Hello SAS Experts,    Following code runs but get error as I only have 10 quarters of data and proc x12 needs 3 years (12 quarters). Any workaround for this?   data have; input quarter : YYQ6. variable1 variable2 variable3; cards; 2018Q3 501 500 95 2018Q4 520 502 96 2019Q1 510 502 101 2019Q2 515 400 105 2019Q3 506 402 106 2019Q4 530 400 98 2020Q1 515 399 95 2020Q2 520 399 95 2020Q3 510 399 95 2020Q4 540 399 95 ; run; proc x12 data=have date= quarter interval=quarter; var variable1; x11; output out = out a1 d10 d11 d12 d13; run;   But get following errors: NOTE: This release of the X12 procedure is based on the Census Bureau X-13ARIMA-SEATS Release Version 1.1. ERROR: Series to be modeled and/or seasonally adjusted must have at least 3 complete years of data. ERROR: An error was found in PROC X12 input. Processing skipped for variable variable1.   ... View more

Hello SAS time Series experts,    Following code gives me error about insufficient number observations as minimum is 31 but I only have 29. Unfortunately I will have to wait months to get additional historical data points Are there any additional options to force SAS to run this test with 28 or 29 observations?   /*cointegrating test (Johansen)*/ title1 "Cointegration Test"; proc varmax data=testdata; model y x1 / dftest cointtest=(johansen); run; title1; Get this error:       ... View more

Dear Time Series/Regression Experts: Is it econometrically sound/okay to use non-stationary independent variables in a time-series (ols) regression? Here's the following delimma/options using the same starting raw/untransformed original data series: 1. I have a regression whereby the dependent variable and independent variables are all stationary (let's say using log difference transformation for all dependent and independent variables). However, then the R squared value from the regression is very low (below 0.20). Coefficients of all variables are statistically significant and regression residuals are stationary and normally distributed. 2. I can run a regression whereby the dependent variable "is" stationary but independent variables are not stationary. However, the R Squared value is much better (>0.40). Coefficients of all variables are statistically significant and regression residuals are still stationary and normally distributed. 3. I compare the final forecasts of the raw/untransformed data resulting form 1 and 2 and and they are very similar. Below are my questions: Is the 2nd model econometrically sound? Are there any published papers to support this? I found an interesting article about "ovedifferencing" by Professor Cochrane which argues that when we overdifference, much of the variation in the data can be thrown out. https://static1.squarespace.com/static/ ... encing.pdf Any latest research suggests option 2 is "okay"? Thanks!   ... View more

my data set has following variables x1 x2 x3 x4....x100 Month   How do I rearrange so that the following happens: Month x1 x2 x3 x4....x100   In other words I need to rearrange so that Month is the first column. But I don't want to have to write down all other 100 variables (i actually have over 7,000 variables).   Thanks in advance!     ... View more

Hello, I have some conceptual econometrics questions on the use of interactoin terms. I have the following time series regression: logdiff(y) = logdiff(r) + logdiff(m) (eq 1), where all variables are stationary. But in their raw form y, r and m are not stationary. But taking a log and first differencing each variable makes them stationary. I have 3 questions: 1) If I want to know see if m and r interact significantly, do I just create a new variable (let's say) r_times_m and let it equal to rXm and re-run the regression as the following?   logdiff(y) = logdiff(r) + logdiff(m) + r_times_m ( eq 2)   2) If I run the above model (eq 2), r_times_m won't be stationary, so would that still be a valid regression for interpretation?   3) Or should I create a a new variables logdiff_r_times_logdiff_m and let it equal to logdiff(r)Xlogdiff(m), then run the following regression?   logdiff(y) = logdiff(r) + logdiff(m) + logdiff_r_times_logdiff_m ( eq 3) Thanks in advance! ... View more