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工具变量有效性问题.zip IV-PROBIT
http://bbs.pinggu.org/thread-2248086-1-1.html
http://bbs.pinggu.org/thread-2210611-1-1.html
eco.zip 东东 2015.02.17
文献01 ENTIRE ARCHIVE
包：ssc install pgmhaz8 in an up-to-date Stata
https://www.iser.essex.ac.uk/resources/survival-analysis-with-stata
https://files.nyu.edu/mrg217/public/homepage.htm
Methods IV: Advanced Quantitative Analysis
http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Survival/
Time to Event Variables
There are unique features of time to event variables. First, times to event are always positive and their distributions are often skewed. For example, in a study assessing time to relapse in high risk patients, the majority of events (relapses) may occur early in the follow up with very few occurring later. On the other hand, in a study of time to death in a community based sample, the majority of events (deaths) may occur later in the follow up. Standard statistical procedures that assume normality of distributions do not apply. Nonparametric procedures could be invoked except for the fact that there are additional issues. Specifically, complete data (actual time to event data) is not always available on each participant in a study. In many studies, participants are enrolled over a period of time (months or years) and the study ends on a specific calendar date. Thus, participants who enroll later are followed for a shorter period than participants who enroll early. Some participants may drop out of the study before the end of the follow-up period (e.g., move away, become disinterested) and others may die during the follow-up period (assuming the outcome of interest is not death).
In each of these instances, we have incomplete follow-up information. True survival time (sometimes called failure time) is not known because the study ends or because a participant drops out of the study before experiencing the event. What we know is that the participants survival time is greater than their last observed follow-up time. These times are called censored times.
There are several different types of censoring. The most common is called right censoring and occurs when a participant does not have the event of interest during the study and thus their last observed follow-up time is less than their time to event. This can occur when a participant drops out before the study ends or when a participant is event free at the end of the observation period.
In the first instance, the participants observed time is less than the length of the follow-up and in the second, the participant's observed time is equal to the length of the follow-up period. These issues are illustrated in the following examples.
Example
A small prospective study is run and follows ten participants for the development of myocardial infarction (MI, or heart attack) over a period of 10 years. Participants are recruited into the study over a period of two years and are followed for up to 10 years. The graphic below indicates when they enrolled and what subsequently happened to them during the observation period.
During the study period, three participants suffer myocardial infarction (MI), one dies, two drop out of the study (for unknown reasons), and four complete the 10-year follow-up without suffering MI. The figure below shows the same data, but shows survival time starting at a common time zero (i.e., as if all participants enrolled in the study at the same time).
Based on this data, what is the likelihood that a participant will suffer an MI over 10 years? Three of 10 participants suffer MI over the course of follow-up, but 30% is probably an underestimate of the true percentage as two participants dropped out and might have suffered an MI had they been observed for the full 10 years. Their observed times are censored. In addition, one participant dies after 3 years of follow-up. Should these three individuals be included in the analysis, and if so, how?
If we exclude all three, the estimate of the likelihood that a participant suffers an MI is 3/7 = 43%, substantially higher than the initial estimate of 30%. The fact that all participants are often not observed over the entire follow-up period makes survival data unique. In this small example, participant 4 is observed for 4 years and over that period does not have an MI. Participant 7 is observed for 2 years and over that period does not have an MI. While they do not suffer the event of interest, they contribute important information. Survival analysis techniques make use of this information in the estimate of the probability of event.
This is called non-informative censoring and essentially assumes that the participants whose data are censored would have the same distribution of failure times (or times to event) if they were actually observed. |
An important assumption is made to make appropriate use of the censored data. Specifically, we assume that censoring isindependent or unrelated to the likelihood of developing the event of interest.
Now consider the same study and the experiences of 10 different participants as depicted below.
Notice here that, once again, three participants suffer MI, one dies, two drop out of the study, and four complete the 10-year follow-up without suffering MI. However, the events (MIs) occur much earlier, and the drop outs and death occur later in the course of follow-up. Should these differences in participants experiences affect the estimate of the likelihood that a participant suffers an MI over 10 years?
In survival analysis we analyze not only the numbers of participants who suffer the event of interest (a dichotomous indicator of event status), but also the times at which the events occur.
The duration of Dutch export relations:
decomposing firm, country and product characteristics
cpb-discussion-paper-258-duration-dutch-export-relations-decomposing-firm-countr.pdf
Product Strategies and Firm Survival in Technologically Dynamic Industries
Product Innovation and Survival in a High-Tech
Industry
DETERMINANTS OF MERGER AND ACQUISITION ACTIVITY IN THE UK:
AN ENDOGENOUS MICROECONOMIC APPROACH
Product Innovation and Survival in a High-Tech
Industry
Survival, Productivity and Growth of New Ventures across Locations
Survival, Productivity and Growth of New Ventures across Locations
A survival analysis of manufacturing firms in export
markets*
Firm survival and economic and financial variability:
Financial uncertainty accelerator?
Entry time, pre-entry experience and firms? exit in the Digital Audio
Players industry
hx24ulom67o9tvo14u6f2hdgxu0g.pdf
Entry time, pre-entry experience and firms? exit in the Digital Audio
Players industry
Evaluating poverty duration and transition:
A spell-approach to rural China
Quality Labels and Firm Survival in the Food Industry
Foreign-owned Plants and Job Security
FIRM’S PERFORMANCE ANALYSIS
USING SURVIVAL METHOD
TO FIND OR NOT TO FIND A FIRST
“SIGNIFICANT” JOB*
—————————————— METHOD ARCHIVE ----------------------
Introduction to Survival Analysis 2014
What is a
Cox model?
Survival Models
Using Time Dependent Covariates and Time Dependent
Coecients in the Cox Model
1. Survival time
2. Censored observations
3. Proc Lifetest: Kaplan-Meier estimate of the survival distribution
4. Comparing survival distributions
5. Proportional hazards regression: Proc PHreg
stset — Declare data to be survival-time data
连老师 面板数据 讲义：
Implementing weak-instrument robust tests for
a general class of instrumental-variables models
Implementing Weak Instrument Robust Tests.pdf
Interaction Effects in Regression
Understanding Interaction Models:
Improving Empirical Analyses
《计量经济学及Stata 应用》，2014 年
Panel Data 4: Fixed Effects vs Random Effects Models
Intra-class correlation in random-effects models
for binary data
Hour 5: Continuous and discrete time, single risk parametric unobserved heterogeneity, gamma frailty, pgmhaz
Repeated Events
Unobserved Heterogeneity and Frailty Models
智取威虎山.The.Taking.of.Tiger.Mountain.2014.TC720P.X264.AAC.Mandarin.CHS-E.torrent
Supplementary material. Discrete time event history analysis
How Frail Are Great British Immigrants to Find First Job After Arrival?
ABSTRACT
Instead of modeling (repeated) cross sectional or panel data when comparing immigrants’ employment patterns, this
study employs Cox models to explore the length of time taking immigrants in the Great Britain (GB) to find their first
employment to measure whether any transition-duration penalties (i.e., the length of time for a transition to take
place) experienced by ethnic minority when compared to the majority groups. Frailty and stratifying terms were
tested to take account of unobserved individual and geographic heterogeneities. Besides testing the default option of
the frailty term in PHREG, three other frailty models were also tested to validate the significance test results of the
frailty term.
xtcloglog — Random-effects and population-averaged cloglog models
https://wap.sciencenet.cn/blog-793574-865649.html
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