Macroeconomic implications of expected loss impairment in banking regulation

Motivation
Whilst working as an Economist for a professional services firm I supported a large UK bank in developing a macro-econometric forecasting model for the purposes of complying with the new IFRS 9 accounting rules. Reflecting on my MSc dissertation ('An Analysis of the Efficiency and Unbiasedness of UK Fixed-Event Macroeconomic Forecasts'), this project prompted questions on whether the potential biases or inefficiencies in the forecasts being used to adhere to this regulation could be having an impact on the wider financial system and economy.
Background and relevant literature
The financial crisis led to a regulatory change in the way that banks assess losses. The new IFRS 9 standard requires early recognition of losses using the expected credit loss approach, which is in contrast to the previous incurred loss approach. Effectively, IFRS 9 requires banks to calculate an unbiased probability-weighted estimate of expected credit loss (ECL) over a range of macroeconomic scenarios.
This is a critical area for study because of the reliance placed on large banks by the economy, as played out by the 2008 financial crisis. How managers implement the regulation and the approach to producing forecasts will implicate both loss impairment and the level of available credit.
There is debate on the impact of loan loss provisioning and the level of bank credit on the real economy:
The (current) incurred loss approach is argued to contribute to procyclicality by forcing a sharp reduction in capital in the bust (default only recognised when probability of default = 100%), whilst enabling excessive lending in the boom.
Converse arguments include the idea that fast recognition of non-performing loans provides immediate pressure for corrective action. E.g. there is evidence that the incurred loss approach did not contribute in a major way to the severity of the financial crisis.
The ECL approach is intended to dampen procyclicality effects, as the ability to recognise credit losses earlier should reduce the build-up of losses. In which case, the regulation is intended to increase financial stability. As the ECL better represents the economic value of the loan, it can be argued to be of most use to main users of bank financial statements (e.g. investors, regulators).
However, elements of the models implementation and the inherent nature of forecasting means that the ECL approach could result in certain imperfections being built into loss assessment, for instance:
Managerial discretion over the timing and measurement of expected losses: IFRS 9 has a three-stage model for impairment, which requires managerial judgement on the length of ECL to recognise (i.e. 12 month or lifetime) for different instruments depending on their level of risk.
Approach to forecasting: Many banks are not fully equipped to make 'unbiased' forecasts, as required by the regulation. For instance, individual forecasters (e.g. in-house economists) have been shown to have biases and do not fully use public information (inefficiency), whilst there are differences in the use of information by city vs. non-city forecasters.
Areas for exploration
The nature of imperfections in the forecasts used by banks in determining ECL (e.g. direction, magnitude, origin). Empirically, this could use forecasts and credit risk summaries published by banks in annual reports.
Does managerial discretion exist only for certain types of risks? E.g. is it specific to the industry of the loan, or does it vary with manager characteristics (e.g. experience).
Then, how do these imperfections impact the level of bank credit, and subsequently the economy? The link between financial stability and bank loan loss provisioning has been studied, but the new IFRS 9 regulation provides a new context for this subject. A DSGE model could be implemented to analyse the wider economic implications.