Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
George Bentley <george.bentley@uconn.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Quantile Regression Coefficient Investigation for Individual Observations |

Date |
Wed, 30 Jan 2013 10:56:21 -0500 |

On Mon, Jan 28, 2013 at 10:22 AM, George Bentley <george.bentley@uconn.edu> wrote: > > Hi David, > > My apologies for the delayed response but I used your advice and thought > about my end goal. I have decided that identifying EKC conformity on a > county by county basis is not necessary. Due to the presence of spatial > autocorrelation in the indicators I am going to use an spatial error > regression model. Thank you again for your assistance, it is appreciated. > > Best, > George > > > On Fri, Jan 18, 2013 at 10:35 PM, David Hoaglin <dchoaglin@gmail.com> > wrote: >> >> Hi, George. >> >> The additional information is helpful. Thank you. >> >> Though you probably do not intend it that way, I could read the >> description of the EKC as longitudinal. Your data are >> cross-sectional. If the EKC was formulated from a longitudinal point >> of view, cross sectional data may show a different pattern of >> behavior. >> >> I am not an expert on quantile regression, but I don't see why it >> would be desirable to use that approach with your data. What happens >> when you use ordinary regression? >> >> A U-shaped pattern may not be quadratic. If the distribution of per >> capita income is reasonable, you could explore the pattern by setting >> up categories of per capita income (perhaps as many as 10 of them) and >> using the corresponding dummy variables (for the categories other than >> the reference category) as the predictors (instead of per capita >> income and per capita income squared). Plotting the coefficients of >> those dummy variables against the midpoints of the categories should >> give you an idea of the pattern, after adjusting for the contributions >> of the other variables. >> >> So far, I don't see a reason for fitting quantile regressions and >> identifying the counties that happen to have a zero residual for a >> particular quantile regression. I also don't see the logic of making >> statements about whether the coefficients of per capita income and per >> capita income squared are correct. >> >> David Hoaglin >> >> On Fri, Jan 18, 2013 at 8:57 AM, George Bentley >> <george.bentley@uconn.edu> wrote: >> > Hi David, >> > >> > Thank you for your response. I would be happy to supply more >> > information to better detail what I am hoping to achieve. >> > >> > I am investigating an empirical model called the Environmental Kuznets >> > Curve (EKC), it is used to model the relationship between a measure of >> > environmental quality/degradation and a measure of income. When >> > investigating the relationship between environmental quality and >> > income if an EKC exists it appears as a U-shaped curve (quality on y >> > axis, income on x axis). The general idea is that at low levels of >> > income prior to economic development very little damage occurs to the >> > environment, then as economic development and consequent increases in >> > income take place there is greater damage to environment/decrease in >> > environmental quality, finally it is believed an inflection point is >> > reached where further increases in income correspond to improved >> > levels of environmental quality (better technology, greater demand for >> > cleaner environment, etc.). >> > >> > >> > The functional form of the EKC is captured in the EKC income >> > polynomial consisting the per capita income term and per capita income >> > term squared. In this scenario I am treating agriculture as an >> > environmental quality measure so if an EKC exists there should be a >> > negative sign on the per capita income term and positive sign on the >> > per capita income squared term. The county area, county population, >> > and county topography are acting as controls. >> > >> > You are correct, each county of the 153 counties has a value for per >> > capita income, per capita income squared, county area, county >> > population, and county topography. My rationale and train of thought >> > for using quantile regression, please correct me if this is wrong is >> > as follows. >> > >> > After running the code in my original email and constructing the >> > quantile plot I could see that there were cases where the coefficient >> > on the per capita income term is negative and the coefficient on the >> > per capita income squared term is positive. At this point I went into >> > Stata and began running single quantiles individually finding that the >> > quantile range where the values match what I am looking for is >> > .52-.70. As I ran each quantile from .52 to .70 individually I >> > generated the residuals for each observation. After reading that in >> > quantile regression there are as many observations with residuals of >> > zero as indepedent variables (5 indepedent variables + 1 intercept >> > term in my case) I found the six observations in each quantile with a >> > residual of zero. I believed that I could then say for these six >> > counties (in a single quantile) the coefficients on the per capita >> > income term and per capita income squared term were correct. Is this >> > the wrong way to look at the outputs? >> > >> > Again, I appreciate any help and insight you can offer. >> > >> > Thank you, >> > George Bentley >> > >> > On Thu, Jan 17, 2013 at 4:32 PM, David Hoaglin <dchoaglin@gmail.com> >> > wrote: >> >> Hi, George. >> >> >> >> It would help if you gave some more information about your analysis >> >> and its goals. >> >> >> >> Am I correct that your data consist of one observation for each of the >> >> 153 counties? >> >> >> >> What is the motivation for the quantile regressions? >> >> >> >> Each of the quantile regressions has an overall coefficient for per >> >> capita income and an overall coefficient for per capita income >> >> squared, but not separate coefficients for the individual counties. >> >> >> >> How confident are you that per capita income and per capita income >> >> squared capture the functional relation between a given quantile and >> >> per capita income? Does the way in which the coefficients of those >> >> two predictors vary across the quantiles make substantive sense? Many >> >> departures from a linear relation are not quadratic. You may be able >> >> to get the data to guide the choice of functional form. >> >> >> >> I hope this discussion is helpful. >> >> >> >> David Hoaglin >> >> >> >> On Wed, Jan 16, 2013 at 6:24 PM, George Bentley >> >> <george.bentley@uconn.edu> wrote: >> >>> Good evening, >> >>> >> >>> I am new to Stata software and Statalist. So far I have been using >> >>> Stata solely for quantile regression. I am able to run quantile >> >>> regression without issue but would like to delve deeper and am unsure >> >>> of how to proceed. In a project, I am working with 153 counties >> >>> where >> >>> agricultural cover is the dependent variable and the independent >> >>> variables are per capita income, per capita income squared, county >> >>> area, county population, and county topography. >> >>> >> >>> Here is an excerpt of my Stata code: >> >>> >> >>> quietly qreg laagrpc larea lapop topo lainc lainc2, >> >>> quantile(.1) >> >>> estimates store QR_10 >> >>> quietly qreg laagrpc larea lapop topo lainc lainc2, >> >>> quantile(.2) >> >>> estimates store QR_20 >> >>> quietly qreg laagrpc larea lapop topo lainc lainc2, >> >>> quantile(.3) >> >>> estimates store QR_30 >> >>> quietly qreg laagrpc larea lapop topo lainc lainc2, >> >>> quantile(.4) >> >>> estimates store QR_40 >> >>> quietly qreg laagrpc larea lapop topo lainc lainc2, >> >>> quantile(.5) >> >>> estimates store QR_50 >> >>> quietly qreg laagrpc larea lapop topo lainc lainc2, >> >>> quantile(.6) >> >>> estimates store QR_60 >> >>> quietly qreg laagrpc larea lapop topo lainc lainc2, >> >>> quantile(.7) >> >>> estimates store QR_70 >> >>> quietly qreg laagrpc larea lapop topo lainc lainc2, >> >>> quantile(.8) >> >>> estimates store QR_80 >> >>> quietly qreg laagrpc larea lapop topo lainc lainc2, >> >>> quantile(.9) >> >>> estimates store QR_90 >> >>> estimates table QR_10 QR_20 QR_30 QR_40 QR_50 QR_60 QR_70 QR_80 >> >>> QR_90, >> >>> b(%7.3f) se >> >>> >> >>> >> >>> Excerpt of Estimates Table (Standard errors removed): >> >>> >> >>> Q = 0.1 Q = 0.2 Q = 0.3 Q >> >>> = 0.4 Q = 0.5 Q = 0.6 Q = 0.7 Q = 0.8 Q = 0.9 >> >>> Per capita Income 61.581 76.360 65.790 50.775 >> >>> 3.250 -18.945 -6.001 30.653 17.331 >> >>> >> >>> Per Capita Income Sq. -3.039 -3.764 -3.260 -2.553 >> >>> -0.209 0.840 0.196 -1.579 -0.885 >> >>> >> >>> >> >>> From my estimates table I can see that the per capita income term >> >>> has >> >>> a negative coefficient at quantile = 0.6 and quantile = 0.7. Also, I >> >>> can see that the per capita income squared term has a positive >> >>> coefficient at quantile = 0.6 and quantile = 0.7. My main goal is to >> >>> determine the counties having a negative coefficient on the per >> >>> capita >> >>> income term and a positive coefficient on the per capita income >> >>> squared term simultaneously. Is this possible and is there any >> >>> advice >> >>> as to how I should proceed? Thank you for any assistance in advance. >> >>> >> >>> Thank you, >> >>> George Bentley >> >> * >> >> * For searches and help try: >> >> * http://www.stata.com/help.cgi?search >> >> * http://www.stata.com/support/faqs/resources/statalist-faq/ >> >> * http://www.ats.ucla.edu/stat/stata/ >> > * >> > * For searches and help try: >> > * http://www.stata.com/help.cgi?search >> > * http://www.stata.com/support/faqs/resources/statalist-faq/ >> > * http://www.ats.ucla.edu/stat/stata/ >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/faqs/resources/statalist-faq/ >> * http://www.ats.ucla.edu/stat/stata/ > > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Quantile Regression Coefficient Investigation for Individual Observations***From:*George Bentley <george.bentley@uconn.edu>

**Re: st: Quantile Regression Coefficient Investigation for Individual Observations***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Quantile Regression Coefficient Investigation for Individual Observations***From:*George Bentley <george.bentley@uconn.edu>

**Re: st: Quantile Regression Coefficient Investigation for Individual Observations***From:*David Hoaglin <dchoaglin@gmail.com>

- Prev by Date:
**Re: st: how to get hold of the observations used in reg2hdfe** - Next by Date:
**st: error variance increases when we add a variable in a GARCH model** - Previous by thread:
**Re: st: Quantile Regression Coefficient Investigation for Individual Observations** - Next by thread:
**st: sieve function** - Index(es):