Alessandra Canepa

Comparative international diffusion: Patterns, determinants and policies

Using data derived from a number of stand alone surveys in different countries, this paper explores international differences in the paths of diffusion of new manufacturing technologies. It is observed that diffusion paths are technology specific and that no one country either in Europe or North America can be said to exhibit faster, earlier or more extensive diffusion for all technologies than other countries. Using results in the public domain it is also argued that the main driving forces behind diffusion patterns are those generally classified as epidemic and rank effects. Building upon this, the rationale for policy intervention in the diffusion process is discussed and the effectiveness of different policy instruments considered.

FINANCIAL CONSTRAINTS ON INNOVATION: A EUROPEAN CROSS COUNTRY STUDY¤

In this paper we use responses to the second Community Innovation Survey (CIS II) to investigate the relative importance of finance as a constraint to innovation in Europe and also explore differences across industries, countries and firm sizes in the importance of any finance constraints. The findings are related to theoretical propositions and existing empirical literature. It is found that financial constraints are of more importance than other internal and external factors in terms of impacts on projects not starting, being delayed or postponed. It also shown that in market based systems finance is more of a constraint than in bank based systems, and that riskier, newer industries are more constrained by finance. The results on the importance of firm size are less conclusive.

Financing Constraints in the Inter Firm Diffusion of New Process Technologies

This paper explores why finance constraints may impact upon the inter firm diffusion of new technology, incorporates these arguments in a hazard rate formulation of a diffusion model and then estimates that model using data relating to the adoption of CNC machine tools in the UK. The results indicate that financial constraints can be a significant factor in the diffusion process.

Improvement of the quasi-likelihood ratio test in ARMA models: some results for bootstrap methods

Quasi-likelihood ratio tests for autoregressive moving-average (ARMA) models are examined. The ARMA models are stationary and invertible with white-noise terms that are not restricted to be normally distributed. The white-noise terms are instead subject to the weaker assumption that they are independently and identically distributed with an unspecified distribution. Bootstrap methods are used to improve control of the finite sample significance levels. The bootstrap is used in two ways: first, to approximate a Bartlett-type correction; and second, to estimate the p-value of the observed test statistic. Some simulation evidence is provided. The bootstrap p-value test emerges as the best performer in terms of controlling significance levels.

Dynamic Asymmetries in House Price Cycles: A Generalized Smooth Transition Model

In this paper we propose a novel nonlinear model to capture asymmetries in real estate cycles. The approach involves a particular parametrization of the transition function used in the transition equation of a smooth transition autoregressive model which improves the fit in the non-central probability region. The dynamic symmetry in house price cycles is strongly rejected for the housing markets taken into consideration. Further, our results show that the proposed model performs well in a out of sample forecasting exercise.

Testing in VECMs with Small Samples

The inferential procedures discussed in previous chapters are based on asymptotic considerations in the sense that they rely on the convergence of the distribution of the test statistics to some known limit distribution as the sample size goes to infinity. However, in order to work well, first-order asymptotic approximation requires that the asymptotic distribution is an accurate approximation to the finite sample distribution. When dealing with cointegrated VAR models, this is not generally the case. In this chapter we investigate the performance of various small sample inference procedures for cointegrated vector autoregressive models. Special attention is given to the Bartlett(1937) and the bootstrap Bartlett adjustment for the likelihood ratio test. The bootstrap p-value test and an F-type test are also considered. Throughout the chapter performance is assessed in terms of the empirical sizes and power properties of the inference procedures under consideration. An empirical application is also provided to illustrate the use of these procedures with real data. The analysis should provide some guidance to practitioners in doubt about which inference procedure to use when dealing with cointegrated VAR models.

Structure and Evaluation

In this chapter, we consider the question of long-run exogeneity and the related issue of identification. In our opinion, detection of the exogenous variables in the long run or the short run is a precursor to any attempt to structurally identify economic or financial phenomena.

Heteroscedasticity and Multivariate Volatility

In this chapter we develop a number of approaches to handle cointegration under variance that changes over the sample, in the first instance caused by volatility and or time varying heteroscedasticity. There is some debate in the literature about the influence of volatility on the Johansen estimator, but it is quite clear that the distribution towards which the conventional Johansen test tends is different under volatility (Cavaliere and Taylor 2008). And, although it is suggested that the test is asymptotically invariant to the presence of volatility, in the presence of volatility the tests on the cointegrating relations might suffer from significant size distortion in relatively large samples (Cavaliere and Taylor 2008), while Rahbek et al. (2002) are often construed to be suggesting that the impact of volatility on the Johansen estimator and test is innocuous. However, as the volatility becomes persistent, this may not be the case. More specifically the rate at which the test converges to the asymptotic distribution is sensitive to the spectral radius of the ARCH/GARCH polynomial matrices or the dimension of the largest eigenvalue. It has been observed that for a spectral radius in excess of 0.85 the simulated series exhibit quite extreme behaviour and for these types of numbers Rahbek et al. (2002) suggest that inference might only be appropriate when the sample lies in the range 600–1000. Furthermore, inference on the long-run parameters and loadings may also be affected by both inefficiency and inaccurate computation of the conventional Johansen estimator. It has also been shown by Seo (2007) that there can be significant distortion in the estimates of the long-run parameters caused by time varying variance structures.

Multivariate Time Series

In this chapter vector time series models are considered for stationary processes. There is a brief discussion of stationarity, but we leave the reader to refer for further detail to Patterson (2010) and (2011). The models are decomposed into VAR, VMA and mixed models with both characteristics (VARMA). The conventional classical assumptions will be considered and related to the likelihood function and the regression and maximum likelihood estimators. The notion of cointegration is not considered in this chapter, but the reparameterization into error correction form related to persistence is. Often these models are analysed in terms of the causal structure, and it is also possible to consider parameter stability and the related subject of exogeneity. The latter topics will be handled in more detail in Chap. 5 when long-run behaviour and short-run behaviour are considered. VAR in particular is often seen as a tractable reduced form (RF) of a rational expectation model (Sims 1980). In association with VAR, macroeconomic theory is linked to the behaviour of the impulse response function. Here the uniqueness of the impulse response function and the various approaches to handle it are considered. Time series models are also useful for forecasting, which has been a key rationale for their construction. Forecasting stationary VAR and VARMA processes is relatively straightforward when the series are all stationary. However, the problem is open to significant debate when non-stationarity and cointegration are considered; this is considered further in Chap. 5. There is also a more detailed treatment by Clements (2005), while the interested reader is directed to Clements and Hendry (2011).

Models with Alternative Orders of Integration

In this chapter three further topics are considered in some detail: models where the orders of integration of the series are not the same, estimation of models with I(2) variables, and models where the order of integration is fractional.