Since these macroeconomic variables are likely to be mutually endogenous and influence each other with some time lags, a VAR or VARMA model would make more sense. VAR would be the simpler and more popular alternative and is often used for modelling the macroeconomic indicators you have. In a VAR model you would select the lag order using information criteria or residual diagnostics. In R the relevant package would be "vars" and function VARselect.

The drawback with your approach is that endogeneity among variables and lagged effects are not properly accounted for.

Since these macroeconomic variables are likely to be mutually endogenous and influence each other with some time lags, a VAR or VARMA model would make sense. VAR would be the simpler and more popular alternative; it is often used for modelling the macroeconomic indicators you have. In a VAR model you would select the lag order using information criteria or residual diagnostics. In R the relevant package would be "vars" and function VARselect.

Vector error correction model (VECM; it is a special case of VAR) could be relevant if your variables are cointegrated. After selecting the lag order for a VAR model in levels, you would proceed with cointegration analysis (function ca.jo from package "urca") and VECM modelling.

Regarding inclusion/exclusion of the crisis dummy, you could compare values of information criteria and do residual diagnostics, similarly to when doing lag order selection.

Some relevant references:

• Pfaff "Analysis of Integrated and Cointegrated Time Series with R"
• "vars" vignette "VAR, SVAR and SVEC Models: Implementation Within R Package vars"