market.returns.purge=TRUE, nlags=1,
- dates=as.Date(c("2005-01-15","2006-01-07","2007-01-06",
- "2008-01-05","2009-01-03")), verbose=FALSE)
-
- regressand <- cbind(Company_A,Company_B,Company_C)
-
- res <- manyfirmssubperiod.lmAMM(regressand,regressors,lags=1,
- dates=as.Date(c("2005-01-15","2006-01-07","2007-01-06",
- "2008-01-05","2009-01-03")),periodnames=c("P1","P2","P3","P4"),
- verbose=FALSE)
-
- expect_that(as.data.frame(res),
-
- equals(structure(list(market.returns.P1 = c(0.756294904326272, 0.359467326140834,0.914021428042946),
- z.P1 = c(-2.23264294525560, -1.05654919420689,0.296635483126946),
- market.returns.P2 = c(1.02094561445355, 0.988758963378838,0.879236409569888),
- z.P2 = c(-4.72831391695047, -2.0508684999854,-1.02215809586573),
- market.returns.P3 = c(1.20585808099744, 0.676388278572118,0.530718379431386),
- z.P3 = c(-1.32677083522489, -2.74055730512260, -1.50032216697694),
- market.returns.P4 = c(1.11331096371784, 0.437117737120777,0.663182186702262),
- z.P4 = c(-2.05336868436562, -1.60350865767951,-0.466253391408585),
- market.returns.P1 = c(0.143617135793294, 0.263130891045529,0.154272220123111),
- z.P1 = c(1.20226371286803, 1.22122136357895,1.02442932195400),
- market.returns.P2 = c(0.203037609116444, 0.123122376136099,0.121880488983820),
- z.P2 = c(1.118400430819, 0.798694545623495,1.29755067543957),
- market.returns.P3 = c(0.230304109532112, 0.289262660515515,0.164866239494693),
- z.P3 = c(1.17618117392934, 0.795008683829453,0.650736332270758),
- market.returns.P4 = c(0.231338818884745, 0.213858364836974,0.207154237634752),
- z.P4 = c(0.771450066857429, 0.415931231130697,0.696448914066602),
- market.returns.P1 = c(5.26604920888266, 1.36611602200152,5.9247311493511),
- z.P1 = c(-1.85703263049467, -0.865157804896683,0.289561687438957),
- market.returns.P2 = c(5.02835715460001, 8.0307007906172,7.21392256382075),
- z.P2 = c(-4.2277468665565, -2.56777576762391,-0.787759673062059),
- market.returns.P3 = c(5.23593818385294, 2.33831866638673,3.21908464133114),
- z.P3 = c(-1.12803270842405, -3.44720423923131,-2.30557614900882),
- market.returns.P4 = c(4.81246929972659, 2.04395903547657,3.20139329165723),
- z.P4 = c(-2.66170005367969, -3.85522542589652,-0.669472493949494)),
- .Names = c("market.returns.P1", "z.P1", "market.returns.P2","z.P2", "market.returns.P3", "z.P3",
- "market.returns.P4", "z.P4", "market.returns.P1", "z.P1","market.returns.P2", "z.P2", "market.returns.P3", "z.P3",
- "market.returns.P4", "z.P4", "market.returns.P1", "z.P1", "market.returns.P2", "z.P2", "market.returns.P3",
- "z.P3", "market.returns.P4", "z.P4"),
- row.names = c("Company_A","Company_B", "Company_C"), class = "data.frame"),
- check.attributes=FALSE))
-})
Deleted: pkg/inst/tests/test_marketresiduals.R
===================================================================
--- pkg/inst/tests/test_marketresiduals.R 2015-01-26 14:49:09 UTC (rev 404)
+++ pkg/inst/tests/test_marketresiduals.R 2015-02-26 08:07:05 UTC (rev 405)
@@ -1,25 +0,0 @@
-context("Market residuals")
-
-test_that("test.market.residuals", {
-library(eventstudies)
-
-load("test_StockPriceReturns.rda")
-load("test_NiftyIndex.rda")
-
-alldata <- merge(StockPriceReturns, NiftyIndex, all = TRUE)
-StockPriceReturns <- alldata[,-which(colnames(alldata) %in% "NiftyIndex")]
-NiftyIndex <- alldata$NiftyIndex
-
-mm.result <- marketResidual(firm.returns=StockPriceReturns[,c("BHEL")],
- market.returns=NiftyIndex)
-mm.result <- xts(mm.result[complete.cases(mm.result),])
-colnames(mm.result) <- "BHEL"
-
-# Calculating manually
-result <- lm(BHEL ~ NiftyIndex, data=StockPriceReturns)
-resid.res <- xts(result$resid,as.Date(attr(result$resid,"names")))
-colnames(resid.res) <- "BHEL"
-
-expect_that(mm.result,equals(resid.res))
-
-})
Deleted: pkg/inst/tests/test_y3c3.rda
===================================================================
(Binary files differ)
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