Continuous time modeling of panel data: SEM versus filter techniques

Although convincing arguments have been put forward for continuous-time modeling, its use in panel research is rare. In one approach, classical N  = 1 state-space modeling procedures are adapted for panel analysis to estimate the exact discrete model (EDM) by means of filter techniques. Based on earlier less satisfactory indirect methods, a more recent approach uses structural equation modeling (SEM) to get the maximum likelihood estimate of the EDM by the direct method. After an introduction into continuous-time state-space modeling for panel data and the EDM, a thorough comparison is made between the two distinct approaches with quite different histories by means of Monte Carlo simulation studies. The model used in the simulation studies is the damped linear oscillator with and without random subject effects.     

Assessing the relationships between Nationalism, Ethnocentrism, and Individualism in Flanders using Bergstrom's approximate discrete model

Although Nationalism, Ethnocentrism, and Individualism in Flanders have been the subject of several studies before, a longitudinal analysis has not been performed on all three concepts simultaneously nor have their relationships and the direction of their relationships been studied in continuous time. In this study we performed a continuous-time state-space analysis on panel data collected from 1274 subjects, in the years 1991, 1995 and 1999. The LISREL program is used for estimating the approximate discrete model (ADM), and for comparison, also the exact discrete model (EDM) is estimated by means of the Mx program. Details of continuous time modeling, especially the EDM and ADM, are dealt with. Individualism and Ethnocentrism turn out to be connected in a moderately strong feedback relationship with the effect from Individualism towards Ethnocentrism somewhat stronger than that in the opposite direction. Both Individualism and Ethnocentrism have small effects on Nationalism. The autoregression functions, cross-lagged effect functions, and mean predictions are shown.     

A new marked point process model for the federal funds rate target: Methodology and forecast evaluation

Forecasts of key interest rates set by central banks are of paramount concern for investors and policy makers. Recently it has been shown that forecasts of the federal funds rate target, the most anticipated indicator of the Federal Reserve Bank's monetary policy stance, can be improved considerably when its evolution is modeled as a marked point process (MPP). This is due to the fact that target changes occur in discrete time with discrete increments, have an autoregressive nature and are usually in the same direction. We propose a model which is able to account for these dynamic features of the data. In particular, we combine Hamilton and Jordà's [2002. A model for the federal funds rate target. Journal of Political Economy 110(5), 1135–1167] autoregressive conditional hazard (ACH) and Russell and Engle's [2005. A discrete-state continuous-time model of financial transactions prices and times: the autoregressive conditional multinomial-autoregressive conditional duration model. Journal of Business and Economic Statistics 23(2), 166 – 180] autoregressive conditional multinomial (ACM) model. The paper also puts forth a methodology to evaluate probability function forecasts of MPP models. By improving goodness of fit and point forecasts of the target, the ACH–ACM qualifies as a sensible modeling framework. Furthermore, our results show that MPP models deliver useful probability function forecasts at short and medium term horizons.     

Space-varying Coefficient Simultaneous Autoregressive Models for the Structural Analysis of Residential Water Demand

Abstract

This study develops two space-varying coefficient simultaneous autoregressive (SVC-SAR) models for areal data and applies them to the discrete/continuous choice model, which is an econometric model based on the consumer's utility maximization problem. The space-varying coefficient model is a statistical model in which the coefficients vary depending on their location. This study introduces the simultaneous autoregressive model for the underlying spatial dependence across coefficients, where the coefficients for one observation are affected by the sum of those for the other observations. This model is named the SVC-SAR model. Because of its flexibility, we use the Bayesian approach and construct its estimation method based on the Markov chain Monte Carlo simulation. The proposed models are applied to estimate the Japanese residential water demand function, which is an example of the discrete/continuous choice model.     

Econometric modelling in finance and risk management: An overview

This paper gives an overview about the sixteen papers included in this special issue. The papers in this special issue cover a wide range of topics. Such topics include discussing a class of tests for correlation, estimation of realized volatility, modeling time series and continuous-time models with long-range dependence, estimation and specification testing of time series models, estimation in a factor model with high-dimensional problems, finite-sample examination of quasi-maximum likelihood estimation in an autoregressive conditional duration model, and estimation in a dynamic additive quantile model.     

Forecasting realized variance measures using time-varying coefficient models

This paper considers the problem of forecasting realized variance measures. These measures are highly persistent estimates of the underlying integrated variance, but are also noisy. Bollerslev, Patton and Quaedvlieg (2016), Journal of Econometrics 192(1), 1–18 exploited this so as to extend the commonly used heterogeneous autoregressive (HAR) by letting the model parameters vary over time depending on the estimated measurement error variances. We propose an alternative specification that allows the autoregressive parameters of HAR models to be driven by a latent Gaussian autoregressive process that may also depend on the estimated measurement error variance. The model parameters are estimated by maximum likelihood using the Kalman filter. Our empirical analysis considers the realized variances of 40 stocks from the S&P 500. Our model based on log variances shows the best overall performance and generates superior forecasts both in terms of a range of different loss functions and for various subsamples of the forecasting period.     

Measurement models for linking latent variables and indicators: A review of human resource management research using parcels

This paper first reviews the various measurement model options for linking latent variables to indicators that are available to human resource management (HRM) researchers. A special emphasis is placed on the option of parcels, created by combining subsets of items to form indicators. Next, a review is presented of 27 articles from the major HRM journals that have used parcels, with a focus on conceptual/theoretical and empirical issues. Recommendations for HRM researchers for improved use of parcels are also provided.     

Multivariate contemporaneous-threshold autoregressive models

This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex-ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific noise covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates.     

Ecological-Inference-Based Latent Growth Models: Modeling Changes of Alienation

Ecological-inference-based statistical methods employ aggregated (ecological) data to approximately infer individual-level structures of interests when individual-level data were not available. Under the same conceptual frames, we introduce the ecological-inference-based latent growth model (EI-LGM) to analyze cross-years latent trends of a general population when longitudinally collected data were not available. We showed both the substantive values and methodological feasibilities of EI-LGMs. Substantively, we analyze results from several Taiwan Social Change Surveys (TSCS) to show the cross-years latent trends using a subscale of alienation psychological characteristics. Not only the cross-years movements of measurement constructs of the scale were shown, the trends of latent factors were revealed as well. More importantly, these trends can be formally tested under the frameworks of EI-LGMs. Statistically, EI-LGMs were implemented under the weighted least square (WLS) approaches because of the dichotomous outcomes of the subscale. We demonstrate some of the estimation methods as well as some cautions of interpreting EI-LGMs using the estimated results.Part of this paper was presented at the Fourth Survey Research Conference held at the Academia Sinica, Taipei, August 29–August 30, 2002.     

Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.     

A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects

We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuous-time components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency intraday data. The model setup allows us to directly assess the structural inter-dependencies among the shocks to returns and the two different volatility components. The model estimates suggest that the leverage effect, or asymmetry between returns and volatility, works primarily through the continuous volatility component. The excellent fit of the model makes it an ideal candidate for an easy-to-implement auxiliary model in the context of indirect estimation of empirically more realistic continuous-time jump diffusion and Lévy-driven stochastic volatility models, effectively incorporating the interdaily dependencies inherent in the high-frequency intraday data.     

Structural equation modeling and the latent linearity hypothesis in social and behavioral research

The issue of sensitivity of the structural equation modeling (SEM) methodology to violations of the underlying hypothesis of linear latent relationships is the focus of this paper. The identity of overall goodness-of-fit indices of an initially considered linear latent pattern model and of an equivalent model not making this assumption exemplifies the lack of routinely available global means within the methodology to evaluate the linearity assumption. It is next focused on the sensitivity of SEM to violations of presumed linearity for a general, nonlinear pattern of true relationship. The results of a simulation study are then presented which demonstrate that latent correlations and percentage explained variance as well as parameter standard errors and model residuals can provide critical information about violation of latent linearity, and should therefore also be focused on when examining departures from linear relationships at the latent level in applications of the SEM methodology in social and behavioral research.     

A PLS path model to investigate the relations between institutions and human development

The paper studies the relations between institutions and human development, in particular the causal effects of the different types of institutions on different components of human development. We assume development to be created by aggregate demand; in particular that aggregate demand determines the material components of human development. We thus divide institutions into those that create demand and those that are determined by the whole process of development. Similarly we divide human development in its three traditional components (economic development, health, knowledge). Both human development and institutions are assumed to be multidimensional constructs; all the main components of these constructs are defined as latent variables, and the relations between them as structural relations. A partial least squares (PLS) path model is developed: it is the aggregation (and simultaneous estimation) of an outer model relating observed or manifest variables to their own latent variable and of a structural model (inner model) relating some endogenous latent variable to other latent variables. From the goodness of fit point of view, our results seem to validate our theoretical assumptions.     

A reservation-based parking behavioral model for parking demand management in urban areas

The rapid increase in car ownership has become a land-use problem in many cities which have limited land for parking. It also has adverse impacts on congestion, air quality, energy consumption, as well as losses in productivity, among other consequences. The problem worsens in some cities due to the adoption of inefficient mandatory minimum parking policies for new projects, according to a building's use and size, which incentivizes driving. Therefore, it is necessary to assess appropriate parking management policies that maximize social welfare. In this regard, this paper presents a reservation-based parking behavioral hybrid choice model for parking demand management policies in urban areas, that appropriately represents the behavior of private vehicle users when choosing their parking site. The proposed model is statistically significant and consistent with expected behavior and microeconomic theory. The results demonstrate that the possibility of reserving a parking space has a significant impact on car parking market share on- and off-street. The three latent variables included in the model (pro-parking attitude, perception of risk for parking on- and off-street) have a significant effect on the modal split and the evaluation of appropriate parking policies that consider heterogeneity. The latent variable that most impacts the modal shift is the pro-parking attitude, which can generate up to 7% change in the modal partition.     

基于SEM模型的汽车产业集群及其供应链耦合影响因素分析

产业集群和供应链耦合发展是推动区域经济协调发展的重要路径。以汽车产业为例,在相关文献研究的基础上,从经济和技术两个角度阐述影响产业集群与供应链耦合的主要因素,构建结构方程模型(SEM),运用AMOS软件,分析各影响因素的内在关系,并提出相关建议,以促进区域经济的协调发展。     

Customer satisfaction of bicycle sharing: studying perceived service quality with SEM model

ABSTRACT

Bicycle sharing is an emerging business in many cities worldwide and has attracted a large number of users, due to its convenience, environmental friendliness, low cost, and flexibility for short-distance travels. This study evaluates main factors affecting the perceived service quality, satisfaction, and loyalty of bicycle sharing customers. We design measurement variables, conduct a satisfactory survey on customers using OFO bicycle sharing service in Ningbo, China, develop a Service-Satisfaction-Loyalty model using structure equation modeling (SEM), and conduct statistical analysis. The perceived service quality was simplified to three dimensions including: perceived service quality of platform, perceived quality of bicycle entity, and perceived quality of value. The results show that SEM model can account for commonly unobserved variables within satisfaction and loyalty of bicycle-sharing. Perceived services quality of bicycle entity and platform are found to affect customer satisfaction significantly, while perceived quality of value is not a significant factor. This study confirms that satisfaction decisively leads to loyalty of bicycle sharing.     

Recent developments of the autoregressive distributed lag modelling framework

We review the literature on the autoregressive distributed lag (ARDL) model, from its origins in the analysis of autocorrelated trend stationary processes to its subsequent applications in the analysis of cointegrated non-stationary time series. We then survey several recent extensions of the ARDL model, including asymmetric and non-linear generalisations of the ARDL model, the quantile ARDL model, the pooled mean group dynamic panel data model and the spatio-temporal ARDL model.     

Contemporaneous threshold autoregressive models: Estimation,testing and forecasting

This paper proposes a contemporaneous smooth transition threshold autoregressive model (C-STAR) as a modification of the smooth transition threshold autoregressive model surveyed in Teräsvirta [1998. Modelling economic relationships with smooth transition regressions. In: Ullah, A., Giles, D.E.A. (Eds.), Handbook of Applied Economic Statistics. Marcel Dekker, New York, pp. 507–552.], in which the regime weights depend on the ex ante probability that a latent regime-specific variable will exceed a threshold value. We argue that the contemporaneous model is well suited to rational expectations applications (and pricing exercises), in that it does not require the initial regimes to be predetermined. We investigate the properties of the model and evaluate its finite-sample maximum likelihood performance. We also propose a method to determine the number of regimes based on a modified Hansen [1992. The likelihood ratio test under nonstandard conditions: testing the Markov switching model of GNP. Journal of Applied Econometrics 7, S61–S82.] procedure. Furthermore, we construct multiple-step ahead forecasts and evaluate the forecasting performance of the model. Finally, an empirical application of the short term interest rate yield is presented and discussed.     

Stability results for nonlinear error correction models

This paper studies analogs of Granger's representation theorem in the context of a general nonlinear vector autoregressive error correction model. The model allows for nonlinear autoregressive conditional heteroskedasticity and the conditional distribution involved can be a mixture distribution of a rather general type. Mixture models of this kind can be thought of as generalizations of threshold models and they have attracted attention in the recent time series and econometrics literature. The paper develops a useful transformation which shows how the nonlinear error correction model can be transformed to a nonlinear vector autoregressive model so that available results on the stationarity or nonstationarity of the latter can be used for the former. The most satisfactory results are obtained in a model in which a specific structural relation between the nonlinearity and equilibrium correction prevails. Without this structural relation only a lower bound for the number of long-run equilibrium relations can explicitly be determined because the exact number depends on properties of the first and second moments of a nonlinear stationary component of the process.     

Quasi-longitudinal Designs in SEM state Space Modeling

The collection of longitudinal data over the full model time scope is often an appropriate way to estimate the dynamic state space model with time-varying parameters. Nevertheless, in many situations it is possible and preferable to collect and combine data from independent groups of subjects, each covering a shorter interval than the full dynamic model. Several quasi-longitudinal designs are discussed: overlapping designs (overlapping cohort design OCD and overlapping samples design OSD) as well as nonoverlapping designs up to the exclusively cross-sectional design. The use of the structural equation modeling (SEM) program Mx and continuous time state space modeling is recommended. Finally, a number of the quasi-longitudinal designs is empirically evaluated, comparing the results with those of the full-longitudinal design.