Impact of Systematic Sampling on Causality in the presence of Unit Roots

Quite contrary to the stationary case where systematic sampling preserves the direction of Granger causality, this paper shows that systematic sampling of integrated series may induce spurious causality, even if they are used in differenced form.Systematic Sampling, Causality, Unit Roots, Cross covariance

How an Export Boom affects Unemployment

Does trade affect the equilibrium rate of unemployment? To theoretically examine this question, we incorporate firm-union bargaining considerations into a model with a booming external sector and a stagnating manufacturing sector. In the model, a sustained improvement in the terms of trade lowers unemployment. To empirically investigate the predicted determinants of the unemployment rate, we use data for Australia, a country whose prosperity has always depended on the value of its exports. We find strong evidence that higher export prices, capital accumulation in tradeable goods industries and a lower unemployment benefit replacement rate each reduce the equilibrium unemployment rate.

Temporal Aggregation, Causality Distortions, and a Sign Rule

Temporally aggregated data is a bane for Granger causality tests. The same set of variables may lead to contradictory causality inferences at different levels of temporal aggregation. Obtaining temporally disaggregated data series is impractical in many situations. Since cointegration is invariant to temporal aggregation and implies Granger causality this paper proposes a sign rule to establish the direction of causality. Temporal aggregation leads to a distortion of the sign of the adjustment coefficients of an error correction model. The sign rule works better with highly temporally aggregated data. The practitioners, therefore, may revert to using annual data for Granger causality testing instead of looking for quarterly, monthly or weekly data. The method is illustrated through three applications.Granger causality test, cointegration, error correction model, adjustment coefficient, sign rule

The Rise (and Fall) of Labour Market Programmes: The Role of Global and Domestic Factors

We study the political economy of labour market policies. First, it is shown that tax and redistributive considerations lead inside workers to prefer spending on active labour market programmes to passive spending, e.g., on unemployment benefits. We also show that greater active spending may be a feature of globalising economies. In the empirical work, panel data for OECD countries are used to examine the relationship between active and passive labour market spending, various measures of globalisation and controls relevant for analysing the political economy of labour market policies. Overall, we find that factors other than globalisation are more important determinants of labour market expenditures.

Quarterly Real GDP Estimates for China and ASEAN4 with a Forecast Evaluation

The growing affluence of the East and Southeast Asian economies has come about through a substantial increase in their economic links with the rest of the world, the OECD economies in particular. Econometric studies that try to quantify these links face a severe shortage of high frequency time series data for China and the group of ASEAN4 (Indonesia, Malaysia, Philippines and Thailand). In this exercise we provide quarterly real GDP estimates for these countries derived by applying the Chow-Lin related series technique to annual real GDP series. The quality of the disaggregated series is evaluated through a number of indirect methods. Some potential problems of using readily available univariate disaggregation techniques are also highlighted.Univariate disaggregation, Chow-Lin procedure, first-difference method, growth-rate method, output linkages and forecast performance

The Distortionary Effects Of Temporal Aggregation On Granger Causality

Economists often have to use temporally aggregated data in causality tests. A number of theoretical studies have pointed out that temporal aggregation has distorting effects on causal inference. This paper provides a quantitative assessment of the magnitude of the distortions created by temporal aggregation by plugging in theoretical cross covariances into the limiting values of least squares estimates. Some Monte Carlo results and an application are provided to assess the impact in small samples. It is observed that in general the most distorting causal inferences are likely at low levels of temporal aggregation. At high levels of aggregation, causal information concentrates in contemporaneous correlations. At present, a data-based approach is not available to establish the direction of causality between contemporaneously correlated variables.