This paper documents the evolution of variables central to understanding the creation of an Atlantic Economy in wheat between the US and the UK in the nineteenth century. The cointegrated VAR model is then applied to the period 1838-1913 in order to find long-run relationships between these variables. The main result is that explanations for the expansion of trade based on falling barriers to t...

In this analysis the interdependence between foreign exchange markets and stock markets for selected accession and cohesion countries is discussed. This includes basic theoretical approaches. Monthly data for the nominal stock market indices and nominal exchange rates are used, where Ireland, Portugal, Spain, Greece, Poland, Czech Republic, Slovenia, and Hungary are included in the analysis. Fr...

This paper studies U.S. in‡ation adjustment speed to aggregate technology shocks and to monetary policy shocks in a medium size Bayesian VAR model. According to the model estimated on the 1959-2007 sample, in‡ation adjusts much faster to aggregate technology shocks than to monetary policy shocks. These results are robust to di¤erent identi…cation assumptions and measures of aggregate prices. Ho...

This paper develops a framework for stress-testing the credit risk of Chinese commercial banks to macroeconomic shocks. Using data over the period 1985-2008, this study establishes a vector auto-regression (VAR) model to describe the links between default rate and macroeconomic factors, and then designs three stress scenarios to implement the stress testing by Monte Carlo simulation. As a resul...

This paper offers new insights into the processes of firm growth by applying a reduced-form vector autoregression (VAR) model to longitudinal panel data on French manufacturing firms. We observe the co-evolution of key variables such as growth of employment, sales, and gross operating surplus, as well as growth of multifactor productivity. It seems that employment growth is negatively associate...

Available online 19 September 2012 JEL classification: C72 D72 D74

We propose a unified machine learning model (UMLM) for two-class classification, regression and outlier (or novelty) detection via a robust optimization approach. The model embraces various machine learning models such as support vector machine-based and minimax probability machine-based classification and regression models. The unified framework makes it possible to compare and contrast existi...

We report classification experiments using the pilot Infant COPE database of neonatal facial expressions. Two sets of DCT coeffiecents were used to train a neural network simultaneous algorithm (NNSOA) and a linear support vector machine (LSVM) to classify neonatal expressions into the two categories of pain and nonpain. In the first set (VAR) only 80 of the coefficients with the highest varian...

This paper develops an estimated hybrid model that combines the micro-founded DSGE model with the flexibility of the theoretical VAR model. The model is estimated via the maximum likelihood technique based on quarterly data on real Gross National Product (GNP), consumption, investment and hours worked, for the South African economy, over the period of 1970:1 to 2000:4. Based on a recursive esti...

The purpose of this paper is to demonstrate that the success of the Litterman prior in VAR forecasting is not due to the realism of the prior, but rather because the prior conveniently reduces forecast error variance in common cases of misspecification. Specifically, it is shown that the imposition of a random walk prior reduces forecast error variance in misspecifications involving (1) time-va...