I have been stuck with the issue of determining which regression supposed to be used temperature bins' panel data. In my first attempt, I intend to run spline regression on my data by using mkspline, can't work for me. I am wondering which regression supposed to work on temperature bin panel data. Perhaps using baseline regression, or smoothing spline, or restricted cubic spline could work.

Here is the general picture of my panel data specification: at the first row shown below are my dependent variables which presented in natural log terms and independent variables: average temperature, total precipitation and 11 temperature bins and each bin-width (AKA, bin's window) is 3-degree Celsius. (<-6, -6~-3,-3~0,...>21).

Basically, I want to fit split regression on my data (let's say, choose one dependent variable such as ln_gdp_percapita, and multiple independent variables such as bin1 ~ bin10), I want to see the spline regression can fit better on my panel data.

I tried very basic regression on this temperature bin' panel data, here is the stata command down below:

In general, I want to see how agriculture or industry sectors respond to daily temperature. So running a simple regression on temperature bin panel data is not sufficient to reach a conclusion. I believe restricted spline regression/smoothing spline or baseline regression might produce better estimation.

Here is example data snippet down below;

My question is fairly straightforward, which regression can be used for temperature bin' panel data? Any basic demo Stata command to see regression output for temperature panel data?

Here is the general picture of my desired output:

here is the graph that I want to produce in Stata:

in this plot, black point line is estimated regression (either baseline or restricted spline regression) coefficient, and dot blue line is 95%confidence interval based on clustered standard errors.

Is there anyone suggest me which regression model I can use for temperature bin' panel data? Any solid idea? Thanks

Here is the general picture of my panel data specification: at the first row shown below are my dependent variables which presented in natural log terms and independent variables: average temperature, total precipitation and 11 temperature bins and each bin-width (AKA, bin's window) is 3-degree Celsius. (<-6, -6~-3,-3~0,...>21).

Basically, I want to fit split regression on my data (let's say, choose one dependent variable such as ln_gdp_percapita, and multiple independent variables such as bin1 ~ bin10), I want to see the spline regression can fit better on my panel data.

I tried very basic regression on this temperature bin' panel data, here is the stata command down below:

Code:

xtreg ln_gva_agr_per_worker bin4 bin5 bin6 bin7 bin8 bin9 bin10 bin11 i.year, fe

Here is example data snippet down below;

My question is fairly straightforward, which regression can be used for temperature bin' panel data? Any basic demo Stata command to see regression output for temperature panel data?

Here is the general picture of my desired output:

Code:

b_h b b_l st error t_stat p-value bin1 (-23.87:-18.44) -0.0129 -0.0306 -0.0483 0.009026 -3.39 0.001 bin2 (-18.44:-13.02) -0.0050 -0.0096 -0.0141 0.002334 -4.1 0 bin3 (-13.02:-7.59) -0.0040 -0.0057 -0.0075 0.00089 -6.44 0 bin4 (-7.59:-2.17) 0.0030 0.0021 0.0011 0.000492 4.23 0 bin5 (-2.17:3.26) -0.0007 -0.0012 -0.0018 0.000278 -4.48 0 bin6 (3.26:8.69) 0.0000 0.0000 0.0000 bin7 (8.69:14.11) 0.0008 0.0001 -0.0005 0.00035 0.41 0.681 bin8 (14.11:19.54) 0.0010 0.0000 -0.0010 0.000511 0.06 0.956 bin9 (19.54:24.96) 0.0028 0.0016 0.0005 0.000574 2.85 0.004 bin10 (24.96:30.39) -0.0031 -0.0057 -0.0083 0.001308 -4.36 0

in this plot, black point line is estimated regression (either baseline or restricted spline regression) coefficient, and dot blue line is 95%confidence interval based on clustered standard errors.

Is there anyone suggest me which regression model I can use for temperature bin' panel data? Any solid idea? Thanks

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