In a conversation with uni professor of financial data analysis and econometrics, a possible explanation came up for the Phillips curve, as for omitted variable bias that doesn’t allow the pure Phillips curve model to demonstrate the correlation between unemployment & inflation, the Phillips curve model omitted variable bias could be improved by adding other estimator parameters as nominal income, average annual income and similar, in order to explain, the inference between levels of unemployment and inflation of prices. the Granger Wald data clearly explains that Inflation whether CPI or CORE helps predict unemployment, but the opposite inference doesn’t really match up. Omitted variable bias could be explained with nominal GDP, and annual average incomes also detailed for income and age brackets.
4 lags VARbasic, parameters: U6unemployment, PCE, GDPxcapita, CORE Inflation. Adding two variables such as PCE and GDPxCapita to integrate the parametric inference with CORE Inflation. Graphs explain how CORE Inflation impulses generate +/- 2 signa responses in PCE, GDPxCpt, U6
The VAR model still doesn’t explain U6 unemployment parametric inference on other variables. That seems consistent with the hypothesis that the Phillips curve model may be OVB and that GDPxcapita and wage levels could be more effective variables.
Phillips curve model, added Employment ratio, to Unemployment, CPI & CORE. Graph regress less than +/-1 sigma, considering the employment ratio & unemployment regressed with CPI. impulse response produces less than 1 sigma dispersion on Inflation.
can see also that the inference between CORE sticky inflation | employment ratio, has very similar impulse responses, of Employment ratio | CPI. What found it’s that CORE Inflation generates impulse responses in the Unemployment rate change. These can provide empirical evidence, that the least decelerating measure of Inflation metrics, such as CORE Inflation it’s correlated to the robustness of labor market and low unemployment rate. In hypothesis, could be possible to forecast that decreasing CORE Inflation to 2%, could produce an inverse correlation in the unemployment rate and therefore see an increase in unemployment in the coming months and quarters.
These are the month-on-month % change of United States CORE INFLATION. Possible to see how the monthly % change rises in 3 months period, a signal of robust core spending and available disposable income. Eventually, a decrease in CORE Inflation would reflect a decline in spending & income. Steady averaging 0.30% | 0.45% month on month, year on year CORE Inflation reflects the decreased variance from the previous 12-month period.
This graph makes a better work of the VAR parameters.
Now possible to see how the Unemployment rate & Employment ratio generate impulse responses on CPI, CORE Inflation and most Personal Consumer Expenditures. Almost +/- 1 sigma variation in the impulse responses. These can provide empirical evidence, that the least decelerating measure of Inflation metrics, such as CORE Inflation it’s correlated to the robustness of labor market and low unemployment rate. In hypothesis, could be possible to forecast decreasing CORE Inflation to 2%.