A Hidden Markov Model Approach to Information‐Based Trading: Theory and Applications

This paper develops a novel approach to information‐based securities trading by characterizing the hidden state of the market, which varies following a Markov process. Extensive simulation demonstrates that the approach can successfully identify market states and generate dynamic measures of information‐based trading that outperform prevailing models. A sample of 120 NYSE stocks further verifies that it can better depict trading dynamics. With this sample, we characterize the features of information asymmetry and belief dispersion around earnings announcements. The sample is also applied to the study of the co‐movements of trading activities due to private information or disputable public information. Copyright © 2014 John Wiley & Sons, Ltd.     

Evaluating interval forecasts of high‐frequency financial data

A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen ( 1998 ), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression‐based test, and a modified version of Christoffersen's Markov chain test for independence, and analyse their properties when the financial time series exhibit periodic volatility. These approaches lead to different conclusions when interval forecasts of FTSE100 index futures returns generated by various GARCH(1,1) and periodic GARCH(1,1) models are evaluated. Copyright © 2003 John Wiley & Sons, Ltd.     

Probabilistic forecasting of industrial electricity load with regime switching behavior

This paper suggests a novel inhomogeneous Markov switching approach for the probabilistic forecasting of industrial companies’ electricity loads, for which the load switches at random times between production and standby regimes. The model that we propose describes the transitions between the regimes using a hidden Markov chain with time-varying transition probabilities that depend on calendar variables. We model the demand during the production regime using an autoregressive moving-average (ARMA) process with seasonal patterns, whereas we use a much simpler model for the standby regime in order to reduce the complexity. The maximum likelihood estimation of the parameters is implemented using a differential evolution algorithm. Using the continuous ranked probability score (CRPS) to evaluate the goodness-of-fit of our model for probabilistic forecasting, it is shown that this model often outperforms classical additive time series models, as well as homogeneous Markov switching models. We also propose a simple procedure for classifying load profiles into those with and without regime-switching behaviors.     

Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro‐level Analysis?

Forecasting labour market flows is important for budgeting and decision‐making in government departments and public administration. Macroeconomic forecasts are normally obtained from time series data. In this article, we follow another approach that uses individual‐level statistical analysis to predict the number of exits out of unemployment insurance claims. We present a comparative study of econometric, actuarial and statistical methodologies that base on different data structures. The results with records of the German unemployment insurance suggest that prediction based on individual‐level statistical duration analysis constitutes an interesting alternative to aggregate data‐based forecasting. In particular, forecasts of up to six months ahead are surprisingly precise and are found to be more precise than considered time series forecasts.     

Nonparametric model validations for hidden Markov models with applications in financial econometrics

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous-time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.     

Inter‐state dynamics of invention activities, 1930–2000

We study the dynamics of the cross‐section distribution of patents per capita for the 48 continental US states from 1930 to 2000 using a discrete‐state Markov chain. We test for and find evidence in favor of the (knowledge) convergence hypothesis. The distribution of patents is converging to a limiting distribution that is significantly more concentrated than its initial distribution. States in the extreme are more mobile than states in the middle of the cross‐sectional distribution and are likely to move to the middle. However, the rate of convergence to the limiting distribution is ‘slow’. Copyright © 2006 John Wiley & Sons, Ltd.     

Modeling and calculating the effect of treatment at baseline from panel outcomes

We propose and examine a panel data model for isolating the effect of a treatment, taken once at baseline, from outcomes observed over subsequent time periods. In the model, the treatment intake and outcomes are assumed to be correlated, due to unobserved or unmeasured confounders. Intake is partly determined by a set of instrumental variables and the confounding on unobservables is modeled in a flexible way, varying both by time and treatment state. Covariate effects are assumed to be subject-specific and potentially correlated with other covariates. Estimation and inference is by Bayesian methods that are implemented by tuned Markov chain Monte Carlo methods. Because our analysis is based on the framework developed by Chib [2004. Analysis of treatment response data without the joint distribution of counterfactuals. Journal of Econometrics, in press], the modeling and estimation does not involve either the unknowable joint distribution of the potential outcomes or the missing counterfactuals. The problem of model choice through marginal likelihoods and Bayes factors is also considered. The methods are illustrated in simulation experiments and in an application dealing with the effect of participation in high school athletics on future labor market earnings.     

Solidarity properties and a Doeblin decomposition for a class of non-Markovian stochastic processes

We are concerned with solidarity and a Doeblin decomposition for a class of non-Markovian discrete parameter stochastic processes. Since any such process is associated with a certain general Markov chain whose transition probability function has a special form, we use the theory of Markov chains with continuous components to this particular chain in order to get properties of the non-Markovian process. We illustrate our results on a model closely related to learning theory.     

Coherent forecasting for count time series using Box–Jenkins's AR(p) model

During the last three decades, integer‐valued autoregressive process of order p [or INAR(p)] based on different operators have been proposed as a natural, intuitive and maybe efficient model for integer‐valued time‐series data. However, this literature is surprisingly mute on the usefulness of the standard AR(p) process, which is otherwise meant for continuous‐valued time‐series data. In this paper, we attempt to explore the usefulness of the standard AR(p) model for obtaining coherent forecasting from integer‐valued time series. First, some advantages of this standard Box–Jenkins's type AR(p) process are discussed. We then carry out our some simulation experiments, which show the adequacy of the proposed method over the available alternatives. Our simulation results indicate that even when samples are generated from INAR(p) process, Box–Jenkins's model performs as good as the INAR(p) processes especially with respect to mean forecast. Two real data sets have been employed to study the expediency of the standard AR(p) model for integer‐valued time‐series data.     

Measuring Business Cycles Intra‐Synchronization in US: A Regime‐switching Interdependence Framework

This paper proposes a Markov‐switching framework to endogenously identify periods where economies are more likely to (i) synchronously enter recessionary and expansionary phases, and (ii) follow independent business cycles. The reliability of the framework is validated with simulated data in Monte Carlo experiments. The framework is applied to assess the time‐varying intra‐country synchronization in the US. The main results report substantial changes over time in the cyclical affiliation patterns of US states, and show that the more similar the economic structures of states, the higher the correlation between their business cycles. A synchronization‐based network analysis discloses a change in the propagation pattern of aggregate contractionary shocks across states, suggesting that the US has become more internally synchronized since the early 1990s.     

Valuation ratios and long‐horizon stock price predictability

Using annual data for 1872–1997, this paper re‐examines the predictability of real stock prices based on price–dividend and price–earnings ratios. In line with the extant literature, we find significant evidence of increased long‐horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at short horizons but can be rejected at longer horizons based on bootstrapped critical values constructed from linear representations of the data. While increased statistical power at long horizons in finite samples provides a possible explanation for the pattern of predictability in the data, we find via Monte Carlo simulations that the power to detect predictability in finite samples does not increase at long horizons in a linear framework. An alternative explanation for the pattern of predictability in the data is nonlinearities in the underlying data‐generating process. We consider exponential smooth‐transition autoregressive models of the price–dividend and price–earnings ratios and their ability to explain the pattern of stock price predictability in the data. Copyright © 2005 John Wiley & Sons, Ltd.     

Macroeconomic forecasting in times of crises

We propose a parsimonious semiparametric method for macroeconomic forecasting. Based on ideas of clustering and similarity, we partition the series into blocks, search for the closest blocks to the latest block of observations, and forecast with the matched blocks. In a real-time forecasting exercise, we show that our approach does especially well for labor market and other key macro variables. Our method outperforms parametric linear, nonlinear, time-varying, and combination forecasts for the period 1999–2015 and particularly in the Great Recession. When adding financial spreads, our method delivers further improvements for labor market variables and capacity utilization.     

Conditional Markov chain and its application in economic time series analysis

Motivated by the great moderation in major US macroeconomic time series, we formulate the regime switching problem through a conditional Markov chain. We model the long‐run volatility change as a recurrent structure change, while short‐run changes in the mean growth rate as regime switches. Both structure and regime are unobserved. The structure is assumed to be Markovian. Conditioning on the structure, the regime is also Markovian, whose transition matrix is structure‐dependent. This formulation imposes interpretable restrictions on the Hamilton Markov switching model. Empirical studies show that this restricted model well identifies both short‐run regime switches and long‐run structure changes in the US macroeconomic data. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluating the dynamic employment effects of training programs in East Germany using conditional difference‐in‐differences

This study analyzes the employment effects of training in East Germany. We propose and apply an extension of the widely used conditional difference‐in‐differences estimator. Focusing on transition rates between nonemployment and employment, we take into account that employment is a state‐ and duration‐dependent process. Our results show that using transition rates is more informative than using unconditional employment rates as commonly done in the literature. Moreover, the results indicate that due to the labor market turbulence during the East German transformation process the focus on labor market dynamics is important. Training as a first participation in a program of Active Labor Market Policies shows zero to positive effects both on re‐employment probabilities and on probabilities of remaining employed with notable variation over the different start dates of the program. Copyright © 2008 John Wiley & Sons, Ltd.     

Enterprise level cluster innovation with policy design

ABSTRACT

An industrial cluster is an important link in the process of industrialization. The existing research is mainly based on the market economy. Our paper considers external policy design for cluster innovation based on the transition from planned economy to market economy in China. This paper finds some enterprises in the cluster are transferred from micro-enterprises to small ones, but does not find clustering from the small enterprise to middle or larger enterprise. Furthermore, our paper explained why such a cluster occurs by applying a semi-parametric counterfactual approach. The results indicate that building cluster zones as upgrading the enterprise structure policy and implementing VAT tax systems as the tax benefit policy has the most proponent role in industrial clustering, whereas increasing the loan/financing as the credit policy has a minor impact, which is not negligible either. Overall, this study explains why clusters shift to high output valued with a high interpretation of up to 97%. The contribution of this paper is not only to describe the time process of micro-to-small enterprise clustering but also to give the policy design how to achieve rapid micro-to-small enterprise clustering.     

The Credibility of Hong Kong's Currency Board System: Looking Through the Prism of MS‐VAR Models with Time‐Varying Transition Probabilities

This paper employs a Markov regime‐switching VAR model to describe and analyse the time‐varying credibility of Hong Kong's currency board system. The endogenously estimated discrete regime shifts are made dependent on macroeconomic fundamentals. This enables us to determine which changes in macroeconomic variables can trigger switches between the low and high credibility regimes. We carry out extensive testing to search for the most appropriate specification of the Markov regime‐switching model. We find strong evidence of regime switching behaviour that portrays the time‐varying nature of credibility in the historical data.     

A cross‐country study on the relationship between financial development and earnings management

This study investigates whether a country's level of financial development is associated with earnings management in an international setting. Financial development is likely to heighten the monitoring and scrutiny of accounting numbers because of strengthened investor protection laws and regulations as well as sophisticated market participants. Therefore, we first hypothesize that both accrual‐based and real earnings management decrease with greater financial development. However, research shows that managers tend to apply real earnings management, instead of accrual‐based earnings management, under strict accounting standards, regulations, and close auditor scrutiny. Thus, we explore the alternative hypothesis that accrual‐based earnings management decreases but real earnings management increases along with higher financial development. We examine the relationship between financial development and both types of earnings management using 56,830 observations in 37 countries covering the period 2009–2012. The results indicate that both types of earnings management are more restrained under higher levels of financial development.     

Modelling dependence using skew t copulas: Bayesian inference and applications

We construct a copula from the skew t distribution of Sahu et al. ( 2003 ). This copula can capture asymmetric and extreme dependence between variables, and is one of the few copulas that can do so and still be used in high dimensions effectively. However, it is difficult to estimate the copula model by maximum likelihood when the multivariate dimension is high, or when some or all of the marginal distributions are discrete‐valued, or when the parameters in the marginal distributions and copula are estimated jointly. We therefore propose a Bayesian approach that overcomes all these problems. The computations are undertaken using a Markov chain Monte Carlo simulation method which exploits the conditionally Gaussian representation of the skew t distribution. We employ the approach in two contemporary econometric studies. The first is the modelling of regional spot prices in the Australian electricity market. Here, we observe complex non‐Gaussian margins and nonlinear inter‐regional dependence. Accurate characterization of this dependence is important for the study of market integration and risk management purposes. The second is the modelling of ordinal exposure measures for 15 major websites. Dependence between websites is important when measuring the impact of multi‐site advertising campaigns. In both cases the skew t copula substantially outperforms symmetric elliptical copula alternatives, demonstrating that the skew t copula is a powerful modelling tool when coupled with Bayesian inference. Copyright © 2010 John Wiley & Sons, Ltd.     

Job hopping, earnings dynamics, and industrial agglomeration in the software publishing industry

This paper investigates the implications of industrial clustering for labor mobility and earnings dynamics in one large and increasingly important high-technology sector. Taking advantage of longitudinal employee-employer matched data, I exploit establishment-level variation in agglomeration to explore how clustering in the software publishing industry affects labor market outcomes. The results show that clustering makes it easier for workers to job hop within the sector. Higher earnings levels in more agglomerated areas are partly attributable to sorting across locations among workers and firms in the industry on the basis of observable and unobservable characteristics. Controlling for this heterogeneity, workers in clusters have relatively steep earnings-tenure profiles, accepting lower wages early in their careers in exchange for stronger earnings growth and higher wages later. These findings are consistent with theoretical models in which agglomeration improves labor market coordination and facilitates greater learning and human capital formation.     

Job hopping,earnings dynamics,and industrial agglomeration in the software publishing industry

This paper investigates the implications of industrial clustering for labor mobility and earnings dynamics in one large and increasingly important high-technology sector. Taking advantage of longitudinal employee-employer matched data, I exploit establishment-level variation in agglomeration to explore how clustering in the software publishing industry affects labor market outcomes. The results show that clustering makes it easier for workers to job hop within the sector. Higher earnings levels in more agglomerated areas are partly attributable to sorting across locations among workers and firms in the industry on the basis of observable and unobservable characteristics. Controlling for this heterogeneity, workers in clusters have relatively steep earnings-tenure profiles, accepting lower wages early in their careers in exchange for stronger earnings growth and higher wages later. These findings are consistent with theoretical models in which agglomeration improves labor market coordination and facilitates greater learning and human capital formation.