# Is my model defensible for my setting? Help checking assumptions of dynamic panel data model

I have panel data for 150 countries, with daily data from the start of 2018. I want to estimate the effect of the lags of one variable (x) on another variable (y), accounting for some covariates. I have selected a model I think works, but I'm not sure, and I could use some help checking that I'm not violating any key assumptions of the model.

## Here is my model

(Sorry for the length.) I am using a linear regression model that includes lags of x and y as an explanatory variable, with random coefficients and random effects for each unit. Specifically:

• The form of the equation is y_i,t = a_i + j_1 * y_i,t-1 + j_2 * y_i,t-2 + B_1 * x_i,t-1 + B_2 * x_i,t-2...
• a_i is a unit-specific intercept / random effect (not a fixed effect, as explained below)
• In the equation, y is regressed on the past values of y, in addition to the past values of x
• There are also 8 other covariates / other explanatory variables with the same form as x
• One issue is that while y, x vary day by day, some covariates vary much more slowly - e.g. year by year.
• I run a separate regression for each unit, which includes an intercept
• I use the weighted average of the coefficients (each unit has its own coefficients) see page 204 here
• The coefficients are just estimated with Ordinary Least Squares.
• I can either find a way to Wald-test the coefficients of x, or just use the variance of the coefficents of x to test for significance.

## Here are my concerns about the model

Some reasons I chose this model, and some concerns I had about it:

• The data are non-stationary, and their order of integration is 1 (first-differencing the data yields stationary data). I think this could be a problem but I'm not sure how to fix it. The Toda-Yamamoto procedure recommends only modeling with p+1 lags of x and only Wald-testing the first p lags, but I'm not sure if that applies here.
• Because of the equation's form, the error term correlates with the first-difference of y. I think this is called Nickell bias, which I know disappears as T grows - my T is large so I think it should be okay.
• x is lagged because it cannot have an immediate effect on y - the effect could be delayed by between 1 and 14 days (from domain knowledge)
• The Granger Causality test is an intuitive choice for my data, and this equation has the same form as the equation used in the Granger test. Intuitively, if x has a causal effect on y, x should affect y beyond the past values of y alone.
• I tested for serial correlation in the residuals and there isn't any, for any of the panel units.
• Granger causality is not causality unless you make some strong assumptions. Commented Apr 24, 2021 at 17:18
• I'm not using the Granger test directly - my equation just has the same form as the equation typically used in the Granger test. And in my context, there are good reasons to believe there is a causal influence if the coefficients of my model turn out to be significant. Commented Apr 25, 2021 at 14:51