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A CYCLING METHOD FOR CONSTRUCTING INPUT–OUTPUT TABLE TIME SERIES FROM INCOMPLETE DATA

There are a number of approaches for constructing time series of input–output tables. Some authors generate an initial estimate for a base year, and then serially estimate tables for subsequent years using the balanced prior-year table as an initial estimate. Others first generate a series of initial estimates for the entire period, and then balance tables in parallel. Current serial methods are affected by sudden leaps in the magnitude of table elements, which occur straight after a period of data unavailability. Current parallel methods require two complete tables for base and final years in the same classification, and therefore do not work under misaligned or incomplete data. We present a new method for constructing input–output table time series that overcomes these problems by averaging over alternate forward and backward sweeps across the time series period. We also solve the problem of hysteresis causing forecast and backcast table estimates to differ.     

Sectoral labour market effects of the 2006 FIFA World Cup

Using the case of the 2006 FIFA World Cup, this study is the first to test the employment effects of a mega-sporting event on the basis of data that combines both regional and sectoral data. It is also the first study of sporting events to use a semi-parametric test method. Earlier studies on the World Cup could hardly identify any employment effects. In contrast, we find a small but significant positive employment effect on the hospitality sector.     

Optimal Public Debts, Sustainable Deficits, and Budgetary Consolidation

It is hardly surprising that government budgets have always been widely debated inboth the political and academic arenas as public finances in general, and the budgetin particular, reflect the political and ideological colour of the government whichruns the state. No less inevitable are academic debates on budgetary issues giventhat economics is a multi-paradigmatic science. This background makes all the moremysterious the current complete consensus on the need for budgetary consolidationand the overwhelming acceptance of the `balanced budget' principle in politics as wellas in academic economics. In the paper, this position is questioned by producing a simple model of optimal public debt, sustainable deficits and optimal budgetary consolidation. Different possible trajectories of fiscal restriction and expansion – based on a Post-Keynesian and Rational Expectations paradigm alternatively – are then being empirically tested by comparing the German and British historical accounts of public finances over the past three decades.     

Evaluating FOMC forecast ranges: an interval data approach

The Federal Open Market Committee (FOMC) of the U.S. Federal Reserve publishes the range of members’ forecasts for key macroeconomic variables, but not the distribution of forecasts within this range. To evaluate these projections, previous papers compare the midpoint of the range with the realized outcome. This paper proposes an alternative approach to forecast evaluation that takes account of the interval nature of projections. It is shown that using the conventional Mincer–Zarnowitz approach to evaluate FOMC forecasts misses important information contained in the width of the forecast interval. This additional information plays a minor role at short forecast horizons but turns out to be of sometimes crucial importance for longer-horizon forecasts. For 18-month-ahead forecasts, the variation of members’ projections contains information that is more relevant for explaining future inflation than information embodied in the midpoint. Likewise, when longer-range forecasts for real GDP growth and the unemployment rate are considered, the width of the forecast interval comprises information over and above the one given by the midpoint alone.     

Testing structural equation models: the impact of error variances in the data generating process

Yet another paper on fit measures? To our knowledge, very few papers discuss how fit measures are affected by error variance in the Data Generating Process (DGP). The present paper deals with this. Based upon an extensive simulation study, this paper shows that the effects of increased error variance differ significantly for various fit measures. In addition to error variance the effects depend on sample size and severity of misspecification. The findings confirm the general notion that good fit as measured by the chi-square, RMSEA and GFI etc. does not necessarily mean that the model is correctly specified and reliable. One finding is that the chi square test may give support to misspecified models in situations with a high level of error variance in the DGP, for small sample sizes. Another finding is that the chi-square test looses power also for large sample sizes when the model is negligible misspecified. Other results include incremental fit indices as NFI and RFI which prove to be more informative indicators under these circumstances. At the end of the paper we formulate some guidelines for use of different fit measures.