Simple solutions to the initial conditions problem in dynamic,nonlinear panel data models with unobserved heterogeneity

I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models. Rather than attempting to obtain the joint distribution of all outcomes of the endogenous variables, I propose finding the distribution conditional on the initial value (and the observed history of strictly exogenous explanatory variables). The approach is flexible, and results in simple estimation strategies for at least three leading dynamic, nonlinear models: probit, Tobit and Poisson regression. I treat the general problem of estimating average partial effects, and show that simple estimators exist for important special cases. Copyright © 2005 John Wiley & Sons, Ltd.     

Nonparametric identification in nonseparable panel data models with generalized fixed effects

This paper extends the familiar notion of fixed effects to nonlinear structures with infinite-dimensional unobservables, like preferences. The main result is that a generalized version of differencing identifies local average responses (LARs) in nonseparable structures. In contrast to existing results, this does not require either substantial restrictions on functional form or independence between the persistent unobservables and the explanatory variables of interest, and it requires only two time periods. On the other hand, the results are confined to the subpopulation of “stayers” (Chamberlain, 1982), i.e., the population for which the explanatory variables do not change over time. We extend the basic framework to include time trends and dynamics in the explanatory variables, and we show how distributional effects as well as average partial effects are identified. Our approach also allows endogeneity in the transitory unobservables. Furthermore, we show that this new identification principle can be applied to well-known objects like the slope coefficient in the semiparametric panel data binary choice model with fixed effects. Finally, we suggest estimators for the local average response and average partial effect, and we analyze their large- and finite-sample behavior.     

A consistent nonparametric test for nonlinear causality—Specification in time series regression

Since the pioneering work by Granger (1969), many authors have proposed tests of causality between economic time series. Most of them are concerned only with “linear causality in mean”, or if a series linearly affects the (conditional) mean of the other series. It is no doubt of primary interest, but dependence between series may be nonlinear, and/or not only through the conditional mean. Indeed conditional heteroskedastic models are widely studied recently. The purpose of this paper is to propose a nonparametric test for possibly nonlinear causality. Taking into account that dependence in higher order moments are becoming an important issue especially in financial time series, we also consider a test for causality up to the Kth conditional moment. Statistically, we can also view this test as a nonparametric omitted variable test in time series regression. A desirable property of the test is that it has nontrivial power against T^1/2-local alternatives, where T is the sample size. Also, we can form a test statistic accordingly if we have some knowledge on the alternative hypothesis. Furthermore, we show that the test statistic includes most of the omitted variable test statistics as special cases asymptotically. The null asymptotic distribution is not normal, but we can easily calculate the critical regions by simulation. Monte Carlo experiments show that the proposed test has good size and power properties.     

Quantile models and estimators for data analysis

Quantile regression is used to estimate the cross sectional relationship between high school characteristics and student achievement as measured by ACT scores. The importance of school characteristics on student achievement has been traditionally framed in terms of the effect on the expected value. With quantile regression the impact of school characteristics is allowed to be different at the mean and quantiles of the conditional distribution. Like robust estimation, the quantile approach detects relationships missed by traditional data analysis. Robust estimates detect the influence of the bulk of the data, whereas quantile estimates detect the influence of co-variates on alternate parts of the conditional distribution. Since our design consists of multiple responses (individual student ACT scores) at fixed explanatory variables (school characteristics) the quantile model can be estimated by the usual regression quantiles, but additionally by a regression on the empirical quantile at each school. This is similar to least squares where the estimate based on the entire data is identical to weighted least squares on the school averages. Unlike least squares however, the regression through the quantiles produces a different estimate than the regression quantiles.     

Extreme returns and intensity of trading

Asymmetric information models of market microstructure claim that variables such as trading intensity are proxies for latent information on the value of financial assets. We consider the interval‐valued time series (ITS) of low/high returns and explore the relationship between these extreme returns and the intensity of trading. We assume that the returns (or prices) are generated by a latent process with some unknown conditional density. At each period of time, from this density, we have some random draws (trades) and the lowest and highest returns are the realized extreme observations of the latent process over the sample of draws. In this context, we propose a semiparametric model of extreme returns that exploits the results provided by extreme value theory. If properly centered and standardized extremes have well‐defined limiting distributions, the conditional mean of extreme returns is a nonlinear function of the conditional moments of the latent process and of the conditional intensity of the process that governs the number of draws. We implement a two‐step estimation procedure. First, we estimate parametrically the regressors that will enter into the nonlinear function, and in a second step we estimate nonparametrically the conditional mean of extreme returns as a function of the generated regressors. Unlike current models for ITS, the proposed semiparametric model is robust to misspecification of the conditional density of the latent process. We fit several nonlinear and linear models to the 5‐minute and 1‐minute low/high returns to seven major banks and technology stocks, and find that the nonlinear specification is superior to the current linear models and that the conditional volatility of the latent process and the conditional intensity of the trading process are major drivers of the dynamics of extreme returns.     

Robust feature screening for varying coefficient models via quantile partial correlation

This article is concerned with feature screening for varying coefficient models with ultrahigh-dimensional predictors. We propose a new sure independence screening method based on quantile partial correlation (QPC-SIS), which is quite robust against outliers and heavy-tailed distributions. Then we establish the sure screening property for the QPC-SIS, and conduct simulations to examine its finite sample performance. The results of simulation study indicate that the QPC-SIS performs better than other methods like sure independent screening (SIS), sure independent ranking and screening, distance correlation-sure independent screening, conditional correlation sure independence screening and nonparametric independent screening, which shows the validity and rationality of QPC-SIS.     

The Basu Measure as an Indicator of Conditional Conservatism: Evidence from UK Earnings Components

Following the work of Basu in 1997, the excess of the sensitivity of accounting earnings to negative share return over its sensitivity to positive share return (the Basu coefficient) has been interpreted as an indicator of conditional accounting conservatism. Although this interpretation is supported by substantial evidence that the Basu coefficient is associated with likely demands for conservatism, concerns have arisen that it may reflect factors not directly related to conservatism, and that this may adversely affect its validity as an indicator of that phenomenon. We argue that evidence on the validity of the Basu coefficient as an indicator of conditional conservatism can be obtained by disaggregating earnings into components, classifying those components by whether or not they are likely to be affected by conditional conservatism, and examining whether the Basu coefficient arises primarily from components likely to be affected by conditional conservatism. We implement this procedure for UK firms reporting under FRS 3: Reporting Financial Performance from 1992 to 2004. Although a substantial proportion of the Basu coefficient emanates from cash flow from operating and investing activities (CFOI), which cannot directly reflect accounting conservatism, its incidence across other components of earnings is predominantly within those components likely to be affected by conditional conservatism. Also, although the bias documented by Patatoukas and Thomas in 2009 is present in all of our aggregate earnings measures, it is heavily concentrated in the CFOI component of earnings and largely absent from components classified as likely to be affected by conditional conservatism. With the important caveat that researchers should test the robustness of their results to the exclusion of the element of the Basu coefficient due to cash flows, our findings are consistent with the conditional conservatism interpretation of the coefficient.     

Instrumental variable estimation in the presence of many moment conditions

This paper develops shrinkage methods for addressing the “many instruments” problem in the context of instrumental variable estimation. It has been observed that instrumental variable estimators may behave poorly if the number of instruments is large. This problem can be addressed by shrinking the influence of a subset of instrumental variables. The procedure can be understood as a two-step process of shrinking some of the OLS coefficient estimates from the regression of the endogenous variables on the instruments, then using the predicted values of the endogenous variables (based on the shrunk coefficient estimates) as the instruments. The shrinkage parameter is chosen to minimize the asymptotic mean square error. The optimal shrinkage parameter has a closed form, which makes it easy to implement. A Monte Carlo study shows that the shrinkage method works well and performs better in many situations than do existing instrument selection procedures.     

Automatic neural network modeling for univariate time series

Artificial neural networks (ANNs) are an information processing paradigm inspired by the way the brain processes information. Using neural networks requires the investigator to make decisions concerning the architecture or structure used. ANNs are known to be universal function approximators and are capable of exploiting nonlinear relationships between variables. This method, called Automated ANNs, is an attempt to develop an automatic procedure for selecting the architecture of an artificial neural network for forecasting purposes. It was entered into the M-3 Time Series Competition. Results show that ANNs compete well with the other methods investigated, but may produce poor results if used under certain conditions.     

Co‐integration Rank Determination in Partial Systems Using Information Criteria

We investigate the asymptotic and finite sample properties of the most widely used information criteria for co‐integration rank determination in ‘partial’ systems, i.e. in co‐integrated vector autoregressive (VAR) models where a sub‐set of variables of interest is modelled conditional on another sub‐set of variables. The asymptotic properties of the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the Hannan–Quinn information criterion (HQC) are established, and consistency of BIC and HQC is proved. Notably, the consistency of BIC and HQC is robust to violations of weak exogeneity of the conditioning variables with respect to the co‐integration parameters. More precisely, BIC and HQC recover the true co‐integration rank from the partial system analysis also when the conditional model does not convey all information about the co‐integration parameters. This result opens up interesting possibilities for practitioners who can now determine the co‐integration rank in partial systems without being concerned about the weak exogeneity of the conditioning variables. A Monte Carlo experiment based on a large dimensional data generating process shows that BIC and HQC applied in partial systems perform reasonably well in small samples and comparatively better than ‘traditional’ methods for co‐integration rank determination. We further show the usefulness of our approach and the benefits of the conditional system analysis in two empirical illustrations, both based on the estimation of VAR systems on US quarterly data. Overall, our analysis shows the gains of combining information criteria with partial system analysis.     

基于人工神经网络与主成分分析的建筑工料(工日)估算方法

针对建筑工料(工日)估算问题,文章首先利用主成分分析得到一组新的输入变量,相对于原始输入变量,有效降低了输入维数,且消除了各输入分量之间的相关性,然后以新的输入变量作为改进型BP网络的输入进行训练与估算,得到了一种新的工料(工日)估算方法。仿真结果表明与直接利用BP网络训练估算相比较,采用这种估算方法,估算结果更加准确.     

基于Copula理论的宏观经济与股票市场相关性研究

为了刻画宏观经济与股票市场波动间的相关性,在静态Copula模型的基础上,应用了一种全新的条件动态Copula(DCC-Copula)技术,它可以捕捉到经济变量间动态的相关结构。结合Gaussian-GARCH模型和DCC-Copula函数,建立了DCC Copula-GARCH模型全面对宏观经济变量与股票市场之间相关性进行了分析。结果说明,随着时间的变化,宏观经济与股票市场波动之间存在着较稳定的正相关关系。     

On the equivalence of location choice models: Conditional logit, nested logit and Poisson

It is well understood that the two most popular empirical models of location choice - conditional logit and Poisson - return identical coefficient estimates when the regressors are not individual specific. We show that these two models differ starkly in terms of their implied predictions. The conditional logit model represents a zero-sum world, in which one region’s gain is the other regions’ loss. In contrast, the Poisson model implies a positive-sum economy, in which one region’s gain is no other region’s loss. We also show that all intermediate cases can be represented as a nested logit model with a single outside option. The nested logit turns out to be a linear combination of the conditional logit and Poisson models. Conditional logit and Poisson elasticities mark the polar cases and can therefore serve as boundary values in applied research.     

A note on exchangeability of random variables

A sufficient condition for the induced exchangeability or partial exchangeability of linear functions of exchangeable random variables is presented. The use of this result is illustrated through the establishment of conditional exchangeability for two sets of dependent random variables that are important in constructing conditionally distribution–free test procedures for two distinctly different problems.     

Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables

I propose a quasi-maximum likelihood framework for estimating nonlinear models with continuous or discrete endogenous explanatory variables. Joint and two-step estimation procedures are considered. The joint procedure is a quasi-limited information maximum likelihood procedure, as one or both of the log likelihoods may be misspecified. The two-step control function approach is computationally simple and leads to straightforward tests of endogeneity. In the case of discrete endogenous explanatory variables, I argue that the control function approach can be applied with generalized residuals to obtain average partial effects. I show how the results apply to nonlinear models for fractional and nonnegative responses.     

Impact of the COVID-19 outbreak and its related announcements on the Chinese conventional and Islamic stocks’ connectedness

In this paper, we assess the impacts of the COVID-19 counts (infected cases, deaths and recovered) and related announcements on the Islamic and conventional stocks interplays in the Chinese market. We test whether Islamic stocks are perceived as assets providing diversification benefits in time of COVID-19 pandemic. Doing so, we implement a multivariate GJR-GARCH model under dynamic conditional correlation (DCC) as well as multiple and partial wavelet coherence methods to recent Chinese daily data ranging from 2 December 2019 to 8 May 2020 and COVID-19 related announcement for the period. Our results from multivariate GJR-GARCH models reveal that COVID-19 infected cases and deaths do impact mean DCCs between Islamic and conventional stocks, number of recovered do not have such impact, while none of the above have any significant impact on the DCCs fluctuations. However, when we analyze the impact of COVID-19 related announcement on the variation of conditional correlation between two stocks (i.e. DCC volatility) our findings show that 7 out of 10 such announcements (mainly those with serious health treats or economic implications) do effect those volatilities in Chinese equity market. The empirical findings from partial and multiple wavelet coherences provide robust evidence of instability in the co-movement between Islamic and conventional indexes for different scales and over dissimilar sub-periods. Indeed, the weakening of co-movements is especially notable in the very short and short-run where operating the short-term investors. Our empirical findings offer several key propositions for policy makers and portfolio managers in China with broad implications applicable to other markets.     

Nonlinear Correlograms and Partial Autocorrelograms*

This paper proposes neural network‐based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely on nonparametric estimation of densities or multivariate integration, our autocorrelograms are simple to calculate and appear to work well in relatively small samples.     

Evaluating the conditionality of judgmental forecasts

We propose a framework for evaluating the conditionality of forecasts. The crux of our framework is the observation that a forecast is conditional if revisions to the conditioning factor are incorporated faithfully into the remainder of the forecast. We consider whether the Greenbook, Blue Chip survey and Survey of Professional Forecasters exhibit systematic biases in the manner in which they incorporate interest rate projections into the forecasts of other macroeconomic variables. We do not find strong evidence of systematic biases in the three economic forecasts that we consider, as the interest rate projections in these forecasts appear to be incorporated efficiently into the forecasts of other economic variables.     

Short-selling restrictions,takeovers and the wealth of long-run shareholders

In this paper we consider a situation in which a firm may be able to influence the investors’ ability to short-sell its stock. We analyze the effect short-selling restrictions have on the market price and the subsequent effect generated on the market for corporate control. More precisely, we argue that short-selling restrictions may lead to exclusion of pessimistic beliefs and may therefore inflate prices. Thus, if a company is poorly managed and has a stock with strong short-selling restrictions, a profitable takeover will not emerge because of the high stock price. The raider may not have the incentives to acquire the company as its price will be above its fundamental value, conditional on takeover, even accounting for the potential benefits of takeover. We then argue that such effects are detrimental to long-run shareholders and that a value-maximizing strategy is to have a stock with no short-selling restrictions.     

A neural network demand system with heteroskedastic errors

In this paper we consider estimation of demand systems with flexible functional forms, allowing an error term with a general conditional heteroskedasticity function that depends on observed covariates, such as demographic variables. We propose a general model that can be estimated either by quasi-maximum likelihood (in the case of exogenous regressors) or generalized method of moments (GMM) if the covariates are endogenous. The specification proposed in the paper nests several demand functions in the literature and the results can be applied to the recently proposed Exact Affine Stone Index (EASI) demand system of [Lewbel, A., Pendakur, K., 2008. Tricks with Hicks: The EASI implicit Marshallian demand system for unobserved heterogeneity and flexible Engel curves. American Economic Review (in press)]. Furthermore, flexible nonlinear expenditure elasticities can be estimated.