Grzegorz Koloch

Monetary Policy in a Non-Representative Agent Economy: A Survey

It is well known that central bank policies affect not only macroeconomic aggregates, but also their distribution across economic agents. Similarly, a number of papers demonstrated that heterogeneity of agents may matter for the transmission of monetary policy to macro variables. Despite this, the mainstream monetary economics literature has so far been dominated by dynamic stochastic general equilibrium models with representative agents. This paper aims to tilt this imbalance towards heterogeneous agents setups by surveying the main positive and normative findings of this line of the literature, and suggesting areas in which these models could be implemented. In particular, we review studies that analyse the heterogeneity of (i) households’ income, (ii) households’ preferences, (iii) consumers’ age, (iv) expectations and (v) firms’ productivity and financial position. We highlight the results on issues that, by construction, cannot be investigated in a representative agent framework and discuss important papers modifying the findings from the representative agent literature.

Shocks and rigidities as determinants of CEE labour markets’ performance

In this article, the impact of real wage, productivity, labour demand and supply shocks on eight Central and Eastern European (CEE) economies from 1996–2007 is analysed with a panel structural vector error correction model. A set of long‐run restrictions derived from the dynamic stochastic general equilibrium (DSGE) model is used to identify structural shocks, and fluctuations in foreign demand are controlled for. We find that the propagation of shocks on CEE labour markets resembles that found for OECD countries. Labour demand shocks emerge as the main determinant of employment and unemployment variability in the short‐to‐medium run, but wage rigidities were equally important for observed labour market performance, especially in Poland, Czech Republic and Lithuania. We associate these rigidities with collective bargaining, minimum wage, active labour market policies and employment protection legislation.

Forecasting the Polish zloty with non-linear models

The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical economic models or the use of advanced econometric techniques can help explain future movements in exchange rates. The results of the empirical studies for major world currencies show that forecasts from a naive random walk tend to be comparable or even better than forecasts from more sophisticated models. In the case of the Polish zloty, the discussion in the literature on exchange rate forecasting is scarce. This article fills this gap by testing whether non-linear time series models are able to generate forecasts for the nominal exchange rate of the Polish zloty that are more accurate than forecasts from a random walk. Our results confirm the main findings from the literature, namely that it is difficult to outperform a naive random walk in exchange rate forecasting contest.

Output analysis for terminating simulations with partial observability

Abstract In this paper we propose methods of output analysis for terminating simulations when the researcher is unable to fully specify the initial state of the simulation using observations of the real system. We call this situation “partial observability” and argue that it is common in practice, especially in the case of complex agent-based simulations. We provide classification of situations where not all input parameters or simulation state variables are observable and for each case we propose a method of terminating simulation output analysis. In particular, we focus on the situation where a rare event needs to be analyzed, since it requires careful design of a simulation experiment in order to minimize the computational budget and avoid bias in the output predictions.

On elicitation of preferences from social networks through synthetic population modelling

Social network platforms are a useful source of information on preferences of citizens. However, population exposed on social network platform is non representative and in result preferences collected through such platforms are biased. The goal of the public administration is to utilize the data that can be collected through such online platforms in order to understand preferences and its structure in the society and hence better react to communitys needs. This situation calls for an algorithm that will allow to generalize information collected on social platform users on the entire population. We propose and evaluate a two-step methodology for testing of such algorithms: (1) synthetic population is generated and its sample is selected that represents social platform users and (2) regenerate the whole population on the basis of data from sub-population. In this way we can evaluate the quality of different algorithms aimed at preference elicitation from social platform data.

Penalty Rules in Multicriteria Genetic Search

In the paper, by means of numerical experiments conducted on artificially constructed problem instances, we test penalty rules for constrained genetic optimization of the Capacitated Heterogeneous Vehicle Routing Problem with Time-Windows in a bi-objective framework. Optimized criteria are cost minimization and capacity utilization maximization. Two approaches are employed – scalarization of objectives and dominance-based evaluation of solutions. We show that it is possible to handle infeasibility in such a way, that this risk of divergence to regions of infeasibility is acceptable. The most secure penalty rule among the tested ones turns out to be the rule which explicitly controls the proportion of infeasible solutions in the population. This rule, along with the rule which accounts only the notion of solutions distance from the feasible set, outperforms rules based on time-penalties and best to best-feasible solution comparison over considered case studies.

Vehicle Routing with Three‐dimensional Container Loading Constraints—Comparison of Nested and Joint Algorithms

In the paper we examine a modification of the classical Vehicle Routing Problem (VRP) in which shapes of transported cargo are accounted for. This problem, known as a three‐dimensional VRP with loading constraints (3D‐VRP), is appropriate when transported commodities are not perfectly divisible, but they have fixed and heterogeneous dimensions. In the paper restrictions on allowable cargo positionings are also considered. These restrictions are derived from business practice and they extended the baseline 3D‐VRP formulation as considered by Koloch and Kaminski (2010). In particular, we investigate how additional restrictions influence relative performance of two proposed optimization algorithms: the nested and the joint one. Performance of both methods is compared on artificial problems and on a big‐scale real life case study.