### International publications

#### Testing for co-nonlinearity (2014)

posted Nov 10, 2014, 2:36 AM by Håvard Hungnes   [ updated Nov 10, 2014, 2:42 AM ]

 Studies in Nonlinear Dynamics & Econometrics, DOI: 10.1515/snde-2013-0092 Abstract: This article introduces the concept of co-nonlinearity. Co-nonlinearity is an example of a common feature in time series [Engle, Robert F., and Sharon Kozicki. 1993. “Testing for Common Features.” Journal of Business & Economic Statistics 11 (4): 369–380] and an extension of the concept of common nonlinear components [Anderson, Heather M., and Farshid Vahid. 1998. “Testing Multiple Equation Systems for Common Nonlinear Components.” Journal of Econometrics 84 (1): 1–36]. If some time series follow a nonlinear process but where a linear relationship between the levels of these series removes the nonlinearity, such a relationship is defined as co-nonlinear. In this article I show how to determine the number of such co-nonlinear relationships. Furthermore, I show how to formulate hypothesis tests on the co-nonlinear relationships in a full maximum likelihood framework. The framework for identifying co-nonlinear relationships is illustrated in a system of Norwegian interest rates.

#### Identifying Structural Breaks in Cointegrated Vector Autoregressive Models (2010)

posted Apr 23, 2010, 5:54 AM by Håvard Hungnes   [ updated Jun 16, 2010, 7:49 AM ]

 Oxford Bulletin of Economics and Statistics 72(4):551-565. DOI: 10.1111/j.1468-0084.2010.00586.x Abstract: This article suggests an alternative formulation of the cointegrated vector autoregressive (VAR) model such that the coefficients for the deterministic terms have straightforward interpretations. These coefficients can be interpreted as growth rates and cointegration mean level coefficients and express long-run properties of the model. For example, the growth rate coefficients tell us how much to expect (unconditionally) the variables in the system to grow from one period to the next, representing the underlying (steady state) growth in the variables. The estimation of the proposed formulation is made operationally in GRaM, which is a program for Ox Professional. GRaM can be used for analysing structural breaks when the deterministic terms include shift dummies and broken trends. By applying a formulation with interpretable deterministic components, different types of structural breaks can be identified. Shifts in both intercepts and growth rates, or combinations of these, can be tested for. The ability to distinguish between different types of structural breaks makes the procedure superior compared with alternative procedures. Furthermore, the procedure utilizes the information more efficiently than alternative procedures. Finally, interpretable coefficients of different types of structural breaks can be identified.

#### A demand system for input factors when there are technological changes in production (2011)

posted Apr 23, 2010, 5:47 AM by Håvard Hungnes   [ updated Apr 1, 2011, 4:26 AM ]

 Empirical Economics (2011) 40-581-600. DOI: 10.1007/s00181-010-6-0346-y Abstract: In a system with n input factors there are n − 1 independent cost shares. An often-used approach in estimating factor demand systems is to (implicitly or explicitly) assume that there is a (independent) cointegrating relationship for each of the n − 1 independent cost shares. However, due to technological changes, there might not be as many cointegrating relationships as there are (independent) cost shares. This article presents a flexible demand system that allows for both factor neutral technological changes as well as technological changes that affect the relative use of the different factors. The empirical tests indicate that there are fewer cointegrating relationships than usually implied using conventional estimation approaches. This result is consistent with technological changes that affect the relative use of the different input factors. I argue that, since such unexplained technological changes are likely to affect input factor decisions, a demand system that allows for such changes should be preferred.

#### The Commodity Currency Puzzle (2008)

posted Apr 23, 2010, 5:26 AM by Håvard Hungnes   [ updated Mar 25, 2011, 6:49 AM ]

 The IUP Journal of Monetary Economics 6.2: 7-30. http://www.iupindia.in/508/ijmoe.asp (Joint work with Hilde C. Bjørnland.) Abstract: This paper addresses the purchasing power parity (PPP) puzzle for commodity currencies. In particular, we analyze the real exchange rate behavior in Norway, which has a primary commodity (oil) that constitutes the majority of its exports. A substantial part of the literature on commodity currencies has found that, despite controlling for theeffect of commodity prices, PPP does not hold in the long run. We show that once we also control for the effect of the interest rate differential in the real exchange rate relationship, the deviations from PPP are fully accounted for. Furthermore, with the interest rate differential included in the long run real exchange rate relationship, the real oil price plays a minor role. Adjustment to equilibrium (half-lives) is also substantially reduced, taking no more than one year on average. Hence, contrary to earlier findings on commodity currencies, this paper has effectively dealt with the PPP puzzle.

#### The importance of interest rates for forecasting the exchange rate (2006)

posted Apr 8, 2010, 6:34 AM by Håvard Hungnes   [ updated Apr 28, 2010, 5:39 AM ]

 Journal of Forecasting 25.3: 209-221. DOI: 10.1002/for.983. (Joint work with Hilde C. Bjørnland.)   Abstract: This study compares the forecasting performance of a structural exchange rate model that combines the purchasing power parity condition with the interest rate differential in the long run, with some alternative exchange rate models. The analysis is applied to the Norwegian exchange rate. The long-run equilibrium relationship is embedded in a parsimonious representation for the exchange rate. The structural exchange rate representation is stable over the sample and outperforms a random walk in an out-of-sample forecasting exercise at one to four horizons. Ignoring the interest rate differential in the long run, however, the structural model no longer outperforms a random walk.

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