Investigating first‐stage exchange rate pass‐through: Sectoral and macro evidence from euro area countries

In this paper, we evaluate the first‐stage pass‐through, namely the responsiveness of import prices to the exchange rate changes, for a sample of euro area (EA) countries. Our study aimed to shed further light on the role of microeconomic factors versus macroeconomic factors in influencing the extent of the exchange rate pass‐through (ERPT). As a first step, we conduct a sectoral analysis using disaggregated import prices data. We find a much higher degree of pass‐through for more homogeneous goods and commodities, such as oil and raw materials, than for highly differentiated manufactured products, such as machinery and transport equipment. Our results confirm that cross‐country differences in pass‐through rates may be due to divergences in the product composition of imports. The higher share of imports from sectors with lower degrees of pass‐through, the lower ERPT for an economy will be. In a next step, we investigate for the impact of some macroeconomics factors or common events experienced by EA members on the extent of pass‐through. Using the system generalised method of moments within a dynamic panel‐data model, our estimates indicate that decline of import‐price sensitivity to the exchange rate is not significant since the introduction of the single currency. Our findings suggest instead that the weakness of the euro during the first 3 years of the monetary union significantly raised the extent of the ERPT. This outcome could explain why the sensitivity of import prices has not fallen since 1999. We also point out a significant role played by the inflation in the Eurozone, as the responsiveness of import prices to exchange rate fluctuations tends to decline in a low and more stable inflation environment. Overall, our findings support the view that the extent of pass‐through is comprised of both macro‐ and microeconomic aspects that policymakers should take into account.     

Time series momentum and moving average trading rules

We compare and contrast time series momentum (TSMOM) and moving average (MA) trading rules so as to better understand the sources of their profitability. These rules are closely related; however, there are important differences. TSMOM signals occur at points that coincide with a MA direction change, whereas MA buy (sell) signals only require price to move above (below) a MA. Our empirical results show MA rules frequently give earlier signals leading to meaningful return gains. Both rules perform best outside of large stock series which may explain the puzzle of their popularity with investors, yet lack of supportive evidence in academic studies.     

Saddlepoint approximations to the mean and variance of the extended hypergeometric distribution

Conditional inference on 2 x 2 tables with fixed margins and unequal probabilities is based on the extended hypergeometric distribution. If the support of the distribution is large, exact calculation of the conditional mean and variance of the table entry may be computationally demanding. This paper proposes a single‐saddlepoint approximation to the mean and variance. While the approximation achieves acceptable accuracy for ordinary practical purposes, an alternative saddlepoint approximation is provided that gives much closer to exact results. It improves the accuracy of current approximations to up to more than four powers of ten.     

The post‐application labour market consequences of employment tribunal claims

This article examines the post‐application employment consequences for individuals registering complaints to Employment Tribunals following dismissal or redundancy. We consider several pieces of evidence: (a) the probability of finding another job, (b) the time taken to get a new job and (c) the pay/status of the new job. Our results indicate that age plays a significant role in aspects (a) and (c), while those who previously held managerial positions generally took longest to get a new job and found it most difficult to achieve a similar level of pay/status in their current jobs. Long‐term health problems/disability is associated with significantly worse outcomes on all three measures. Respondents whose cases were dismissed by tribunals without hearings fared worst in terms of obtaining a new job and the time it took to do so. There are, however, fewer differences by tribunal outcome in the relative pay/status of the claimant's current job.     

The integration of fuzzy sets and statistics: toward strict falsification in the social sciences

Whilst statistics take up a prominent place in the social science research toolkit, some old problems that have been associated there with have not been fully resolved. These problems include bias through the inclusion of irrelevant variation and the exclusion of relevant variation, which may lead to hidden and spurious correlations in more extreme—however not at all unthinkable—cases. These issues have been addressed by Ragin by building a case for the usage of fuzzy set theory in social science. In this paper, we take a complementary view, insofar as we incorporate fuzzy set theory in current statistical analyses. Apart from shedding new light on the main issues associated with (population based) statistics, this approach also offers interesting prospects for the falsification of theories—rather than single relations between variables—in the social sciences.     

THE AGEING POPULATION: CRUNCH TIME FOR GOVERNMENT REFORMS

Current discussions about reforms to health and long‐term care miss the most important trends that should be influencing debate. With an ageing population that includes a higher proportion of people with manageable illnesses, health and care costs will rise. Reforms must ensure that existing government spending is used more efficiently and that it harnesses – rather than works against – private financing.     

Estimating Transition Probabilities from a Time Series of Independent Cross Sections

This paper considers the implementation of a nonstationary, heterogeneous Markov model for the analysis of a binary dependent variable in a time series of independent cross sections. The model, previously considered by M offitt (1993), offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time-constant and time-varying covariates on the hazards. We show how ML estimates of the parameters can be obtained by Fisher's method-of-scoring and how to estimate both fixed and time-varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch women using data from the Socio-economic Panel (SEP) study conducted in the Netherlands between 1986 and 1995. We treat the panel data as independent cross sections and compare the employment status sequences predicted by the model with the observed sequences in the panel. Some open problems concerning the application of the model are also discussed.