Guesstimation

Macroeconomic model builders attempting to construct forecasting models frequently face constraints of data scarcity in terms of short time series of data, and also of parameter non-constancy and underspecification. Hence, a realistic alternative is often to guess rather than to estimate parameters of such models. This paper concentrates on repetitive guessing (drawing) parameters from iteratively changing distributions, with the straightforward objective function being that of minimisation of squares of ex-post prediction errors, weighted by penalty weights and subject to a learning process. The numerical Monte Carlo examples are those of a regression problem and a dynamic disequilibrium model.estimation; short data series; macromodels; computations; methodology

LAM modelling of East European economies: Methodology, EU accession and privatisation

The paper presents a new approach to modelling economies in transition, where the adjustment processes are often nonlinear and data series are short. The model presented in the paper, the LAM-3 model, is the latest development in a series of long-run adjustment models, used for simulation and forecasting of several East European Economies. In particular the model contains short-run equations with bilinear error correction derived from a structural vector autoregressive model. The paper also gives derivations of two long-run relationships of the model, those for full-capacity output (reformable and non-reformable labour) and the relationship linking prices, money, incomes and exchange rates. The short-run part evolves around the specification of price and wage dynamics according to the NAIRU principle. Due to the fact that series of data for Eastern European countries are short, the parameters are evaluated with the use of global optimization technique (repetitive stochastic guestimation) rather than by a traditional econometric method. The model was estimated and applied for Czech Republic, Estonia, Latvia, Lithuania, Poland, Slovak Republic, Romania and Ukraine. For each country it consists of 3 long-run and 21 short-run relationships. Examples of simulations presented here evaluate European Union accessibility through inflation correlation measures and Aghion-Blanchard optimal speed of privatisation

Ex-ante dynamics of real effects of monetary policy: Theory and evidence for Poland and Russia, 2001-2003

The paper proposes a new indicator of expected real effects of a policy aimed at controlling inflation. The indicator, called real effect of inflation targeting (REIT), involves the comparison of expected and output-neutral inflation. It is shown that it can be derived from a simple two-dimensional vector autoregressive model of inflation and output gap. The microdynamics of such model are explained in terms of the foundations of Taylor-type staggered wage contracts. It is assumed that the monetary authority has some discretion regarding the timing of monetary actions. Here REIT can be used to set the optimal times for such actions, if the control of output is regarded as a secondary policy target. A simulation experiment illustrates the rationale of such a device for timing monetary measures. The REIT has been used by the Polish Monetary Policy Council since 2001 in it's inflation targeting and is thought to have contributed to a substantial decline in Polish inflation in 2003 and to an increase in output growth in 2004. A similar indicator computed for Russia as a means of monitoring monetary policy rather than as an active tool confirms that active expansionary policy in 2002 and 2003 might have contributed to Russian economic growth in 2004 and 2005, whereas similar policy measures for 2004 are likely to prove ineffective.

Forecasting the duration of short-term deflation episodes

The paper proposes a simulation‐based approach to multistep probabilistic forecasting, applied for predicting the probability and duration of negative inflation. The essence of this approach is in counting runs simulated from a multivariate distribution representing the probabilistic forecasts, which enters the negative inflation regime. The marginal distributions of forecasts are estimated using the series of past forecast errors, and the joint distribution is obtained by a multivariate copula approach. This technique is applied for estimating the probability of negative inflation in China and its expected duration, with the marginal distributions computed by fitting weighted skew‐normal and two‐piece normal distributions to autoregressive moving average ex post forecast errors and using the multivariate Student t copula

Guesstimation.

Macroeconomic model builders attempting to construct forecasting models frequently face constraints of data scarcity in terms of short time series of data, and also of parameter non-constancy and underspecification. Hence, a realistic alternative is often to guess rather than to estimate parameters of such models. This paper concentrates on repetitive guessing (drawing) parameters from iteratively changing distributions, with the straightforward objective function being that of minimisation of squares of ex-post prediction errors, weighted by penalty weights and subject to a learning process. The numerical Monte Carlo examples are those of a regression problem and a dynamic disequilibrium model