Price currents and newspapers are major sources of information on prices during the eighteenth and nineteenth centuries, but drawing conclusions about trends and fluctuations in values from the quotations in these sources poses several recurrent difficulties. After discussing the origins of the prices in these sources, we use a range of examples, mainly involving commodity prices, to illustrate important problems in working with historical price data. These include missing observations and price inertia, varying gaps between low and high price quotations, and the splicing together of price series from different sources or for different commodity qualities. The last two problems often arise from changes over time in the detail with which prices for heterogeneous commodities were reported.

The paper solves the loss reserving problem using Kalman recursions in linear statespace models. In particular, if one orders claims data from run-off triangles to time series with missing observations, then state space formulation can be applied for projections or interpolations of IBNR (Incurred But Not Reported) reserves. Namely, outputs of the corresponding Kalman recursion algorithms for missing or future observations can be taken as the IBNR projections. In particular, by means of such recursive procedures one can perform effectively simulations in order to estimate numerically the distribution of IBNR claims which may be very useful in terms of setting and/or monitoring of prudency level of loss reserves. Moreover, one can generalize this approach to the multivariate case of several dependent run-off triangles for correlated business lines and the outliers in claims data can be also treated effectively in this way. Results of a numerical study for several sets of claims data (univariate and multivariate ones) are presented.