We examine new mechanisms that introduce environmentally friendly eco-changes involving the elimination of noxious commodities and take into account the structure of demand without a detrimental effect to agents' position. In the era of the fourth industrial revolution, these mechanisms allow eliminating unnecessary services or goods that are being replaced by modern technologies. We define optimal mechanisms under the criterion of distance minimization, when a small number of detrimental commodities is excluded from production processes as well as when producers are change-averse. The results have the form of theorems with rigorous proofs.

This paper proposes a microfounded model featuring frictional labor markets that generates procyclical R&D expenditures as a result of optimizing behavior by heterogeneous monopolistically competitive firms. This allows to show that business cycle fluctuations affect the aggregate endogenous growth rate of the economy. Consequently, transitory shocks leave lasting level effects. This mechanism is responsible for economically significant hysteresis effects that significantly increase the welfare cost of business cycles relative to the exogenous growth model. I show that this has serious policy implications and creates ample space for policy intervention. I find that several static and countercyclical subsidy schemes are welfare improving. Importantly, I find that due to labor market frictions subsidizing incumbent firms generates large and positive welfare effects.

We analysed the empirical importance of the capital flows in processes of economic convergence of the CEE region. We depart from reference net measures of capital flow reflecting the level of development of the financial system and focus on gross capital flow. Our econometric model is based on Seemingly Unrelated Regression Equation (SURE) elaborated by Arnold Zellner. This environment seems an alternative to standard panel regression, because it enables cross-country heterogeneity of parameters of interest (like pace of convergence). We tested several restrictions of the unconstrained SURE model, leading to simpler specifications that would allow for regional homogeneity of the role of a particular factor (like capital flows) in growth fluctuations and β-type convergence.

The presence of a binary variable in the cointegrated VAR (CVAR) model is most often interpreted as the structural break affecting the data generating process. It is proved in the paper that to enjoy this interpretation the binary variable must appear simultaneously inside and outside the cointegration space. In order to test for the break we advocate to employ the Wald statistic, however, its critical values and the power had to be simulated separately for the possible change of the constant, the trend, and both. The experiments were designed for different sizes of the cointegrating space, number of variables, the span of the break, normally and t-distributed errors. It is shown that the power of the test depends mostly on the magnitude of the break and the sample size while other factors are of secondary importance. In order to test for the break at unknown period the supWald statistic was proposed.

The United Nations General Assembly established the Sustainable Development Goals in 2015 to achieve an equitable and sustainable future for all by 2030. This study aims to model the relationship between government revenue per capita, quality of governance and the targets of several of these goals, including the coverage of the critical determinants of health; water, sanitation, healthcare, and education. We used government revenue because the policies and practices of international and multinational organisations - including corporations and banks - are more likely to influence revenue rather than government spending in countries in which they are engaged. Also, government revenue reflects a government's ability to spend across all sectors rather than just health or education. An unbalanced non-linear panel data model was employed, and annual data on 217 countries over the period 1960-2000 was used. The coverage of the Sustainable Development Goal variables was expressed as percentages and measures of the quality of governance included in the model. A linear relationship between revenue and the determinants of health would not be appropriate; therefore, we employ a logistic function. A standard panel logistic function would impose the same shape “S” curve on all countries, which is inappropriate. Therefore, we augment the parameters of the logistic function with measures of the quality of governance in each country, which allows each country to have a different “S” shape as the quality of its governance varies. Our study found that increased government revenue is associated with increased progress towards the Sustainable Development Goals. An improvement in the quality of governance could amplify this effect. This modelling and its accompanying visualisations can predict the potential of an increase in government revenue in an individual country regarding progress towards the Sustainable Development Goals.

This paper investigates the linkages between economic growth and fiscal policy under perfect capital mobility. The model incorporates wide range of fiscal policy instruments: the budget deficit, the structure of public debt, public expenditures on education, public consumption, and four tax rates. We prove that two tax rates - on consumption and interest on government bonds held by domestic lenders - are neutral for economic growth: both for the balanced growth path (BGP), and for transitory dynamics. All other parameters of fiscal policy are not neutral. Theoretical results are illustrated with an empirical analysis for Poland based on post-global financial crisis data for the Polish economy (2009-2018). Numerical simulations show that if fiscal policy remains unchanged, Polish economy will converge to the BGP with GDP growing at 2.3%. The best way to accelerate growth is to increase public investment in education. The other budgetary policy instruments are less effective in shaping economic growth.

We consider a monetary DSGE model featuring a borrowing constraint such that the amount of debt cannot be larger than a fraction - the debt-to-income (DTI) limit - of borrowers' labor income and the DTI limit is endogenous. The coexistence of financial amplification mechanisms warranted by this model provides a role for a specific macroprudential tool: a countercyclical DTI limit. Conditional on the pre-crisis sample and in a more recent out-of-sample period, our ex-post normative analysis shows that when this policy is implemented the cooperation between central bank and macroprudential authority in pursuing the “two instruments for two goals” strategy delivers an efficient performance in terms of macroeconomic stabilization, significantly outperforming the central bank's policy of “leaning against the wind”. This implies that a central bank should only be focused on its standard objectives (inflation and output stabilization) while financial stability be monitored by a macroprudential authority.

The purpose of this paper is to compare two approaches applied in property valuation: artificial neural networks and spatial regression. Despite the fact that artificial neural networks are often the first choice for modeling in the big data era, spatial econometrics methods offer incorporation of information on dependences between multiple objects in the studied space. Although this dependency structure can be incorporated into artificial neural network via feature engineering, this study is focused on abilities of reproducing it with machine learning method from crude coordinate data. The research is based on the database of 18,166 property sale transactions in Warsaw, Poland. According to this study, such volume of data does not allow artificial neural networks to compete in reflecting spatial dependence structure with spatial regression models.

The studies using Mincer equations are generally applied to cross-sectional data at the micro-level. There are however limited studies conducted with macro or panel data for wage equations. Pseudo panel data methods can be applied to empirical studies by creating cohorts from repeated cross-sectional data in the absence of genuine panel data. Difference in both the human and labour resources according to the spatial positions may also affect the prediction of the wage equations. We aim to introduce the application of spatial pseudo panel models by creating cohorts according to the birth years of employees and regions in which they live from the Turkish household labour survey for the period 2010-2015. As a result, we find that the spatial autocorrelation model is appropriate for wage equations of Turkey. We also find that return of education on wages is 11% while return of experience on wages is 4%.

We develop and study in detail a new family of distributions called Half-logistic Odd Power Generalized Weibull-G (HLOPGW-G) distribution, which is a linear combination of the exponentiated-G family of distributions. From the special cases considered, the model can fit heavy tailed data and has non-monotonic hazard rate functions. We further assess and demonstrate the performance of this family of distributions via simulation experiments. Real data examples are given to demonstrate the applicability of the proposed model compared to several other existing models.

In this paper we consider a class of nonlinear autoregressive models in which a specific type of dependence structure between the error term and the lagged values of the state variable is assumed. We show that there exists an equivalent representation given by a p-th order state-dependent autoregressive (SDAR(p)) model where the error term is independent of the last p lagged values of the state variable (y_{t-1},…,y_{t-p}) and the autoregressive coefficients are specific functions of them. We discuss a quasi-maximum likelihood estimator of the model parameters and we prove its consistency and asymptotic normality. To test the forecasting ability of the SDAR(p) model, we propose an empirical application to the quarterly Japan GDP growth rate which is a time series characterized by a level-increment dependence. A comparative analyses is conducted taking into consideration some alternative and competitive models for nonlinear time series such as SETAR and AR-GARCH models.

In this article we have described a multiproduct model of economical dynamics of Gale type, in which the changes in production technology (the dynamics of Gale type production spaces) depend upon the scale of targeted investments. Under such assumptions we have proved a so-called “weak” version of a multilane turnpike theorem in the Gale type economy with varying technology which converges to a certain limit technology. It states that in the long periods of time, regardless of the initial state of the economy, the optimal growth processes almost always lie close to the family of steady growth paths with maximum growth rate called the multilane turnpike.

Small sample properties of unrestricted and restricted canonical correlation estimators of cointegrating vectors for panel vector autoregressive process are considered when the cross-sectional dependencies occur in the process generating nonstationary panel data. It is shown that the unrestricted Box-Tiao estimator is slightly outperformed by the unrestricted Johansen estimator if the dynamic properties of the underlying process are correctly specified. The comparison of performance of the restricted canonical correlation estimator of cointegrating vectors for the panel VAR and for the classical VAR applied independently for each cross-section reveals that the latter performs better in small samples when the cross-sectional dependence is limited to the error terms correlations, even though it is inefficient in the limit, but it falls short in comparison to the former when there are cross-sectional dependencies in the short-run dynamics and/or in the long-run adjustments.

This paper aims to investigate the impact of exogenous fiscal policies on the Indonesian main macroeconomic indicators and the implications on different institutions and sectors in the economy using the static Computable General Equilibrium (CGE) analysis. Three simulations are conducted in order to analyze the expansion of exogenous public spending. The results revealed that the increase of government expenditure on goods under the adjusted government deficit and balance of payment generates the highest improvement on Indonesian GDP but resulting an increase in government deficit. In contrast, under financing scheme of either lowering subsidy rates across activities or increasing the ad valorem tax rates would result in lower improvement on Indonesian GDP. This is because it directly escalates the cost of production and thus increases the prices of final goods purchased by the households which result in a fall in their real consumption and in turn eventually could lead to a decrease in national income.

A Bayesian stochastic volatility model with a leverage effect, normal errors and jump component with the double exponential distribution of a jump value is proposed. The ready to use Gibbs sampler is presented, which enables one to conduct statistical inference. In the empirical study, the SVLEDEJ model is applied to model logarithmic growth rates of one month forward gas prices. The results reveal an important role of both jump and stochastic volatility components.

The first so-called hybrid MSV-MGARCH models were characterized by the conditional covariance matrix that was a product of a univariate latent process and a matrix with a simple MGARCH structure (Engle’s DCC or scalar BEKK). The aim was to parsimoniously describe volatility of a large group of assets. The proposed hybrid models, similarly as pure MSV specifications (and other models based on latent processes), required the Bayesian approach equipped with efficient MCMC simulation tools. The numerical effort has payed – the hybrid models seem particularly useful due to their good fit and ability to jointly cope with large portfolios. In particular, the simplest hybrid, now called the MSF-SBEKK model, has been successfully used in many applications. However, one latent process may be insufficient in the case of a highly heterogeneous portfolio. Thus, in this study we discuss a general hybrid MSV-MGARCH model structure, showing its basic characteristics that explain greater flexibility of such hybrid structure with respect to the corresponding MGARCH class. From the empirical perspective, we advocate the GMSF-SBEKK specification, which uses as many latent processes as there are relatively homogeneous groups of assets. We present full Bayesian inference for such models, with the use of an efficient MCMC simulation strategy. The approach is used to jointly model volatility on very different markets. Joint modelling is formally compared to individual modelling of volatility on each market.

We apply Bayesian inference to estimate transformation matrix that converts vector of industry outputs from NACE Rev. 1.1 to NACE Rev. 2 classification. In formal terms, the studied issue is a representative of the class of matrix balancing (updating, disaggregation) problems, often arising in the field of multi-sector economic modelling. These problems are characterised by availability of only partial, limited data and a strong role for prior assumptions, and are typically solved using bi-proportional balancing or cross-entropy minimisation methods. Building on Bayesian highest posterior density formulation for a similarly structured case, we extend the model with specification of prior information based on Dirichlet distribution, as well as employ MCMC sampling. The model features a specific likelihood, representing accounting restrictions in the form of an underdetermined system of equations. The primary contribution, compared to the alternative, widespread approaches, is in providing a clear account of uncertainty.

We propose a method of constructing multisector-multiregion input-output tables, based on the standard multisector tables and the tools of spatial econometrics. Voivodship-level (NUTS-2) and subregion-level data (NUTS-3) on sectoral value added is used to fit a spatial model, based on a modification of the Durbin model. The structural coefficients are calibrated, based on I-O multipliers, while the spatial weight matrices are estimated as parsimoniously parametrised functions of physical distance and limited supply in certain regions. We incorporate additional restrictions to derive proportions in which every cross-sectoral flow should be interpolated into cross-regional flow matrix. All calculations are based on publicly available data. The method is illustrated with an example of regional economic impact assessment for a generic construction company located in Eastern Poland.

This paper describes an analysis of the effects of both foreign exchange (FX) risk and interest rate risk on installments of the housing FX loan using classic comparative statics approach. By focusing on sensitivity of annuity with respect to infinitesimal changes of parameters it presents the impact of the interest rate and FX rate on installments in terms of their shares of the total outstanding in foreign currency, and illustrates using values, in Polish zlotys, for three example loans extended during the period when Poland saw its most intensive FX lending. This analysis represents an attempt to answer a question frequently raised in this country of late: does the issue of debt servicing housing FX loans matter for borrowers and therefore could affect banks’ loan portfolio quality?

Bayesian VAR (BVAR) models offer a practical solution to the parameter proliferation concerns as they allow to introduce a priori information on seasonality and persistence of inflation in a multivariate framework. We investigate alternative prior specifications in the case of time series with a clear seasonal pattern. In the empirical part we forecast the monthly headline inflation in the Polish economy over the period 2011‒2014 employing two popular BVAR frameworks: a steady-state reduced-form BVAR and just-identified structural BVAR model. To evaluate the forecast performance we use the pseudo real-time vintages of timely information from consumer and financial markets. We compare different models in terms of both point and density forecasts. Using formal testing procedure for density-based scores we provide the empirical evidence of superiority of the steady-state BVAR specifications with tight seasonal priors.