Convergence analysis of regional income in Northeastern China: A classical convergence approach
Introduction
Research Methodology
Data
Descriptive analysis
df.describe().round(2)
Exploratory analysis
#line chart
px.line(
df1,
x="year",
y="ln_rgdppc",
color="City"
)
#line chart after hp filter (parameter=100)
px.line(
df3,
x="year",
y="trend100_lngdp",
color="City"
)
px.strip(
df1,
x="ln_rgdppc",
hover_name = 'City',
animation_frame = 'year',
#color="Province",
range_x = [1, 5.5]
)
px.box(
df1,
x="ln_rgdppc",
hover_name = 'City',
animation_frame = 'year',
color="Province",
range_x= [1, 5.5]
)
Regression analysis
Absolute convergence
sns.lmplot(x="lnrgdp2000", y="gr0019", data=df)
px.scatter(
df,
x="lnrgdp2000",
y="gr0019",
hover_name="City",
color="Province",
trendline="ols")
Results and discussion
Regression Table
y = df['gr0019']
X = df['lnrgdp2000']
X_withconst = sm.add_constant(X)
OLS = sm.OLS(y, X_withconst).fit()
print(OLS.summary())
smf.ols(formula='gr0019 ~ lnrgdp2000 + share_sec2000_x', data=df).fit().summary()