The Relationship between Growth and Volatility under Alternative Shocks

This paper presents a simple stochastic endogenous growth model with multiple shocks � a preference shock and a learning shock. The model is used to predict alternative relationships between growth and volatility on the basis of the underlying impulse source of fluctuations.

Monetary Stabilisation Policy and Long-run Growth

This paper presents a stochastic monetary growth model with nominal rigidities and active monetary policy in which technological change contains both deliberate (internal) and serendipitous (external) learning mechanisms. The model is used to describe how the implications of monetary stabilization policy for the long-run economic performance could change due to the ambiguity on the relationship between secular growth and cyclical volatilitygrowth, cyclies, money, stabilisation policy

Uncertainty, learning and growth

The paper extends Blackburn and Galindev (2003)' s stochastic growth model in which productivity growth entails both external and internal learning behaviour with a Constant Relative Risk Aversion utility function and productivity shocks. Consequently, the relationship between long-term growth and short-term volatility depends not only on the relative importance of each learning mechanism but also on a parameter measuring individuals' attitude towards risk.Growth; Uncertainty, Learning

On the Effect of Monetary Stabilisation Policy on Long-run Growth (Revised September 2005)

This paper presents a stochastic monetary growth model with nominal rigidities and active monetary policy in which technological change contains both deliberate (internal) and serendipitous (external) learning mechanisms. The model is used to describe how the implications of monetary stabilization policy for the long-run economic performance could change due to the ambiguity on the relationship between secular growth and cyclical volatility.

Discretization of highly persistent correlated AR(1) shocks

The finite state Markov-Chain approximation method developed by Tauchen (1986) and Tauchen and Hussey (1991) is widely used in economics, finance and econometrics in solving for functional equations where state variables follow an autoregressive process. For highly persistent processes, the method requires a large number of discrete values for the state variables to produce close approximations which leads to an undesirable reduction in computational speed, especially in a multidimensional case. This paper proposes an alternative method of discretizing vector autoregressions. This method can be treated as an extension of Rouwenhorst's (1995) method which, according to our experiments, outperforms the existing methods in the scalar case for highly persistent processes. The new method works well as an approximation that is much more robust to the number of discrete values for a wide range of the parameter space.Finite State Markov-Chain Approximation; Discretization of Multivariate Autoregressive Processes; Transition Matrix; Numerical Methods; Value Function Iteration; the Rouwenhorst method; VAR

On the Effect of Monetary Stabilisation Policy on Long-run Growth

This paper presents a stochastic monetary growth model with nominal rigidities and active monetary policy in which technological change contains both deliberate (internal) and serendipitous (external) learning mechanisms. The model is used to describe how the implications of monetary stabilization policy for the long-run economic performance could change due to the ambiguity on the relationship between secular growth and cyclical volatility

The Relationship between Growth and Volatility under Alternative Shocks

This paper presents a simple stochastic endogenous growth model with multiple shocks a preference shock and a learning shock. The model is used to predict alternative relationships between growth and volatility on the basis of the underlying impulse source of fluctuations

Effect of nickel precursor and catalyst activation temperature on methanation performance

This work studied an effect of anionic precursor on the preparation of active and fine nickel metal catalysts for syngas methanation. Nickel catalysts were pr¬epared by impregnation-co-precipitation method. Nickel hydrate salts of Ni(NO3)2·6H2O, NiSO4·6H2O and NiCl2·6H2O were used as a metal catalyst precursor, and the obtained catalysts were named as Ni/Al (N), Ni/Al (S) and Ni/Al (Cl), respectively. Methanation synthesis of carbon monoxide was carried out in a fixed bed stainless reactor. Prior to experiment, the catalyst powder was pressed into tablets, then crushed and sieved to use 0.5-0.9 mm particles. Reactions were performed at the temperature of 350 °C in the pressure of 3 atm of H2:CO syngas (the molar ratio of 3:1) with the GHSV of 3000 h-1. In the present methanation conditions, the Ni/Al (N), Ni/Al (S) and Ni/Al (Cl) catalysts gave the CH4 selectivity of 93%, 18% and 91% (vol.), respectively. The XRD and ICP-OES analysis clarified that although the Ni/Al (S) catalyst contained a similar nickel amount of 17.4 wt % to other two catalysts, its metal distribution was poor. Also the low activity of the Ni/Al (S) catalyst was caused by the contamination of remained sulfur from sulfate precursor. This work also examined an influence of catalyst activation temperature pre-synthesis. The Ni/Al (N) catalyst was reduced by pure hydrogen gas at different temperatures of 350 ºС, 400 ºС or 450 ºС. The catalyst activated at 400 ºС produced the highest CH4 amount of 0.087 mmol·g-1cat for the duration of 1h methanation. An initial temperature of methane formation was the lowest for the Ni/Al (N) catalyst which was activated at 400 ºС among three catalysts

Discretization of highly persistent correlated AR(1) shocks

The finite state Markov-Chain approximation method developed by Tauchen (1986) and Tauchen and Hussey (1991) is widely used in economics, finance and econometrics in solving for functional equations where state variables follow an autoregressive process. For highly persistent processes, the method requires a large number of discrete values for the state variables to produce close approximations which leads to an undesirable reduction in computational speed, especially in a multidimensional case. This paper proposes an alternative method of discretizing vector autoregressions. This method can be treated as an extension of Rouwenhorst's (1995) method which, according to our experiments, outperforms the existing methods in the scalar case for highly persistent processes. The new method works well as an approximation that is much more robust to the number of discrete values for a wide range of the parameter space