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applemonster · ‎01-07-2022

Hi all the genius, I need to run a two-way fixed effects regression on a panel dataset and enroll instrumental variables (IVs) and 2SLS. I thought I had two options but it seems neither of them work well. Option 1: The syslin procedure. This procedure seems to have a good package to deal with endogeneity, IV and 2SLS but be not compatible with panel data. Option 2: The panel procedure I use this procedure a lot. It works well with panel data and has a statement named "INSTRUMENTS". However, according to the documentation, I find this statement is deeply binded with the moment condition equations of the dynamic panel estimator, which is not my case. So can someone suggest me a good procedure to handle my case? Thanks.

applemonster · ‎01-07-2022

I compare the results of a two-way fixed effects panel data regression returned by SAS and Stata and find the they are completely different. Here below are my SAS code and result screen shot, where bHHIs is my variable of interest. proc panel data=ASSD.province_quarter_2015_12; id Province TimeID; model LoanNum = bHHIs population GDP deltaFIL_GDP / fixtwo; run; Here below are screen shots of my stata settings and results, as currently I have not learned any codes of stata. I choose Statistics -> Longitudinal/panel data -> Linear models -> Linear regression(FE, RE, PA, BE). Regression settings: Panel settings: Automatically generated codes after submission: And results: As you can see, the coefficient of bHHIs flips the sign. Coefficients of other variables also change. Note: in SAS I use "province_quarter_2015_12" whereas in Stata I use "province_quarter_2015_14". It's just because Stata does not accept string Panel ID so I change it into numbers.

applemonster · ‎09-22-2020

Problem is solved in a surprising way. Yes, declaring dummy variables as categorical variables is not the only way to include interaction terms. Instead, this time I add interaction terms by manually multiplying dummy variables with control variables. SAS system does not warn possible multicollinearity any longer. Since I have not made any essential changes to my model, I think possible multicollinearity before is not severe or even just a bug of SAS?

applemonster · ‎09-21-2020

NOTE: The transformed regression does not have full rank. Be aware of possible multicollinearity and/or identification problems before using the FixOne method results.

applemonster · ‎09-21-2020

Recently I use panel procedure to do a panel data analysis. Here below is my original code: /* R square = 0.3966 F test for no fixed effects p < 0.0001 Some cross-section fixed effects are significant while some are not. beta_StructureBreak = 3996.575 p <0.0001 beta_event_level1 = 380.9467 p = 0.0185 beta_event_level2 = 563.2005 p = 0.0005 beta_GDP_per_capita = 344.8128 p < 0.0001 beta_HHI = 0.643129 p <0.0001 */ proc panel data=rrd_come.city_ln_combo_7; id City TimeID; model LoanNum = StructureBreak event_level1 event_level2 GDP_per_capita HHI / fixone printfixed; run; My dataset covers 38 quarters, and event_level1 and event_level2 are two dummies representing 2020Q1 and 2020Q2, respectively. Since the dummy variables are far less than periods, it will not cause perfect multicollinearity. By now, everything was fine. Then I want to add some terms interacted with these two dummy variables. The internet said I have to first declare these two dummy variables as categorical variables in the panel procedure. So here below is my new code: /* R square = 0.4910 F test for no fixed effects p < 0.0001 Some cross-section fixed effects are significant while some are not. beta_StructureBreak = 4056.795 p < 0.0001 beta_event_level1 = -5116.73 p < 0.0001 beta_event_level2 = -4629.75 p < 0.0001 beta_GDP_per_capita = 228.6001 p < 0.0001 beta_HHI = 0.392422 p <0.0001 beta_event_level1*GDP_per_capita = 1071.573 p < 0.0001 beta_event_level2*GDP_per_capita = 1039.692 p < 0.0001 beta_event_level1*HHI = -0.94809 p < 0.0001 beta_event_level2*HHI = -1.04389 p < 0.0001 */ proc panel data=rrd_come.city_ln_combo_7; id City TimeID; class event_level1 event_level2; model LoanNum = StructureBreak event_level1 event_level2 GDP_per_capita HHI event_level1*GDP_per_capita event_level2*GDP_per_capita event_level1*HHI event_level2*HHI / fixone printfixed; run; In the log, the SAS system warns possible multicollinearity. I first thought interaction terms cause it, so I delete relative code and retry the following: proc panel data=rrd_come.city_ln_combo_7; id City TimeID; class event_level1 event_level2; model LoanNum = StructureBreak event_level1 event_level2 GDP_per_capita HHI / fixone printfixed; run; SAS system still warns the possible multicollinearity, which means just declaring these two dummy variables as categorical variables cause possible multicollinearity. So anyone can explain the reason for that? Thanks a lot!!

applemonster · ‎09-17-2020

I cannot quite understand the advantages of proc panle and proc glmmix over proc panel. In other words, what is the major problem if I use proc panel to analyze my case?

applemonster · ‎09-16-2020

I want to add interaction terms in SAS Proc Panel. I checked some online materials and heard that I need to use the Class Statment. So here below is my code: proc panel data=rrd_come.city_ln_combo_8; id City TimeID; class event_level1 event_level2; model LoanNum = StructureBreak event_level1 event_level2 GDP_per_capita HHI event_level1*GDP_per_capita event_level2*GDP_per_capita event_level1*HHI event_level2*HHI/ fixone printfixed; run; And the result looks like below: It makes sense, but it looks ugly. I want to ask whether it is the optimal strategy to include interaction terms. Do we have to treat dummy variables as categorical variables? I mean SAS may treat the values of a categorical variable as text, which may explain the way it output the results. But a dummy variable has two numerical values, 0 and 1, so I think it is a waste to use a categorical-variable format to represent a dummy variable. Is there a better way to write the code? Thanks for your valuable suggestions!

applemonster · ‎09-16-2020

The code to get the first two pictures is: /* R square = 0.2854 F test for no fixed effects p < 0.0001 Some cross-section fixed effects are significant, some are not beta_event_level1 = 3855.073 p < 0.0001 beta_event_level2 = 2038.478 p < 0.0001 beta_GDP_per_capita = 434.9171 p <0.0001 beta_HHI = 0.231173 p = 0.0039 */ proc panel data=rrd_come.city_ln_combo_8; id City TimeID; model LoanNum = event_level1 event_level2 GDP_per_capita HHI / fixone printfixed; run; The code to get the middle two pictures is: /* R square = 0.2886 F test for no fixed effects p < 0.0001 All time fixed effects are significant beta_event = 0 beta_GDP_per_capita = 186.5933 p <0.0001 beta_HHI = -0.11344 p = 0.0002 */ proc panel data=rrd_come.city_ln_combo_8; id City TimeID; model LoanNum = event GDP_per_capita HHI / fixonetime printfixed; run; The code to get the last three pictures is: /* R square = 0.4044 F test for no fixed effects p < 0.0001 All cross-section and time fixed effects are zero and insignificant beta_event_level1 = -4.39E12 p = 0.9997 beta_event_level2 = 0 beta_GDP_per_capita = 329.8557 p < 0.0001 beta_HHI = 0.505056 p <0.0001 */ proc panel data=rrd_come.city_ln_combo_8; id City TimeID; model LoanNum = event_level1 event_level2 GDP_per_capita HHI / fixtwo printfixed; run;

applemonster · ‎09-16-2020

The code to get the first two pictures is: /* R square = 0.2854 F test for no fixed effects p < 0.0001 Some cross-section fixed effects are significant, some are not beta_event_level1 = 3855.073 p < 0.0001 beta_event_level2 = 2038.478 p < 0.0001 beta_GDP_per_capita = 434.9171 p <0.0001 beta_HHI = 0.231173 p = 0.0039 */ proc panel data=rrd_come.city_ln_combo_8; id City TimeID; model LoanNum = event_level1 event_level2 GDP_per_capita HHI / fixone printfixed; run; The code to get the middle two pictures is: /* R square = 0.2886 F test for no fixed effects p < 0.0001 All time fixed effects are significant beta_event = 0 beta_GDP_per_capita = 186.5933 p <0.0001 beta_HHI = -0.11344 p = 0.0002 */ proc panel data=rrd_come.city_ln_combo_8; id City TimeID; model LoanNum = event GDP_per_capita HHI / fixonetime printfixed; run; The code to get the last three pictures is: /* R square = 0.4044 F test for no fixed effects p < 0.0001 All cross-section and time fixed effects are zero and insignificant beta_event_level1 = -4.39E12 p = 0.9997 beta_event_level2 = 0 beta_GDP_per_capita = 329.8557 p < 0.0001 beta_HHI = 0.505056 p <0.0001 */ proc panel data=rrd_come.city_ln_combo_8; id City TimeID; model LoanNum = event_level1 event_level2 GDP_per_capita HHI / fixtwo printfixed; run;

applemonster · ‎09-16-2020

Thank you very much for your suggestions. I will study the test statement. But by now, based on what I have know, I guess your suggestion means that I cannot include both time fixed effects and other dummy variables indicating whether interested event happens in the same model. Am I understanding it correctly?

applemonster · ‎09-16-2020

I currently have a panel data set that contains the quarterly increment of loans initiated from more than 300 cities in China over the period from 2011Q1 to 2020Q2. I want to examine the impact of COVID-19 on lending activities. I believe that both time fixed effects and city fixed effects exist—both the F test for poolability and Hausman Test support fixed-effect model. I use the panel procedure to analyze it. I also set a dummy variable, "event", to indicate whether the pandemic has occurred. The dummy variable "event" equals to 1 if the period is 2020Q1 or 2020Q2. Currently, I have not added any interactive terms. If I specify a one-way model using fixone option that only allows cross-section fixed effects, I found that some coefficients of these fixed effects are significant while some are not. The coefficients of the dummy variable "event" and other control variables are also significant. By now, it is acceptable. However, if I specify a one-way model using fixonetime option that only allows time fixed effects, it will be a mess. Although the coefficients of all time fixed effects are significant, the coefficient of the dummy variable "event" becomes zero. I am wondering whether it is caused by perfect multicollinearity. After all, in my setting, event = 1 means the same thing as the fixed effects of 2020Q1 and 2020Q2. My guess is that the impact of the dummy variable "event" has already been absorbed by time fixed effects. Is that right? If it is correct, does it mean that you can never use the time fixed effects and some dummy variables indicating certain event happens at the same time? However, several papers are studying the impact of COVID-19 using them simultaneously in their model. They did not mention any problems of such a setting. So I feel confused. In a branched setting, instead of using the dummy variable "event", I use two dummy variable, "event_level1" and "event_level2", which equals to 1 if the period is 2020Q1 and 2020Q2 respectively, to visualize the dynamic of COVID-19 impact. The result is getting worse because the coefficients of all fixed effects and interested dummy variables "event_level1" and "event_level2" become zero this time. It looks caused by other reasons instead of multicollinearity this time. Are there any generous and smart friends who know the reason? Thank you so much!

applemonster · ‎08-18-2020

Multicollinearity usually happens when variables describe multiple sides of the same thing. However, this may be not my case because NewConfirmed here is the daily increase of new confirmed COVID19 cases and NewSuspected here is the daily increase of new suspected COVID19 cases. So, theoretically, there should be no multicollinearity problem.

applemonster · ‎08-18-2020

I run the following code: proc panel data=rrd_come.citydata_4; id City TimeID; model LoanNum = NewConfirmed NewSuspected / fixone; run; and I got such error: ERROR: No residual in the model to analyze. Computations will not be performed. NOTE: The transformed regression does not have full rank. Be aware of possible multicollinearity and/or identification problems before using the FixOne method results. NOTE: PROCEDURE PANEL used (Total process time): real time 4.64 seconds cpu time 4.68 seconds Can any generous friends help me out? I can send the data through email if necessary. Thanks in advance!

applemonster · ‎08-18-2020

I use proc panel to run a simple panel data analysis. Here below is the most relevant code: proc panel data=rrd_come.citydata_4; id City TimeID; model LoanNum = NewConfirmed NewSuspected / ranone; run; and I got the following Errors (Error is the mother of success but obviously my success has too many mothers): ERROR: Invalid Operation. ERROR: Termination due to Floating Point Exception I have searched google and find the following information: https://support.sas.com/kb/46/318.html But it is definitely not my case because my dataset is just a 68846*5 table. Are there any geniuses can help me out? Thanks in advance!

applemonster · ‎08-16-2020

It's not used for report purpose. I want to do panel data analysis of the impact of COVID19 on the lending and borrowing activities in China. Panel data analysis typically studies multiple areas over multiple periods. I want to treat loan number, average loan size and other similar variables of a city as explained variables, and handle daily new COVID19 cases and other control variables as explaining variables. So I think it is instinctively and visually better to adopt such a dataset format. But obviously, I am not a data scientist yet, so I hope that you can give me some constructive suggestions.

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Re: Just declaring categorical variables cause multicollinearity

Re: Just declaring categorical variables cause multicollinearity

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Re: Why do the coefficients of cross-sectional fixed effects and time ...

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