r - Lag dependent variable -


i want compute following time series regression using r:

$\delta y_t=\beta_1 \delta x_t+\beta_2 \delta z_t+\beta_3 \delta m_t+\beta_4 \delta y_{t−1}$

since have not experience r want ask if following r code gives me want: 

y <- ts(diff(yy)) x <- ts(diff(xx)) z <- ts(diff(zz)) m <- ts(diff(mm)) l1 <- lag(y, k=-1) int <- ts.intersect(y, x, z, m, l1) reg1 <- lm(y~x+z+m+l1, data=int) summary(reg1)`

sorry can`t find typo in formula.

here data sample:

date         yy     xx       zz      mm 03.01.2005  2.154   2.089   0.001   344999 04.01.2005  2.151   2.084   0.006   344999 05.01.2005  2.151   2.087   -0.007  333998 06.01.2005  2.15    2.085   -0.005  333998 07.01.2005  2.146   2.086   -0.006  333998 10.01.2005  2.146   2.087   -0.007  333998 11.01.2005  2.146   2.089   -0.009  333998 12.01.2005  2.145   2.085   -0.005  339999 13.01.2005  2.144   2.084   -0.004  339999 14.01.2005  2.144   2.085   -0.005  339999 17.01.2005  2.143   2.085   -0.005  339999 18.01.2005  2.144   2.085   -0.005  347999 19.01.2005  2.143   2.086   -0.006  354499 20.01.2005  2.144   2.087   -0.007  354499 21.01.2005  2.143   2.087   -0.007  354499 24.01.2005  2.143   2.086   -0.006  354499 25.01.2005  2.144   2.086   -0.006  354499 26.01.2005  2.143   2.086   -0.006  347999 27.01.2005  2.144   2.085   -0.005  352998 28.01.2005  2.144   2.084   -0.004  352998 31.01.2005  2.142   2.084   -0.004  352998 01.02.2005  2.142   2.083   -0.003  352998 02.02.2005  2.141   2.083   -0.003  357499 03.02.2005  2.144   2.088   -0.008  357499 04.02.2005  2.142   2.084   -0.004  357499 07.02.2005  2.142   2.084   -0.004  359999 08.02.2005  2.141   2.083   -0.003  355500

i tried fg nu answered original question error message. 1. zoox = zoo(test4[, -1], order.by = test4$date) comand works fine. (the first column of data set date column, dataset looks data sample in question.) 2. ran regression: lmx = dynlm(d(yy) ~ d(xx) + d(zz) + d(mm) + l(yy, 1), data = zoox) here following error message: error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-na) cases in addition: warning message: in dynlm(d(yy) ~ d(xx) + d(zz) + d(mm) + l(yy, 1), data = zoox) : empty model frame specified bug overseeing?

use dynlm package. here example using data supplied:

library(dynlm)  dfx = read.table(   textconnection(     "date         yy     xx       zz      mm   03.01.2005  2.154   2.089   0.001   344999   04.01.2005  2.151   2.084   0.006   344999   05.01.2005  2.151   2.087   -0.007  333998   06.01.2005  2.15    2.085   -0.005  333998   07.01.2005  2.146   2.086   -0.006  333998   10.01.2005  2.146   2.087   -0.007  333998   11.01.2005  2.146   2.089   -0.009  333998   12.01.2005  2.145   2.085   -0.005  339999   13.01.2005  2.144   2.084   -0.004  339999   14.01.2005  2.144   2.085   -0.005  339999   17.01.2005  2.143   2.085   -0.005  339999   18.01.2005  2.144   2.085   -0.005  347999   19.01.2005  2.143   2.086   -0.006  354499   20.01.2005  2.144   2.087   -0.007  354499   21.01.2005  2.143   2.087   -0.007  354499   24.01.2005  2.143   2.086   -0.006  354499   25.01.2005  2.144   2.086   -0.006  354499   26.01.2005  2.143   2.086   -0.006  347999   27.01.2005  2.144   2.085   -0.005  352998   28.01.2005  2.144   2.084   -0.004  352998   31.01.2005  2.142   2.084   -0.004  352998   01.02.2005  2.142   2.083   -0.003  352998   02.02.2005  2.141   2.083   -0.003  357499   03.02.2005  2.144   2.088   -0.008  357499   04.02.2005  2.142   2.084   -0.004  357499   07.02.2005  2.142   2.084   -0.004  359999   08.02.2005  2.141   2.083   -0.003  355500"   ), header = true) dfx$date = as.date(dfx$date, format = "%d.%m.%y")  # convert zoo format zoox = zoo(dfx[, -1], order.by = dfx$date)  # run regression time transformed regressors lmx = dynlm(d(yy) ~ d(xx) + d(zz) + d(mm) + d(l(yy, 1)), data = zoox) summary(lmx)

this gives output:

> summary(lmx)  time series regression "zoo" data: start = 2005-01-05, end = 2005-02-08  call: dynlm(formula = d(yy) ~ d(xx) + d(zz) + d(mm) + d(l(yy, 1)),      data = zoox)  residuals:        min         1q     median         3q        max  -0.0039592 -0.0003746  0.0000854  0.0006254  0.0018715   coefficients:               estimate std. error t value pr(>|t|)   (intercept) -5.008e-04  2.766e-04  -1.811   0.0853 . d(xx)        2.943e-01  2.409e-01   1.222   0.2359   d(zz)        2.038e-03  1.715e-01   0.012   0.9906   d(mm)        7.808e-08  8.251e-08   0.946   0.3553   d(l(yy, 1)) -1.677e-01  2.103e-01  -0.797   0.4346   --- signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  residual standard error: 0.001248 on 20 degrees of freedom multiple r-squared:  0.2579,    adjusted r-squared:  0.1095  f-statistic: 1.738 on 4 , 20 df,  p-value: 0.1813

Comments

Post a Comment

Popular posts from this blog

python - Mongodb How to add addtional information when aggregating? -

i beginner , wrote line pipeline works, wanna add other information output, screen name , or number of tweets.i tried add under $group gave me syntax error everytime here pipeline: def make_pipeline(): # complete aggregation pipeline pipeline = [ { '$match': { "user.statuses_count": {"$gt":99 }, "user.time_zone": "brasilia" } }, { "$group": { "_id": "$user.id", "followers": { "$max": "$user.followers_count" } } }, { "$sort": { "followers": -1 } }, { "$limit" : 1 } ]; i using on example : { "_id" : objectid("5304e2e3cc9e684aa98bef97"), "text" : "first week of school on :p", "in_reply_to_status_id" : null, "retweet_count" : null, ...
Read more

java - Spring Data JPA: Why findOne(id) executing delete query internally? -

i have 1 application using @manytomany annotation link 2 different entities 1 parent entity. not sure if have implemented properly. when execute .findone() on parent entity's repository find it, @ first fetch data along executing delete queries internally delete record mapping table (a table jpa creates after adding @manytomany annotation.). following generated sql queries , code snippet: java class @entity @table(name = "inventory") public class inventory { @id long id; // many other properties @manytomany(fetch=fetchtype.lazy) private list<stream> streams; @manytomany(fetch=fetchtype.lazy) private list<subject> subjects; } there no other annotation i've put in stream , subject entity class link inventory class. when run app, create following tables: inventory stream subject inventory_stream inventory_subject (inventory_stream , inventory_subject, 2 tables created automatically, don't k...
Read more

java - Incorrect order of records in M-M relationship in hibernate -

Image
i have 4 records in bridge table in mysql database. using @manytomany in hibernate, accurate order of employees incorrect incorrect of projects. following required material figure out doing mistake. this source. @entity @table(name="project") public class projectbean { @id @generatedvalue @column(name="project_id") private int projectid; @column(name="title") private string projecttitle; @manytomany(mappedby="projects") private collection<employeebean> employees; public projectbean() { employees = new arraylist<employeebean>(); } //getters & setters } @entity @table(name="employee") public class employeebean { @id @generatedvalue @column(name="employee_id") private int employee_id; @column(name="first_name") private string ...
Read more