Causal relationships in econometrics are typically based on the concept of predictability and are established by testing Granger causality. Such relationships are susceptible to change, especially during times of financial turbulence, making the real-time detection of instability an important practical issue. This article develops a test for detecting changes in causal relationships based on a recursive evolving window, which is analogous to a procedure used in recent work on financial bubble detection. The limiting distribution of the test takes a simple form under the null hypothesis and is easy to implement in conditions of homoskedasticity and conditional heteroskedasticity of an unknown form. Bootstrap methods are used to control family-wise size in implementation. Simulation experiments compare the efficacy of the proposed test with two other commonly used tests, the forward recursive and the rolling window tests. The results indicate that the recursive evolving approach offers the best finite sample performance, followed by the rolling window algorithm. The testing strategies are illustrated in an empirical application that explores the causal relationship between the slope of the yield curve and real economic activity in the United States over the period 1980-2015. 6 Recall that the duration of the causality episode is 0.2. When the minimum window f 0 exceeds that duration, the regression contains a mix of causal and non-causal observations. 7 They are obtained by matching the SV model to real exchange rate data (Shephard, 1996).

A new test for time -dependent parameters is proposed. The Trig-test is based on a trigonometric expansion to approximate the unknown functional form of the variation in the parameters concerned. It is shown to have the correct empirical size and excellent power to detect structural breaks and stochastic parameter variation. The appropriate use of the Trigtest is demonstrated by testing for structural breaks in the U.S. inflation rate. The test detects a statistically significant increase in the U.S. inflation rate beginning in the early 1970s and lasting through to the early 1980s.

This paper reexamines changes in the causal link between money and income in the United States for over the past half century (1959-2014). Three methods for the datadriven discovery of change points in causal relationships are proposed, all of which can be implemented without prior detrending of the data. These methods are a forward recursive algorithm, a recursive rolling algorithm and the rolling window algorithm all of which utilize subsample tests of Granger causality within a lag-augmented vector autoregressive framework. The limit distributions for these subsample Wald tests are provided. The results from a suite of simulation experiments suggest that the rolling window algorithm provides the most reliable results, followed by the recursive rolling method. The forward expanding window procedure is shown to have worst performance. All three approaches find evidence of money-income causality during the Volcker period in the 1980s. The rolling and recursive rolling algorithms detect two additional causality episodes: the turbulent period of late 1960s and the starting period of the subprime mortgage crisis in 2007.

Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent and asymptotically efficient, but unfortunately difficult to obtain if a closed form expression for the transitional probability density function of the process is not available. As a result, a large number of competing estimation procedures have been proposed. This paper provides a critical evaluation of the various estimation techniques. Special attention is given to the ease of implementation and comparative performance of the procedures when estimating the parameters of the Cox-Ingersoll-Ross and Ornstein-Uhlenbeck equations respectively.

Medium-term momentum, or the tendency of investment strategies based on buying past winning stocks while selling past losing stocks to maintain above normal performance over a period, has been a well-documented feature of stock returns in the US. We investigate the performance of momentum investment strategies in portfolios of Australian stocks and examine some of the common explanations and empirical features of momentum. The paper establishes the presence of a strong medium-term momentum effect, which cannot be completely accounted for by any of the possible explanations considered in this paper.

Individuals' attitudes to inequality aversion are measured using survey data, based on the leaky-bucket experiment, for several groups of students in Australia and Israel. Three forms of social welfare function are estimated. It is found that measures of inequality aversion can be obtained with some precision and that these estimates are substantially lower than the values typically used by those measuring inequality and examining optimal tax structures. Furthermore, a welfare function based on the Gini inequality measure is generally found to give a better ®t than forms based on constant relative or constant absolute inequality aversion.

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This paper re-examines changes in the causal link between money and income in the United States over the past half century (1959–2014). Three methods for the data-driven discovery of change points in causal relationships are proposed, all of which can be implemented without prior detrending of the data. These methods are a forward recursive algorithm, a rolling window algorithm, and a recursive evolving algorithm all of which utilize subsample tests of Granger causality within a lag-augmented vector autoregressive framework. The limit distributions for these subsample Wald tests are provided. Bootstrap methods are developed to control family-wise size in the implementation of the recursive testing algorithms. The results from a suite of simulation experiments suggest that the recursive evolving window algorithm provides the most reliable results, followed by the rolling window method. The forward expanding window procedure is shown to have the worst performance. Both the rolling window and recursive evolving approaches find evidence of Granger causality running from money to income during the Volcker period in the 1980s. The forward algorithm does not find any evidence of causality over the entire sample period.