This paper presents maximum score type estimators for linear, binomial, tobit and truncated regression models. These estimators estimate the normalized vector of slopes and do not provide the estimator of intercept, although it may appear in the model. Strong consistency is proved. In addition, in the case of truncated and tobit regression models, maximum score estimators allow restriction of the sample in order to make ordinary least squares method consistent.
Poland is expected to enter the Exchange Rate Mechanism II (ERM II). The European Central Bank recommends that the ERM II central rate should reflect the best possible assessment of the equilibrium exchange rate. Since the equilibrium rate is changing in time, it is important to identify the pushing and pulling forces of the exchange rate. This knowledge will let the authorities to defend only the exchange rate that is in equilibrium and to assess outcomes of their actions. We use the VEC approach of Johansen to estimate the behavioral equilibrium exchange rate and to identify the pushing forces of the Polish zloty/euro rate. We apply the Gonzalo-Granger decomposition to calculate the permanent equilibrium exchange rate and to identify the pulling forces of the zloty exchange rate. We demonstrate that this approach may be useful for Polish authorities while entering the ERM II as well as within that mechanism.
In this paper we present the Bayesian model selection procedure within the class of cointegrated processes. In order to make inference about the cointegration space we use the class of Matrix Angular Central Gaussian distributions. To carry out posterior simulations we use an alorithm based on the collapsed Gibbs sampler. The presented methods are applied to the analysis of the price – wage mechanism in the Polish economy.
In this paper we show that in the lognormal discrete-time stochastic volatility model with predictable conditional expected returns, the conditional expected value of the discounted payoff of a European call option is infinite. Our empirical illustration shows that the characteristics of the predictive distributions of the discounted payoffs, obtained using Monte Carlo methods, do not indicate directly that the expected discounted payoffs are infinite.
The primary goal of the study is to diagnose satisfaction and loyalty drivers in Polish retail banking sector. The problem is approached with Customer Satisfaction Index (CSI) models, which were developed for national satisfaction studies in the United States and European countries. These are multiequation path models with latent variables. The data come from a survey on Poles’ usage and attitude towards retail banks, conducted quarterly on a representative sample. The model used in the study is a compromise between author’s synthesis of national CSI models and the data constraints.
There are two approaches to the estimation of the CSI models: Partial Least Squares – used in national satisfaction studies and Covariance Based Methods (SEM, Lisrel). A discussion is held on which of those two methods is better and in what circumstances. In this study both methods are used. Comparison of their performance is the secondary goal of the study.
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