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The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross‐section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse‐group LASSO regularization which can take advantage of the mixed‐frequency time series panel data structures. Our empirical results show the superior performance of our machine learning panel data regression models over analysts' predictions, forecast combinations, firm‐specific time series regression models, and standard machine learning methods. [ABSTRACT FROM AUTHOR]

This paper studies Nasdaq market makers' activities during the one and one-half hour preopening period. Price discovery during the preopening is conducted via price signaling as opposed to the auction used to open the NYSE or the continuous market used during trading. In the absence of trades, Nasdaq dealers use crossed and locked inside quotes to signal to other market makers which direction the price should move. Furthermore, we find evidence of price leadership among market makers that bears little resemblance to their IPO/SEO lead underwriter participation. [ABSTRACT FROM AUTHOR]

There is now considerable evidence suggesting that estimated betas of unconditional capital asset pricing models (CAPMs) exhibit statistically significant time variation. Therefore, many have advocated the use of conditional CAPMs. If we succeed in capturing the dynamics of beta risk, we are sure to outperform constant beta models. However, if the beta risk is inherently misspecified, there is a real possibility that we commit serious pricing errors, potentially larger than with a constant traditional beta model. In this paper we show that this is indeed the case, namely that pricing errors with constant traditional beta models are smaller than with conditional CAPMs. [ABSTRACT FROM AUTHOR]

We examine the effects of mixed sampling frequencies and temporal aggregation on the size of commonly used tests for cointegration, and we find that these effects may be severe. Matching sampling schemes of all series generally reduces size distortion, and the nominal size is obtained asymptotically only when all series are skip sampled in the same way - for example, end-of-period sampling. We propose and analyse mixed-frequency versions of the cointegration tests in order to control the size when some high-frequency data are available. Otherwise, when no high-frequency data are available, we discuss controlling size using bootstrapped critical values. We test stock prices and dividends for cointegration as an empirical demonstration. [ABSTRACT FROM AUTHOR]

Using a unique data set of detailed balance sheet information on mutual funds, we find that most mutual funds using derivatives do so to a very limited extent that has little discernable impact on returns. However, there exist two types of funds that make more extensive use of derivatives, global funds and specialized domestic equity funds. The risk and return characteristics of these two groups of funds are significantly different from funds employing derivatives sparingly or not at all. Fund managers time their use of derivatives in response to past returns. Evidence during the financial crisis of August 1998 supports the hypothesis that the effects of derivative use are most pronounced during the periods of extreme movement. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:629-658, 2011 [ABSTRACT FROM AUTHOR]

We consider a log-linearized version of a discounted rents model to price commercial real estate as an alternative to traditional hedonic models. First, we verify a key implication of the model, namely, that cap rates forecast commercial real estate returns. We do this using two different methodologies: time series regressions of 21 US metropolitan areas and mixed data sampling (MIDAS) regressions with aggregate REIT returns. Both approaches confirm that the cap rate is related to fluctuations in future returns. We also investigate the provenance of the predictability. Based on the model, we decompose fluctuations in the cap rate into three parts: (i) local state variables (demographic and local economic variables); (ii) growth in rents; and (iii) an orthogonal part. About 30% of the fluctuation in the cap rate is explained by the local state variables and the growth in rents. We use the cap rate decomposition into our predictive regression and find a positive relation between fluctuations in economic conditions and future returns. However, a larger and significant part of the cap rate predictability is due to the orthogonal part, which is unrelated to fundamentals. This implies that economic conditions, which are also used in hedonic pricing of real estate, cannot fully account for future movements in returns. We conclude that commercial real estate prices are better modelled as financial assets and that the discounted rent model might be more suitable than traditional hedonic models, at least at an aggregate level. [ABSTRACT FROM AUTHOR]

In this paper we examine the prevalence of data, specification, and parameter uncertainty in the formation of simple rules that mimic monetary policymaking decisions. Our approach is to build real-time data sets and simulate a real-time policy-setting environment in which we assume that policy is captured by movements in the actual federal funds rate, and then to assess what sorts of policy rule models and what sorts of data best explain what the Federal Reserve actually did. This approach allows us not only to track the performance of alternative rules over time (hence facilitating a type of model selection among competing rules), but also to more generally assess the importance of the data revision process in the formation of macroeconomic time series models. From the perspective of real-time data, our results suggest that the use of data that are erroneous, in the sense that they were not available at the time decisions could have been made based on forecasts from the rules, can lead to the selection of quantitatively different models. From the perspective of finding a rule that best approximates what the Federal Reserve Board (Fed) has actually done (and hence from the perspective of finding a rule that best approximates what the Fed will do in the future), we find that (i) our version of "calibration" is better than naïve estimation, although both are dominated by an approach to rule formation based on the use of adaptive least-squares learning; (ii) rules based on data that are not seasonally adjusted are more reliable than those based on seasonally adjusted data; and (iii) rules based solely on preliminary data do not minimize mean square forecast error risk. In particular, early releases of data can be noisy, and for this reason it is useful to also use data that have been revised when making decisions using policy rules. [ABSTRACT FROM AUTHOR]

Evaluates the performance of proposed tests for structural breaks in the conditional variance dynamics of asset returns. Use of high-frequency data; Identification of the number and location of multiple breaks; Size and power of the tests for detecting breaks in the conditional variance.

Time series are demeaned when sample autocorrelation functions are computed. By the same logic it would seem appealing to remove seasonal means from seasonal time series before computing sample autocorrelation functions. Yet, standard practice is only to remove the overall mean and ignore the possibility of seasonal mean shifts in the data. Whether or not time series are seasonally demeaned has very important consequences on the asymptotic behavior of autocorrelation functions. The effect on the asymptotic distribution of seasonal mean shifts and their removal is investigated and the practical consequences of these theoretical developments are discussed. We also examine the small sample behavior of autocorrelation function estimates through Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Presents information on a study on modelling seasonality in non-linear analysis in the context of econometry. Description of non-linear time series models and seasonality; Periodic regime-switching models; Methodology; Conclusions of the study.

Presents a generalized predictive testing procedure for structural stability in nonlinear dynamic simultaneous equations model. Attractive features of the test; Two alternative ways of dealing with nonnormal disturbances; Applications of the techniques suggested to a VAR model and to consumption-based asset pricing models.

A predictive test for parameter stability of GMM estimates is studied. The test is based on examining whether parameter estimates of one subsample can be used to predict over the other subsample. It applies to a wide variety of dynamic, nonlinear simultaneous equation models for the wide class of inference methods covered by GMM. The asymptotic properties and local power are derived. It is shown that the test has equal asymptotic power against local alternatives in both subsamples regardless of which subsample is conditioned on. The test is also compared with tests proposed by Andrews and Fair (1988). [ABSTRACT FROM AUTHOR]

Provides a statistical analysis of the Canadian budget process with information provided by the Department of Finance. Description of methodology used in analysis; Types of illustrations used in analysis, and why they were used; Observations made with applications of tests to fiscal forecasts; Detailed information on analysis.

To forecast the turnaround of an economy we do not usually take seasonal effects into account. Recently, the author showed that business cycle turning points as well as durations do not appear to be uniformly distributed throughout the year (see Ghysels, 1991a). In this paper we suggest improving the forecasting performance of turning-point predictions by adopting periodic hazard models. Following Diebold and Rudebusch (1989), we construct several formal probability models and score their prediction performance. The results indicate that for sequential forecasting rules significant gains can be made by exploiting periodicities in turning-point probabilities. [ABSTRACT FROM AUTHOR]

The article focuses on identification and prediction of economic recessions using time-frequency functional models. It argues that the model being used by several researchers including Holan, Yang, and Matheson provides a better forecast than models that excludes daily frequency variable. It adds that their framework can be represented into a Monitoring, Intrusion, Detection, Administration System (MIDAS) extension of a probit model.