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Featured researches published by Troy D. Matheson.


Journal of Economic Surveys | 2010

RBCs and DSGEs: The Computational Approach to Business Cycle Theory and Evidence

Ozer Karagedikli; Troy D. Matheson; Christie Smith; Shaun P. Vahey

Real business cycle (RBC) and dynamic stochastic general equilibrium (DSGE) methods have become essential components of the macroeconomists toolkit. This literature review stresses recently developed techniques for computation and inference, providing a supplement to the Romer textbook, which stresses theoretical issues. Many computational aspects are illustrated with reference to the simple divisible labor RBC model. Code and US data to replicate the computations are provided on the internet, together with a number of appendices providing background details.


International Review of Applied Economics | 2007

Convergence in Productivity Across Industries: Some Results for New Zealand and Australia

Troy D. Matheson; Les Oxley

Abstract New Zealand shares a wealth of common interests and experiences with Australia. This has tempted some to assume that these economies form an ‘Economic Club’, in which one would expect to identify common aggregate trends and growth experiences. In this paper we present results that test, and generally reject, convergence in labour productivity across Australia and New Zealand, using both aggregate and disaggregate, industry‐level data. We find that only two industries satisfy our definition of Conditional Convergence (Agriculture, Forestry and Fishing and Cultural and Recreational Services), and that the Mining and Wholesale Trade industries have particularly important roles to play in explaining the measured divergence. Cointegration‐based tests reveal more stochastic trends governing Australian productivity than in New Zealand. The evidence suggests, therefore, that the underlying growth processes of the two economies are fundamentally different, thereby questioning the relevance of aggregate comparisons between them. New evidence using industry‐level data does not, therefore, resolve the aggregate‐level ‘non‐convergence puzzle’ identified here, and elsewhere.


Applied Economics Letters | 2006

A more flexible decomposition of productivity growth

Troy D. Matheson

Conventional productivity growth decompositions, such as those of Baily, Bartelsman and Haltiwanger (2001) and Grilliches and Regev (1995), first aggregate each firms productivity level into an aggregate productivity index, and then allocate aggregate growth back to the firms forming the aggregate. It is shown that this can produce misleading results, and two more flexible decompositions are proposed that are consistent with the superlative Törnqvist productivity growth index.


Economic Modelling | 2010

An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys

Troy D. Matheson


International Journal of Central Banking | 2006

Factor model forecasts for New Zealand

Troy D. Matheson


Empirical Economics | 2010

Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand

Chris Bloor; Troy D. Matheson


Archive | 2007

OPEN ECONOMY DSGE-VAR FORECASTING AND POLICY ANALYSIS: HEAD TO HEAD WITH THE RBNZ PUBLISHED FORECASTS

Kirdan Lees; Troy D. Matheson; Christie Smith


Journal of Macroeconomics | 2010

Assessing the fit of small open economy DSGEs

Troy D. Matheson


Economics Letters | 2008

Phillips curve forecasting in a small open economy

Troy D. Matheson


Journal of Forecasting | 2009

Nowcasting and predicting data revisions using panel survey data

Troy D. Matheson; James Mitchell; Brian Silverstone

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Kirdan Lees

Reserve Bank of New Zealand

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Christie Smith

Reserve Bank of New Zealand

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Les Oxley

University of Canterbury

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Ozer Karagedikli

Reserve Bank of New Zealand

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Christie Smith

Reserve Bank of New Zealand

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Shaun P. Vahey

Australian National University

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