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.