Hi, and welcome!

Please see the FAQ: What's a reproducible example (`reprex`) and how do I create one? Using a reprex, complete with representative data will attract quicker and more answers. It *really* helps to have representative data to work with. Doesn't have to be your complete data or even your data at all, so long as it shows the same error.

Take a look at the signature of `ivreg`

in `help(ivreg)`

and the details:

Usage

ivreg(formula, instruments, data, subset, na.action, weights, offset, contrasts = NULL, model = TRUE, y = TRUE, x = FALSE, ...)

Arguments

formula, instruments formula specification(s) of the regression relationship and the instruments. Either instruments is missing and formula has three parts as in y ~ x1 + x2 z1 + z2 + z3 (recommended) or formula is y ~ x1 + x2 and instruments is a one-sided formula ~ z1 + z2 + z3 (only for backward compatibility).

That is, the formula here is incomplete.

See the example given there

```
suppressPackageStartupMessages(library(AER))
# Examples
## data
data("CigarettesSW", package = "AER")
CigarettesSW$rprice <- with(CigarettesSW, price/cpi)
CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)
CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi)
## model
fm <- ivreg(log(packs) ~ log(rprice) + log(rincome) | log(rincome) + tdiff + I(tax/cpi), data = CigarettesSW, subset = year == "1995")
summary(fm)
#>
#> Call:
#> ivreg(formula = log(packs) ~ log(rprice) + log(rincome) | log(rincome) +
#> tdiff + I(tax/cpi), data = CigarettesSW, subset = year ==
#> "1995")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.6006931 -0.0862222 -0.0009999 0.1164699 0.3734227
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 9.8950 1.0586 9.348 4.12e-12 ***
#> log(rprice) -1.2774 0.2632 -4.853 1.50e-05 ***
#> log(rincome) 0.2804 0.2386 1.175 0.246
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.1879 on 45 degrees of freedom
#> Multiple R-Squared: 0.4294, Adjusted R-squared: 0.4041
#> Wald test: 13.28 on 2 and 45 DF, p-value: 2.931e-05
summary(fm, vcov = sandwich, df = Inf, diagnostics = TRUE)
#>
#> Call:
#> ivreg(formula = log(packs) ~ log(rprice) + log(rincome) | log(rincome) +
#> tdiff + I(tax/cpi), data = CigarettesSW, subset = year ==
#> "1995")
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.6006931 -0.0862222 -0.0009999 0.1164699 0.3734227
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 9.8950 0.9288 10.654 < 2e-16 ***
#> log(rprice) -1.2774 0.2417 -5.286 1.25e-07 ***
#> log(rincome) 0.2804 0.2458 1.141 0.254
#>
#> Diagnostic tests:
#> df1 df2 statistic p-value
#> Weak instruments 2 44 228.738 <2e-16 ***
#> Wu-Hausman 1 44 3.823 0.0569 .
#> Sargan 1 NA 0.333 0.5641
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.1879 on Inf degrees of freedom
#> Multiple R-Squared: 0.4294, Adjusted R-squared: 0.4041
#> Wald test: 34.51 on 2 DF, p-value: 3.214e-08
```

^{Created on 2020-03-29 by the reprex package (v0.3.0)}