Forecasting macroeconomic variables using collapsed dynamic factor analysis

We explore a new approach to the forecasting of macroeconomic variables based on a dynamic factor state space analysis. Key economic variables are modeled jointly with principal components from a large time series panel of macroeconomic indicators using a multivariate unobserved components time series model. When the key economic variables are observed at a low frequency and the panel of macroeconomic variables is at a high frequency, we can use our approach for both nowcasting and forecasting purposes. Given a dynamic factor model as the data generation process, we provide Monte Carlo evidence of the finite-sample justification of our parsimonious and feasible approach. We also provide empirical evidence for a US macroeconomic dataset. The unbalanced panel contains quarterly and monthly variables. The forecasting accuracy is measured against a set of benchmark models. We conclude that our dynamic factor state space analysis can lead to higher levels of forecasting precision when the panel size and time series dimensions are moderate.     

Technical efficiency and region The U.S. manufacturing sector 1972–1977

In this paper we estimate the extent of technological bias in an interregional context for U.S. manufacturing during the period 1972–1977 using a factor augmenting production function approach. We present estimates of the elasticity of factor substitution for each of the 48 states in the sample using a variable elasticity of substitution production function. Next, we use these estimates to generate estimates of the rates of change in the efficiencies of capital and labor inputs and compare these estimates across states and census regions. We also examine and compare estimates of total factor productivity across states and regions. We ,find that the average annual rates of growth of capital efficiency during the period are 5.5 percent in the Northeast, 5.3 percent in the Northcentral, 5.6 percent in the West and 3.1 percent in the South. The rates of change of the efficiencies of labor are found to be negative across all regions except the South. The rates of change of total factor productivity are found to be 1.7 percent in the Northeast, 2.3 percent in the Northcentral, 2.4 percent in the West and 1.7 percent in the South. We also find that between 33 percent and 56 percent of the growth of output across regions is due to technical progress.     

A structural equation approach to measuring technological change: An application to Southeastern U.S. Agriculture

The study demonstrates a method of obtaining consistent estimates of technological change. This was accomplished by extending Theil's differential input demand system into a Multiple Indicator, Multiple Cause (MIMIC) structural equation latent variable model which explicitly accounts for the fact that technological progress is not directly observed and therefore is invariably measured with error. Results obtained for the agricultural sector of the Southeastern states suggest that technological change has been an important determinant of agricultural production and is bias towards the use of capital and against the use of land and labor.This research was partially supported by the McKethan-Matherly Eminent Scholar Chair, graduate school of Business Administration, University of Florida.     

Estimating the output gap in real time: A factor model approach

By using a dynamic factor model, we can substantially improve the reliability of real-time output gap estimates for the U.S. economy. First, we use a factor model to extract a series for the common component in GDP from a large panel of monthly real-time macroeconomic variables. This series is immune to revisions to the extent that revisions are due to unbiased measurement errors or idiosyncratic news. Second, our model is able to handle the unbalanced arrival of the data. This yields favorable nowcasting properties and thus starting conditions for the filtering of data into a trend and deviations from a trend. Combined with the method of augmenting data with forecasts prior to filtering, this greatly reduces the end-of-sample imprecision in the gap estimate. The increased precision has economic importance for real-time policy decisions and improves real-time inflation forecasts.     

The performance of coal-burning electric generating units in the United States: 1960–1980

This paper studies determinants of the thermal efficiency and reliability of coal-burning electric generating units using a new, comprehensive, unbalanced panel data set. For two major technological groups consistent and efficient estimates are obtained of the effects of unit age, vintage, scale, operating practices, and coal quality. Large utilities integrated into design and engineering appear to obtain superior unit performance. Implications of our results for life-cycle generation costs, regulators' performance norms, and the complex pattern of technological change in this industry are discussed.     

Estimating Technological Change Using a Stochastic Frontier Production Function Framework: Evidence from U.S. Firm-Level Data

This paper presents a methodology for estimating an index of technological change using firm-level data in a stochastic frontier production function model that takes into account time-varying technical inefficiency. In contrast to the Solow divisia index approach, econometric estimation of the index with panel data allows the researcher to separate technical progress from the stochastic measurement error. Applying the econometric methodology to a panel of 908 publicly-traded U.S. firms from the COMPUSTAT database, we find evidence of a significant downturn in general technological change for the period, 1970– 1989, whereas the divisia index methodology applied to the same data shows stagnation. When the sample is divided into Manufacturing, Services, and Miscellaneous categories we find that estimates of technological change for the three groups display markedly different stochastic behavior and that the Services group is the source of the downturn.     

Do independent,female and specialist directors promote eco-innovation and eco-design in agri-food firms?

Eco-innovation and eco-design strategies are associated with firms' innovation capabilities. Moreover, they may impact on access to public subsidies and on financial performance. In this respect, the agri-food industry is especially vulnerable, because in general, this sector has less experience of technological innovation, and managers are more likely to be averse to such projects. On the other hand, the board may promote a proactive environmental approach to defend the interests of investors and other stakeholders, taking the view that these strategies reduce the environmental impact of the firm's products and its production processes and are therefore beneficial. Our study aim is to identify the profile of directors who may be favourable to eco-design and eco-innovation strategies, focusing on the traits of independence, gender diversity and environmental specialisation. The results obtained, from a dependence model based on panel data supplied by 321 agri-food companies for the period 2002–2017 (unbalanced panel data with 4878 observations), show that independent directors play a crucial role in implementing eco-innovation and eco-design projects. However, neither the diversity nor the specialisation of directors is a significant factor in this regard.     

FDI对东道国内资企业技术进步影响作用的实证研究——基于苏州和宁波的比较

本文以Caves(1974)的经典模型为基础构建计量模型,以苏州、宁波两地的工业部门为研究对象,分析检验两地FDI对东道国内资企业技术进步的影响作用。分析结果表明,苏州的FDI对本土的内资企业的技术进步产生了显著的促进作用,而宁波的FDI所产生的作用非常微弱,并没有促进内资企业的生产率的提高。     

The measurement of left-right and libertarian-authoritarian values: A comparison of balanced and unbalanced scales

In this paper we compare the performance of balanced and unbalanced Likert scales of two core dimensions of political attitudes: left-right and libertarian-authoritarian values. The balanced scales control for the effects of acquiescence response sets, whereas the unbalanced scales do not. Using data from two panel surveys, balanced and unbalanced scales are compared for reliability, stability and validity both with each other, and with other measures of political ideology and values (left-right self-placement and postmaterialism). Both balanced and unbalanced versions of the left-right and libertarian-authoritarian Likert scales are found to be more stable and strongly associated with social characteristics than are the other measures. The unbalanced scales have slightly higher reliability than the balanced scales; they are also orthogonal, whereas balanced left-right and libertarian-authoritarian scales are moderately correlated. Unbalanced scales also display a slightly stronger relationship with social characteristics, but they do not differ in their pattern of association with political preferences. Differences between balanced and unbalanced scales are attributed to the effects of acquiescence bias. This needs to be considered when using the scales for substantive analyses, but its effects are not problematic.     

Half‐panel jackknife fixed‐effects estimation of linear panels with weakly exogenous regressors

This paper considers estimation and inference in linear panel regression models with lagged dependent variables and/or other weakly exogenous regressors when N (the cross‐section dimension) is large relative to T (the time series dimension). It allows for fixed and time effects (FE‐TE) and derives a general formula for the bias of the FE‐TE estimator which generalizes the well‐known Nickell bias formula derived for the pure autoregressive dynamic panel data models. It shows that in the presence of weakly exogenous regressors inference based on the FE‐TE estimator will result in size distortions unless N/T is sufficiently small. To deal with the bias and size distortion of the FE‐TE estimator the use of a half‐panel jackknife FE‐TE estimator is considered and its asymptotic distribution is derived. It is shown that the bias of the half‐panel jackknife FE‐TE estimator is of order T^?2, and for valid inference it is only required that N/T³→0, as N,T→∞ jointly. Extension to unbalanced panel data models is also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE‐TE estimator can suffer from large size distortions when N>T, with the half‐panel jackknife FE‐TE estimator showing little size distortions. The use of half‐panel jackknife FE‐TE estimator is illustrated with two empirical applications from the literature.     

Fixed effects instrumental variables estimation in correlated random coefficient panel data models

We provide a set of conditions sufficient for consistency of a general class of fixed effects instrumental variables (FE-IV) estimators in the context of a correlated random coefficient panel data model, where one ignores the presence of individual-specific slopes. We discuss cases where the assumptions are met and violated. Monte Carlo simulations verify that the FE-IV estimator of the population averaged effect performs notably better than other standard estimators, provided a full set of period dummies is included. We also propose a simple test of selection bias in unbalanced panels when we suspect the slopes may vary by individual.     

The productivity of private and social farms: Multilateral Malmquist indices for Slovene dairying enterprises

Applying programming techniques to farm-level panel data for four cooperative and 12 private dairy farms in the Yugoslav Republic of Slovenia gives estimates of technical efficiency, scale efficiency, and technical progress. These are used to construct multilateral Malmquist indices of total factor productivity (TFP). For the cooperatives, TFP growth has been slow and is attributable to technical progress. For the private farms, technical progress was also slow, but TFP growth was faster due to improved efficiency. Scale inefficiency explains the poorer absolute performance of the private farms, indicating the need for land reform as well as technological change.     

科技进步测度研究初探

经济产出的增长是人类社会发展的共同追求,而科技进步是经济发展最活跃最积极的因素。如何对科技进步进行定量测度?目前测量科技进步的模型很多,如柯布-道格拉斯生产函数模型、增长速度方程等。对柯布道格拉斯生产函数模型进行改进,引进了时间因素,对科技进步进行测量。利用我国1981￣2005年的相关数据进行实证分析,并计算出这些年来我国科技进步速度。     

固定交互效应面板截取模型的ECM算法

交互效应面板数据模型在社会经济问题的实证分析中具有很强的适用性,但现有研究主要集中于线性面板模型。本文将交互效应引入非线性的面板截取模型,并基于ECM算法,建立了有效估计量和识别程序。基于不同因子类型的仿真实验结果显示,ECM算法可以很好地识别面板截取样本中的非观测因子。ECM估计量具有良好的有限样本性质,与其他估计量相比具有更小的偏误和更快的收敛速度。尤其是当共同因子为低频平滑因子时,其表现最为理想。     

Trade,Technology and the Labour Market: The Case of South Africa*

This study advances previous work on the effects of trade and technological change on labour markets within the framework of Heckscher–Ohlin trade theory. We provide evidence for an unskilled labour abundant developing country by employing dynamic heterogeneous panel estimation techniques. For South African manufacturing, trade‐mandated increases in earnings are positive for labour and negative for capital whilst technology‐mandated increases are negative for both factors. We also find it important to take account of endogeneity issues in analysing the impact of technology and price changes on factor returns and in isolating factor‐ and sector‐bias of technological changes.     

Binary response panel data models with sample selection and self‐selection

We consider estimating binary response models on an unbalanced panel, where the outcome of the dependent variable may be missing due to nonrandom selection, or there is self‐selection into a treatment. In the present paper, we first consider estimation of sample selection models and treatment effects using a fully parametric approach, where the error distribution is assumed to be normal in both primary and selection equations. Arbitrary time dependence in errors is permitted. Estimation of both coefficients and partial effects, as well as tests for selection bias, are discussed. Furthermore, we consider a semiparametric estimator of binary response panel data models with sample selection that is robust to a variety of error distributions. The estimator employs a control function approach to account for endogenous selection and permits consistent estimation of scaled coefficients and relative effects.     

技术进步、工业化程度对能耗强度影响的差异分析——基于省际面板数据的实证研究

碳达峰、碳中和的深层次问题是能源问题。本文分析了技术进步、工业化程度影响能耗强度的作用机制,并基于2010-2020年我国30个省区市(未包括西藏及港澳台)的面板数据进行实证研究,结果表明:技术进步与能耗强度显著负相关,技术进步通过改善要素生产率有效减少能耗;工业化程度对能耗强度存在显著的正向作用关系,工业体系中高耗能行业占比较高,高耗能产品份额的持续增加影响了能耗强度的降低;不同地区技术进步、工业化程度对能耗强度的影响存在差异性,这可能是与地区禀赋有关;且回归结果经GMM分析检验后是稳健的。据此,应加大节能技术研发并制定耗能标准,优化产业和能源结构,而不同地区应制定差异化能源政策并加强协作。     

Efficiency of the Indian leather firms: some results obtained using the two conventional methods

Indian leather industry has massive potential for generating employment and achieving high export-oriented growth. However, its economic performance has not been assessed much till date. The present paper attempts to fill in this gap by examining technical efficiency (TE) of individual leather producing firms for some years since the mid-1980’s. Analyzing the industry’s firm-level data through the two conventional tools, viz., data envelopment analysis and stochastic frontier analysis, the paper observes a significant positive association between a firm’s size and its TE, but no such clear relation between a firm’s age and TE. It also finds significant variation in TE across firms in different groups of states as well as under different organizational structures and observes some technological heterogeneity across states. Although, non-availability of panel data does not allow one to assess effects of economic reforms on the performance of the Indian leather firms, the average firm-level TE, however, seems to be on an increasing path, except for downswing in the immediate post-reform years.     

Nonresponse in panel data: The impact on estimates of a life cycle consumption function

If missing observations in a panel data set are not missing at random, many widely applied estimators may be inconsistent. In this paper we examine empirically several ways to reveal the nature and severity of the selectivity problem due to nonresponse, as well as a number of methods to estimate the resulting models. Using a life cycle consumption function and data from the Expenditure Index Panel from the Netherlands, we discuss simple procedures that can be used to assess whether observations are missing at random, and we consider more complicated estimation procedures that can be used to obtain consistent or efficient estimates in case of selectivity of attrition bias. Finally, some attention is paid to the differences in identification, consistency, and efficiency between inferences from a single wave of the panel, a balanced sub-panel, and an unbalanced panel.     

Panel data analysis of U.S. coal productivity

We analyze labor productivity in coal mining in the United States using indices of productivity change associated with the concepts of panel data modeling. This approach is valuable when there is extensive heterogeneity in production units, as with coal mines. We find substantial returns to scale for coal mining in all geographical regions, and find that smooth technical progress is exhibited by estimates of the fixed effects for coal mining. We carry out a variety of diagnostic analyses of our basic model and primary modeling assumptions, using recently proposed methods for addressing ‘errors-in-variables’ and ‘weak instrument bias’ problems in linear and nonlinear models.