Dynamic Factor Models with Control Variables

Understanding the hydrological functioning of tidally influenced floodplain forests is essential for advancing ecosystem protection and restoration goals in impacted systems. However, finding direct relationships between basic hydrological inputs and floodplain hydrology is hindered by complex interactions between surface water, groundwater, and atmospheric fluxes in a variably saturated matrix with heterogeneous soils, vegetation, and topography. Thus, an explanatory method for identifying common trends and causal factors is required. Dynamic factor analysis (DFA), a time series dimension reduction technique, models temporal variation in observed data as linear combinations of common trends, which represent unidentified common factors, and explanatory variables. In this work, DFA was applied to model water table elevation (WTE) in the floodplain of the Loxahatchee River (Florida, USA), where altered watershed hydrology has led to changing hydroperiod and salinity regimes and undesired vegetative changes in the floodplain forest. The technique proved to be a powerful tool for the study of interactions among 29 long‐term, nonstationary hydrological time series (12 WTE series and 17 candidate explanatory variables). Regional groundwater circulation, surface water elevations, and spatially variable net local recharge (cumulative rainfall – cumulative evapotranspiration) were found to be the main factors explaining groundwater profiles. The relative importance of these factors was spatially related to floodplain elevation, distance from the river channel, and distance upstream from the river mouth. The resulting dynamic factor model (DFM) simulated the WTE time series well (overall coefficient of efficiency,Ceff= 0.91) and is useful for assessing management scenarios for ecosystem restoration and predicted sea level rise.

To model processes we propose merging idiographic filter measurement with dynamic factor analysis. This involves testing whether or not the same latent dynamics (concurrent and lagged factor interrelations) can describe different individuals' observed multivariate time series. The methodology allows fitting, across different individuals, dynamic factor models that are invariant with respect to the latent dynamics, but not necessarily the factor loadings (measurement model). This methodology allows the same latent process to manifest differently from one individual to another, thus recognizing that the process is general but its realization in a given person is to some degree idiosyncratic. The approach is illustrated with empirical data.

The main objective of this paper is the development and application of multivariate time series models for forecasting aggregated wind power production in a country or region. Nowadays, in Spain, Denmark or Germany there is an increasing penetration of this kind of renewable energy, somehow to reduce energy dependence on the exterior, but always linked with the increase and uncertainty affecting the prices of fossil fuels. The disposal of accurate predictions of wind power generation is a crucial task both for the System Operator as well as for all the agents of the Market. However, the vast majority of works rarely consider forecasting horizons longer than 48 hours, although they are of interest for the system planning and operation. In this paper we use Dynamic Factor Analysis, adapting and modifying it conveniently, to reach our aim: the computation of accurate forecasts for the aggregated wind power production in a country for a forecasting horizon as long as possible, particularly up to 60 days (2 months). We illustrate this methodology and the results obtained for real data in the leading country in wind power production: Denmark.

Dynamic mortality factor model with conditional heteroskedasticity

In the last decade, most studies on the interaction between the financial sector and economic growth has focused mainly on the banks and the stock market. Recently, growing attention has shifted to the interaction between the non-bank financial intermediaries (NBFIs) such as the insurance companies and economic growth. In this study, we examined the impact of insurance contributions on economic growth in Nigeria over a twenty seven year period, between 1981 and 2008 using a dynamic factor model. The proposed technique described a number of methods designed to analyze a functional relationship between the volume of insurance contribution and economic growth in terms of underlying but unobservable random quantities called factors. The factor loadings indicate which common trend is related to which set of time series. The result obtained through this approach shows that the functional relationship between the volume of insurance contribution and economic growth in Nigeria is a first order vector autoregressive model. Key words: Insurance contribution, dynamic factor model, economic growth.

Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model

Feeding system modelling is the foundation for control strategy optimization, contour error compensation, etc., to improve the productivity and quality of a part. This paper proposes a novel residual learning approach for fitting the simulation error of the dynamics model of a machine tool feeding system. Then, the feeding system model consisting of the dynamics model and the residual model is constructed by integrating prior knowledge with statistical learning knowledge. The residual model is trained by using the training dataset generated from the dynamics model instead of using only the input and output data (i.e., the end-to-end data). In addition, a dynamic nonlinear correlate factor extraction method is proposed to extract the training dataset from the dynamics model and the reference data. Compared to the end-to-end data, the training dataset knows very well about the system’s nonlinear features owing to the internal prior knowledge of the dynamics model. Experiments conducted on a vertical milling centre confirm the effectiveness of the feeding system model in dynamic response prediction. Compared to the existing dynamics or data-driven modelling method, the proposed method can achieve higher prediction accuracy in nonlinear motion processes, such as the reverse process, and can obtain stable performance with respect to different feedrates owing to the residual learning approach.

Improved index insurance design and yield estimation using a dynamic factor forecasting approach

Dynamics of processes in the chemical industries requires substantiation of new approaches to vocational training programs for future mechanical engineers. These scientific and practical innovations should affect the entire system of training, spreading, primarily on professionally oriented disciplines, leading among them for direction « Mechanical Engineering » specialty « chemical production equipment and construction materials » is « processes, machines and machine area » . The paper presents a system-factor approach to enhance the training of future mechanical engineers in the direction of « Engineering » specialty « chemical production equipment and building materials » , the discipline « processes, machines and machine industry » on the basis of system-factor organizing training tasks, which are purposefully reinforce this training for each conditionally selected for the leading function of work, professional category: « Technology » , « mechanics » , « designers » . So, the training of mechanical engineers in the direction of « Engineeing » specialty « chemical production equipment and building materials » should be aimed at the preparation of various experts said the work on the classification of professional groups. Each of the factors proposed system-factor approach to discipline « processes, machines and machine industry » to enhance the training of future mechanical engineers in the direction of « Engineering » is given and should be taken into account, but if necessary, subjected to the necessary adjustments. Conditional inclusion of subjective factors in the objective-subjective, and those – in an objective manner the static measurement of the factor model application system of learning tasks in the preparation of future mechanical engineers specialty « chemical production equipment and building materials » , the discipline « processes, machines and machine industry » . The dynamic factor model as the measurement system application training tasks forms a gradual taking into account the following factors each with a mandatory check the completeness of all previous records. Thus, the implementation of system-factor approach for future mechanical engineers in the direction of « Engineering » specialty « chemical production equipment and building materials » , the discipline « processes, machines and machine industry » to contribute to enhance their professional skills in the specialty for each conditionally selected for leading function of employment, Professional, « technology » , « mechanics » , « designers » . system-factor approach; a mechanical engineer; equipment; chemicals manufacturing and construction materials; engineering direction, objectives; factors, system-factor model

Dynamic Factor Models with Clustered Loadings: Forecasting Education Flows using Unemployment Data

Cross-sector comovements and policy impact in the COVID-19 stock market: A dynamic factor approach

Cold-start problem is a key challenge in recommender system. In order to solve this problem, we proposed a dynamic latent factor model that combined with attributes of user and item. With the increase of interaction between user and recommender system, our model can automatically adjust its coefficient to achieve a more reasonable and more satisfactory recommendation. The experimental results show that the proposed method can not only alleviate the cold-start problem of new users and new items, but also improve the recommendation accuracy to some extent.

In the context of the exact dynamic common factor model, canonical correlations in a multivariate time series are used to identify the number of latent common factors. In this paper, we establish a relationship between canonical correlations and the autocovariance function of the factor process, in order to modify a pre-established statistical test to detect the number of common factors. In particular, the test power is increased. Additionally, we propose a procedure to identify a vector ARMA model for the factor process, which is based on the so-called simple and partial canonical autocorrelation functions. We illustrate the proposed methodology by means of some simulated examples and a real data application.

In this contribution we present a structure theory for generalized linear dynamic factor models. Generalized dynamic factor models have been proposed approximately a decade ago for modeling of high dimensional time series where the cross sectional dimension is of the same order of magnitude as the sample size. In these models the classical assumption for factor models, that the noise components are mutually uncorrelated, is relaxed by allowing for weak dependence. Structure theory turns out to be important for estimation and model selection. The results obtained heavily draw from linear system theory.The contribution consists of two main parts. In the first part we deal with “denoising”, i.e. with getting rid of the noise in the observations. In the second part we deal with constructing linear dynamic systems for the latent variables. Here an important result is the generic zerolessness of the transfer function relating the latent variables and the dynamic factors. This allows for modeling the latent variables by (singular) autoregressions which simplifies estimation.

A viaduct is an important structural form in intercity railway networks, and the curved bridge is inevitable in railway line network planning. When the vehicles cross through the curved bridge, the transverse load experienced by the bridge is much larger than that of the straight bridge due to the centrifugal forces. Therefore, in the bridge resonance analysis, in addition to the vertical moving vehicle load, it is also imperative to consider the radial vehicle load. In order to systematically analyze the dynamic impact factor (or dynamic load allowance) and resonance conditions of the curved intercity railway viaduct, a numerical model is developed. The reliability of the proposed model is assessed by comparing it with the existing literature. Taking the curved viaduct of Zhengzhou-Xuchang railway as a case study, the theoretical resonance velocity, dynamic impact factor, and dynamic time-history response under resonance velocity are calculated. The parameters of different spans, curve radius, and speeds are analyzed, and the reasonable ranges of bridge natural vibration frequency are determined.