Variance changes detection in multivariate time series

This paper studies the detection of step changes in the variances and in the correlation structure of the components of a vector of time series. Two procedures are considered. The first is based on the likelihood ratio test and the second on cusum statistics. These two procedures are compared in a simulation study and we conclude that the cusum procedure is more powerful. The procedures are illustrated in two examples.

The impact of unilateral divorce on crime

In this paper, we evaluate the impact of unilateral divorce on crime. First, using crime rates from the FBI´s Uniform Crime Report program for the period 1965-1998 and differences in the timing in the introduction of the reform, we find that unilateral divorce has a positive impact on violent crime rates, with an 8% to 12% average increase for the period under consideration. Second, arrest data not only confirms the findings of a positive impact on violent crime but also shows that this impact is concentrated among those age groups (15 to 24) that are more likely to engage in these type of offenses. Specifically, for the age group 15-19, we observe an average impact over the period under analysis of 40% and 36% for murder and aggravated assault arrest rates, respectively. Disaggregating total arrest rates by race, we find that the effects are driven by the Black sub-sample. Third, using the age at the time of the divorce law reform as a second source of variation to analyze age-specific arrest rates we confirm the positive impact on the different types of violent crime as well as a positive impact for property crime rates, controlling for all confounding factors that may operate at the state-year, state age or age-year level. The results for murder arrests and for homicide rates (Supplemental Homicide Report) for the 15-24 age groups are robust with respect to specifications and specifically those that include year-state and year-age dummies. The magnitude goes from 15% to 40% depending on the specification and the age at the time of the reform

The reaction of stock market returns to anticipated unemployment

We empirically investigate the short-run impact of anticipated and unanticipated unemployment rates on stock prices. We particularly examine the nonlinearity in stock market’s reaction to unemployment rate and study the effect at each individual point (quantile) of stock return distribution. Using nonparametric Granger causality and quantile regression based tests, we find that, contrary to the general findings in the literature, only anticipated unemployment rate has a strong impact on stock prices. Quantile regression analysis shows that the causal effects of anticipated unemployment rate on stock return are usually heterogeneous across quantiles. For quantile range [0.35, 0.80], an increase in the anticipated unemployment rate leads to an increase in the stock market price. For the other quantiles the impact is statistically insignificant. Thus, an increase in the anticipated unemployment rate is in general a good news for stock prices. Finally, we offer a reasonable explanation of why unemployment rate should affect stock prices and how it affects them. Using Fisher and Phillips curve equations, we show that high unemployment rate is followed by monetary policy action of Federal Reserve (Fed). When unemployment rate is high, the Fed decreases the interest rate, which in turn increases the stock market prices

A new class of distribution-free tests for time series models specification

The construction of asymptotically distribution free time series model specification tests using as statistics the estimated residual autocorrelations is considered from a general view point. We focus our attention on Box-Pierce type tests based on the sum of squares of a few estimated residual autocorrelations. This type of tests belongs to the class defined by quadratic forms of weighted residual autocorrelations, where weights are suitably transformed resulting in asymptotically distribution free tests. The weights can be optimally chosen to maximize the power function when testing in the direction of local alternatives. The optimal test in this class against MA, AR or Bloomfield alternatives is a Box-Pierce type test based on the sum of squares of a few transformed residual autocorrelations. Such transformations are, in fact, the recursive residuals in the projection of the residual autocorrelations on a certain score function

Yet another puzzle? the relation between price and performance in the mutual fund industry

Gruber (1996) drew attention to the puzzle that investors buy actively-managed funds even though, on average, they underperform index funds. We uncover another puzzling fact about the market for actively-managed equity mutual funds: funds with worse before-fee performance charge higher fees. We then conduct a series of robustness checks and find that the apparently anomalous fee-performance relation survives all of them. Finally, we show that this relation may be explained as the outcome of strategic fee setting by mutual funds in the presence of investors with different degrees of sensitivity to performance

The importance of frequency in estimating labour market transition rates

Labour market transition rates are typically estimated using survey data, which are mainly carried out at monthly or quarterly frequency. I argue that rates from surveys at different frequencies are not comparable, even if corrected for time aggregation. I estimate labour market transition rates using monthly and quarterly frequency CPS data. I apply a time-aggregation correction to make them comparable. I find notable differences in terms of levels and volatilities. While the continuous time-aggregation correction does not alter the unemployment decomposition using the monthly survey, it does so when using the quarterly survey