general equilibrium model and the bayesian var model. 1 another study has evaluated our indicator models. 2 in addition we publish information to enable an assessment of the monetary policy conducted over the past 2 - 3 years in a special appendix to the first monetary policy report of every year. it contains, for instance, comparisons of our forecasts with other forecasts and with outcomes. the material we produce in our annual evaluations forms an important basis for the riksdag committee on finance ’ s report on monetary policy. every year the committee evaluates at its own initiative the monetary policy conducted during the previous years. they have done so since 1999 when the riksbank became independent. last year we also had two external researchers, francesco giavazzi and frederic mishkin, who made a more in - depth evaluation of monetary policy. this was commissioned by the committee on finance and covered the entire ten - year period of inflation targeting. new study of the riksbank ’ s forecasting performance as a step in the evaluation work, three economists at the monetary policy department of the riksbank recently made a comprehensive analysis of our forecasting performance during the “ modern forecasting models in action : improving analyses at central banks ” adolfson, m., m. k. andersson, j. linde, m. villani & a. vredin, ( 2007 ), international journal of central banking, in the process of being published. ” the riksbank ’ s new indicator procedures ” andersson, m. and m. lof, sveriges riksbank economic review 2007 : 1, sveriges riksbank. period 2000 - 2006. 3 they have analysed the forecasts of the most important variables for monetary policy – gdp, employment, productivity and inflation. 4 in the study they have tried to estimate how accurate the forecasts have been on average by comparing them with outcomes. to gain a perspective of the accuracy, our forecasts have also been compared with forecasts from a simple time series model. the so - called ar models, which are often used as a standard comparison in evaluations of this type, quite simply function so that one adjusts the variable in question with the aid of its own historical data. the type of time series model chosen for the comparison is, despite its simplicity, difficult to surpass, according to several international studies. moreover, they have analysed how far ahead we can make forecasts with good accuracy, that

2015, would have amounted to about €50 billion, or 5 per cent of all loans disbursed, close to precrisis levels. the growth of npls was strongly influenced by recovery times, which until now have been particularly long in italy ; the measures approved in the last year will shorten them significantly. as the bank of italy has emphasized in the past, reducing average recovery times even by just two years would have determined, together with a higher valuation of npls, a ratio of bad debt to total loans close to half of what it is today. most italian banks are capable of dealing with the still fragile cyclical conditions, lending to the economy and competing efficiently on the market. a number of assessments made in the last few weeks put the recapitalization needs of the entire italian banking system at somewhere in the order of tens of billions of euros, based on the assumption that the total stock of bad loans, and possibly even some of the loans that are “ unlikely to be repaid ”, must be sold at once by all banks at a price equal to approximately half the value of the bad loans recorded in banks ’ balance sheets. in italy a loan is classified as “ non - performing ” based on harmonized criteria established at the european level and published by the eba in 2013, which are largely aligned with those previously applied in italy and with international practice. for the valuation of non - performing loans in their balance sheets, italian banks must comply with international accounting and reporting standards ( ias / ifrs ). as they permit discretion we have constantly encouraged banks to adopt prudent valuation policies that take sufficient account of the degree of uncertainty in recovery times and flows. our supervisory action on provisioning in 2012 and 2013 is a clear example of this. in the approach traditionally taken in italy, npls can be divided into at least two large and very dissimilar categories based on the degree of difficulty faced by debtors : of the €360 billion worth of gross npls outstanding at the end of 2015, bad loans accounted for €210 billion ; €150 billion were in loans classified as “ unlikely to be repaid ”, past - due or in breach of overdraft ceilings. these two categories of npls obviously have different requirements in terms of coverage and write - downs. both types of loans must be booked in the balance sheets with due prudence, but not at values corresponding to their immediate liquidation. loans in the second