Are statistical reporting agencies getting it right? Data rationality and business cycle asymmetry

This paper provides new evidence on the rationality of industrial production (IP) and the producer price index (PPI). However, rather than examining preliminary and fully revised data, as is usually the practice, we examine the entire revision history for each data series. Thus, we are able to assess whether earlier releases of data are in any sense "less" rational than later releases, for example, and when early releases of data become rational. Our findings suggest that seasonally unadjusted IP and PPI become rational after approximately 3-4 months, while seasonally adjusted versions of these series remain irrational for at least 12 months after initial release. Additionally, we find that there is a clear increase in the volatility of early data releases during recessions, suggesting that early data are less reliable in tougher economic times. One feature of the approach that we take is that we are able to include revision histories in the information sets used to examine the rationality of a particular release of data. This in turn allows us to assess whether the revision process itself is predictable from its own past, hence possibly leading to rules for the construction of "better" preliminary releases of data. For most of the variables examined, we find evidence of this form of predictability. Another feature of the approach taken in the paper is that we are able to provide evidence suggesting that nonlinearities in economic behavior manifest themselves in the form of nonlinearities in the rationality of early releases of economic data. This is done by separately analyzing expansionary and recessionary economic phases and by allowing for structural breaks. These types of nonlinearities are shown to be prevalent, and in some cases incorrect inferences concerning unbiasedness and efficiency arise when they are not taken account of. For example, seasonally unadjusted IP data become unbiased much more quickly after 1980 than before 1980. Additionally, seasonally adjusted IP data take less time to become efficient during expansions than during recessions

Forecasting industrial production with linear, nonlinear, and structural change models

We compare the forecasting performance of linear autoregressive models, autoregressive models with structural breaks, self-exciting threshold autoregressive models, and Markov switching autoregressive models in terms of point, interval, and density forecasts for h-month growth rates of industrial production of the G7 countries, for the period January 1960-December 2000. The results of point forecast evaluation tests support the established notion in the forecasting literature on the favorable performance of the linear AR model. By contrast, the Markov switching models render more accurate interval and density forecasts than the other models, including the linear AR model. This encouraging finding supports the idea that non-linear models may outperform linear competitors in terms of describing the uncertainty around future realizations of a time series

Smooth Transition Models: Extensions and Outlier Robust Inference

The dynamic properties of many economic time series variables can be characterised as state-dependent or regime-switching. A popular model to describe this type of non-linear behaviour is the smooth transition model, which accommodates two regimes facilitating a gradual transition from one regime to the other. The first part of this thesis considers three extensions of the basic smooth transition model. Models are developed which allow for more than two regimes, for time-varying properties in conjunction with regime-switching behaviour, and for modeling several time series jointly. Particular emphasis is placed on the inter-related issues of specification and inference in such models. The second part of the thesis concerns the influence of atypical observations on testing procedures for smooth transition non-linearity and on the estimation of smooth transition models. Traditional methods that are used for these purposes are found to be very sensitive to such outliers. Therefore, outlier robust testing procedures and estimation methods are develope

Does the absence of cointegration explain the typical findings in long horizon regressions?

One of the stylized facts in financial and international economics is that of increasing predictability of variables such as exchange rates and stock returns at longer horizons. This fact is based upon applications of long horizon regressions, from which the typical findings are that the point estimates of the regression parameter, the associated t-statistic, and the regression R^2 all tend to increase as the horizon increases. Such long horizon regression analyses implicitly assume the existence of cointegration between the variables involved. In this paper, we investigate the consequences of dropping this assumption. In particular, we look upon the long horizon regression as a conditional error-correction model and interpret the test for long horizon predictability as a single equation test for cointegration. We derive the asymptotic distributions of the estimator of the regression parameter and its t-statistic for arbitrary horizons, under the null hypothesis of no cointegration. It is shown that these distributions provide an alternative explanation for at least part of the typical findings. Furthermore, the distributions are used to derive a Phillips-Perron type correction to the ordinary least-squares t-statistic in order to endow it with a stable size for given, arbitrary, horizon. A local asymptotic power analysis reveals that the power of long horizon regression tests does not increase with the horizon. Exchange rate data are used to demonstrate the empirical relevance of our theoretical results

Short-term volatility versus long-term growth: evidence in US macroeconomic time series

We test for a change in the volatility of 215 US macroeconomic time series over the period 1960-1996. We find that about 90\\% of these series have experienced a break in volatility during this period. This result is robust to controlling for instability in the mean and business cycle nonlinearities. Real variables have seen a reduction in volatility since the early 1980s, which is accompanied by lower but steadier output growth. Furthermore, nominal variables have seen temporary increases in their volatility around the early 1980s. This suggests the existence of a trade-off between short-term volatility and the long-term pattern of growth

Measuring volatility with the realized range

Realized variance, being the summation of squared intra-day returns, has quickly gained popularity as a measure of daily volatility. Following Parkinson (1980) we replace each squared intra-day return by the high-low range for that period to create a novel and more efficient estimator called the realized range. In addition we suggest a bias-correction procedure to account for the effects of microstructure frictions based upon scaling the realized range with the average level of the daily range. Simulation experiments demonstrate that for plausible levels of non-trading and bid-ask bounce the realized range has a lower mean squared error than the realized variance, including variants thereof that are robust to microstructure noise. Empirical analysis of the S&P500 index-futures and the S&P100 constituents confirm the potential of the realized range

How to Identify and Forecast Bull and Bear Markets?

The state of the equity market, often referred to as a bull or a bear market, is of key importance for financial decisions and economic analyses. Its latent nature has led to several methods to identify past and current states of the market and forecast future states. These methods encompass semi-parametric rule-based methods and parametric regime-switching models. We compare these methods by new statistical and economic measures that take into account the latent nature of the market state. The statistical measure is based directly on the predictions, while the economic mea- sure is based on the utility that results when a risk-averse agent uses the predictions in an investment decision. Our application of this framework to the S&P500 shows that rule-based methods are preferable for (in-sample) identification of the market state, but regime-switching models for (out-of-sample) forecasting. In-sample only the direction of the market matters, but for forecasting both means and volatilities of returns are important. Both the statistical and the economic measures indicate that these differences are significant

Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility

This paper documents that factors extracted from a large set of macroeconomic variables bear useful information for predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression models improve upon both benchmark models that only include valuation ratios and interest rate related variables, and possibly individual macro variables, as well as the historical average excess return. The improvements in out-of-sample forecast accuracy are both statistically and economically significant. The factor-augmented predictive regressions have superior market timing ability and volatility timing ability, while a mean-variance investor would be willing to pay an annual performance fee of several hundreds of basis points to switch from the predictions offered by the benchmark models to those of the factor-augmented models. An important reason for the superior performance of the factor-augmented predictive regressions is the stability of their forecast accuracy, whereas the benchmark models suffer from a forecast breakdown during the 1990s

High-Frequency Technical Trading: The Importance of Speed

This paper investigates the importance of speed for technical trading rule performance for three highly liquid ETFs listed on NASDAQ over the period January 6, 2009 up to September 30, 2009. In addition we examine the characteristics of market activity over the day and within subperiods corresponding to hours, minutes, and seconds. Speed has a clear impact on the return of technical trading rules. For strategies that yield a positive return when they experience no delay, a delay of 200 milliseconds is enough to lower performance significantly. On low volatility days this is already the case for delays larger than 50 milliseconds. In addition, the importance of speed for trading rule performance increases over time. Market activity follows a U-shape over the day with a spike at 10:00AM due to macroeconomic announcements and is characterized by periodic activity within the day, hour, minute, and second