Three best practices for applying regression include, first, using time-series data, which contain observation of variables over multiple time periods whenever possible. Regression models typically have better long-term accuracy when the historical patterns of variables are included in the data. When time-series data is not available, these models are best used to estimate short- to medium-term impacts. Second, it is important to evaluate the goodness of fit of a regression model using different indicators such as the coefficient of determination (R-squared), root mean squared error (RMSE), and residual analysis. Third and finally, validation of a regression model generally benefits from sensitivity analysis on the short- and long-term predictions of the model using systematic changes in the variable values.

Regression models, due to their flexibility in explaining statistical correlations between variables, can be used to assess all aspects of impacts listed in the IMO Strategy.

# **5.1.2 Input/Output models**

I/O models have several limitations that make them less preferable when modeling the wider economic impacts of mitigation measures (Bachmann et al., 2014). These limitations include the absence of macroeconomic feedbacks (such as how price-changes would induce changes in demand for commodities), assumptions of linear proportionality between input and output in economic sectors, and a lack of macroeconomic constraints from factors such as labor, capital, and, land. Nevertheless, I/O models have advantages in being relatively simple and able to simulate limited redistribution effects on trade flows due to mitigation measures.

According to Bachmann et al. (2014), I/O models may be used to reduce the burden of a CGE model when: i) The impacts of mitigation measures are not expected to cause a drastic price changes; ii) The main impacts of transport policies are expected to be within the transport networks (such as reduced travel time); and iii) The analysis is focused on changes in trade flow patterns assuming fixed total demand values. As such, I/O models are not suited to model indicators that are sensitive to price changes, such as GDP.

With regard to the impacts of mitigation measures, I/O models can be used to assess transport dependency, connectivity to main markets, socio-economic progress and development, cargo type and value, and cost-effectiveness.

# **5.1.3 (Spatial) Computable General Equilibrium models**

Despite of the ability of CGE models to simulate the wider economic impacts of mitigation measures, these models also have their shortcomings. These include extensive estimation processes for numerous elasticity parameters, sensitivity of the model results to base-year calibration data, extensive data needs, and relatively more effort to explain complex causal relationships produced by the model.

Since CGE models are able to take into account the interaction of different markets and the behavior of economic agents at the micro level while taking into account macroeconomic feedbacks, they excel at estimating the longer-term evolution of economic variables in a technically consistent manner. Furthermore, CGE models are typically able to produce multiple economic indicators of interest, such as GDPs,<sup>7</sup> trade flows, and welfare in a single run.

SCGE models are best suited to estimate the long-term redistribution of trade flows and economic indicators of States at the global level based on strong/disruptive mitigation measures that portend significant economic consequence. Within this context, it is also important for CGE models to use the proper specification of transport costs between States—that is, costs of transport that include the factors mentioned in section 2.2.1 to ensure the accuracy and the validity of the results.

> 7 SCGE models do not produce GDP indicators as their direct output but they are able to produce such indicators with additional assumptions, please also see footnote 2.

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