ON THE PARAMETERIZATION OF TECHNIQUES FOR REPRESENTING REGIONAL ECONOMIC STRUCTURES

This paper is concerned with two parameterized methods of regionalising input–output coefficients: the Flegg et al. Location Quotient (FLQ) and its augmented version (AFLQ). For applying the two techniques, a parameter δ has to be estimated. In this regard, the paper faces two matters that are still open in the literature: the existence of a range of δ that can be used in different regions and the estimation of the most appropriate value of δ. For this aim, a Monte Carlo simulation has been carried out in order to generate ‘true’ multiregional I-O tables randomly. From the simulation, analyses based on probability distributions and regression were also carried out. Finally, these simulation results have been compared with those of an empirical case. Results confirm that there is actually a range of values of δ within which the best δ is more likely to fall. For the FLQ, this range is centred on 0.3 with an associated probability of 33% (if the width of the range is set at 0.1), whereas, for the AFLQ, the relevant range is between 0.3 and 0.4 with a probability by 38%. Finally, this paper provided a way to estimate the best δ for a given region, without knowing the relevant and detailed economic structure at sectoral level.     

Pollution-related variable input-output model: The Tulsa SMSA as a case study

Regional planners face conflicting goals of promoting regional growth and maintaining a cleaner environment since such growth usually causes polluted air. To help understand this complex relationship, pollution-related variable input-output model is developed. An interesting feature of this model is its ability to respond to both income and substitution effects. The pollution may be created not only by an increase in final demand (income effect) but also by a change in input cost (substitution effect). The conventional pollution related input-output model ([2.]) fails to capture the pollution effect associated with the substitution effect. Using the Tulsa SMSA as a case study, the operational impact of entry of a bicycle industry in the area to observe the development potential of other industries and resulting impact on air quality is simulated. Industrial impact was measured in terms of industrial output, personal income, employment, and regional import and export. The pollution impact was measured in terms of particulates, sulfur oxides, hydrocarbon, carbon monoxide, and nitrogen oxides. The simulation analysis of antipollution control measures emphasized the substitution effect of the pollution-related variable input-output model. As input cost is increased due to antipollution control measures, equilibrium prices are increased, and technical coefficients are changed as a result of substituting behavior of firms. A change in technical coefficients affects both the output and the pollutants.     

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.     

Computing median unbiased estimates in macroeconometric models

A stochastic simulation procedure is proposed in this paper for obtaining median unbiased (MU) estimates in macroeconometric models. MU estimates are computed for lagged dependent variable (LDV) coefficients in 18 equations of a macroeconometric model. The 2SLS bias for a coefficient, defined as the difference between the 2SLS estimate and the MU estimate, is on average smaller in absolute value than would be expected from Andrews exact results for an equation with only a constant term, time trend, and LDV. The results also show that in a practical sense the estimated biases are not very large because they have little effect on the overall predictive accuracy of the model and on its multiplier properties.     

基于EMB多重插补法的线性模型系数估计量的模拟研究

基于EMB多重插补法的线性模型系数估计量,分析其统计性质,并与PMM多重插补法以及DA插补法进行比较。模拟结果显示,随着无回答率增加,系数估计量的偏差绝对值、均方误差呈递增趋势,估计方差的递增趋势相对更显著。在完全随机无回答机制或随机无回答机制下,建议插补重数为15。在依赖被解释变量的非随机无回答机制下,建议插补重数可适当增大。在依赖其他变量的非随机无回答机制下,估计量的均方误差和估计方差的差异大,使用EMB多重插补法要谨慎。     

SENSITIVITY AND UNCERTAINTY ANALYSIS IN MRIO MODELLING; SOME EMPIRICAL RESULTS WITH REGARD TO THE DUTCH CARBON FOOTPRINT

Environmental multi-regional input–output (MRIO) models require large amounts of data that all have their specific uncertainties. This paper presents a sensitivity and uncertainty analysis in order to gain an understanding of the directions in which efforts should be made to reduce these uncertainties. The analyses were carried out for an MRIO model to calculate the Dutch carbon footprint. A sensitivity analysis of the technical coefficients showed that changes in the coefficients in the domestic blocks and in the Dutch import blocks had the largest effects on the calculated footprint. The uncertainty analysis consisting of a Monte Carlo simulation based on probability distributions around the model coefficients showed a relatively low degree of uncertainty in the total Dutch carbon footprint; uncertainties in the carbon emissions allocated to regions, sectors and products were larger. Both analyses showed that, in certain cases, it is justified to apply a partial MRIO analysis.     

Panel data models with multiple time-varying individual effects

This paper considers a panel data model with time-varying individual effects. The data are assumed to contain a large number of cross-sectional units repeatedly observed over a fixed number of time periods. The model has a feature of the fixed-effects model in that the effects are assumed to be correlated with the regressors. The unobservable individual effects are assumed to have a factor structure. For consistent estimation of the model, it is important to estimate the true number of individual effects. We propose a generalized methods of moments procedure by which both the number of individual effects and the regression coefficients can be consistently estimated. Some important identification issues are also discussed. Our simulation results indicate that the proposed methods produce reliable estimates.     

Bayesian regression with B‐splines under combinations of shape constraints and smoothness properties

In this paper, we approach the problem of shape constrained regression from a Bayesian perspective. A B‐splines basis is used to model the regression function. The smoothness of the regression function is controlled by the order of the B‐splines, and the shape is controlled by the shape of an associated control polygon. Controlling the shape of the control polygon reduces to some inequality constraints on the spline coefficients. Our approach enables us to take into account combinations of shape constraints and to localize each shape constraint on a given interval. The performance of our method is investigated through a simulation study. Applications to a real data sets in food industry and Global Warming are provided.     

PSEUDO-R2 MEASURES FOR SOME COMMON LIMITED DEPENDENT VARIABLE MODELS

Abstract. A large number of different Pseudo- R ² measures for some common limited dependent variable models are surveyed. Measures include those based solely on the maximized likelihoods with and without the restriction that slope coefficients are zero, those which require further calculations based on parameter estimates of the coefficients and variances and those that are based solely on whether the qualitative predictions of the model are correct or not. The theme of the survey is that while there is no obvious criterion for choosing which Pseudo- R ² to use, if the estimation is in the context of an underlying latent dependent variable model, a case can be made for basing the choice on the strength of the numerical relationship to the OLS- R ² in the latent dependent variable. As such an OLS- R ² can be known in a Monte Carlo simulation, we summarize Monte Carlo results for some important latent dependent variable models (binary probit, ordinal probit and Tobit) and find that a Pseudo- R ² measure due to McKelvey and Zavoina scores consistently well under our criterion. We also very briefly discuss Pseudo- R ² measures for count data, for duration models and for prediction-realization tables.     

Input–Output Analysis,Labour Scarcity and Relative Price Endogeneity: Aggregate Demand Disturbances in a ‘Flex-price’ Leontief System

In this paper, we introduce scarcity into a conventional demand-driven input–output system to produce a ‘flex-price’ Leontief model. We retain the fixed technical coefficients but allow changes in relative prices to reflect variations in the real wage. Because the consumption coefficients increase with the real wage, the aggregate labour demand curve is found to be upward sloping. This produces conventional results, as long as the labour supply curve is perfectly elastic (horizontal). However, once we introduce labour scarcity, in the form of an upward sloping labour supply function, we derive the seemingly paradoxical result that the output and employment effects of a demand expansion are greater here than in conventional input–output. Through simulation, this result is found to be strongly dependent on the assumption of export exogeneity.     

Nonparametric estimation of time varying parameters under shape restrictions

In recent years, a lot of econometric literature has been devoted to estimating time varying coefficients in regression models. Here, a new method based on smoothers is proposed, which is able to introduce shape restrictions over the coefficients. The statistical properties of the estimator are obtained for very general situations, including locally stationary regressors. In particular, the procedure provides consistent results for time varying autoregressive models. The practical problem of implementation is also addressed. A data-driven method for selecting the control parameters is provided, together with an algorithm that reduces the computational cost. A simulation study and an application to real data supports the theoretical results.     

The random coefficients logit model is identified

The random coefficients multinomial choice logit model, also known as the mixed logit, has been widely used in empirical choice analysis for the last thirty years. We prove that the distribution of random coefficients in the multinomial logit model is nonparametrically identified. Our approach requires variation in product characteristics only locally and does not rely on the special regressors with large supports used in related papers. One of our two identification arguments is constructive. Both approaches may be applied to other choice models with random coefficients.     

Autocorrelated disturbances in the light of specification analysis

The paper reconsiders the problem of autocorrelation among disturbances caused by the omission of some regressors from a single-equation regression model. Regression coefficients and disturbances of the misspecified model have been redefined appropriately. Performance of the OLS method of estimation of these regression coefficients and that of the Durbin-Watson test of randomness of disturbances have been studied. Some of the alternative methods of estimating the regression coefficients in situations where the disturbances are autocorrelated heve been examined. It appears that these methods can no longer be used if the autocorrelation is due to omission of regressors.     

CGE EVALUATION OF A UNIVERSITY'S EFFECTS ON A REGIONAL ECONOMY: AN INTEGRATED ASSESSMENT OF EXPENDITURE AND KNOWLEDGE IMPACTS

In recent years many universities have commissioned studies of the effects of their institutions on their local economies. Typically, these impact studies have concentrated on the demand-side stimuli to the regional economy that the university generates. Normally, the studies are undertaken with comparative-static input-output models. The present study employs a dynamic multiregional computable general equilibrium model to investigate supply-side as well as demand-side effects. There are a range of supply-side effects that have been investigated in the spatial econometrics literature. The supply-side impacts of the university that we examine in particular are a rise in the average skill level of the local workforce, and successful R&D outcomes. CGE modelling allows simulation of the associated productivity effects, while the dynamic features of the model allow for consequent effects on a region's population and capital stock growth rates to be taken into account.     

ANALYSING THE STRUCTURE OF THE ECONOMY USING PHYSICAL INPUT–OUTPUT TABLES

Analyses using physical input–output tables (PIOTs) are key to understanding the physical metabolism of economies, since they relate production to the generation of emissions and use of resources. Two methods have been developed to calculate the primary resources and emissions associated with a given final demand. However, one of these alters the PIOT, revealing different technical coefficients and Leontief inverse matrices. Which method should be used for structural analysis? In this paper, I compare both methods, explain the structural differences between them and illustrate the latter through a backward linkage analysis. I find that only one method is suited to the analysis of the physical structure of the economy, since it comprehends both the production of goods and associated emissions. The method is identified as a new model capable of tracing by-products as final outputs. Finally, I generalise both methods to analyse PIOTs including several emission types.     

Forecasting with High‐Dimensional Panel VARs

This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time‐varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation‐free algorithm that relies on analytical approximations to the posterior. We use our methods to forecast inflation rates in the eurozone and show that these forecasts are superior to alternative methods for large vector autoregressions.     

The RAS Approach in Updating Input–Output Matrices: An Instrumental Variable Interpretation and Analysis of Structural Change

The main purposes of the paper are to reconsider the rationale of the RAS method, and to attempt to improve on its interpretation and usefulness. The substitution and fabrication factors in the RAS method are interpreted as statistical estimates obtained by the method of instrumental variables. This enables the computation of asymptotic standard errors for the factors and the relative precision of the predicted technical coefficients. Furthermore, an adjustment cost minimization model to describe how a sector determines its substitution and fabrication factors is presented. The solutions of the model provide another rationale for the RAS method, and the associated Lagrangian multipliers can be useful for assessing structural constraints and structural change.     

Extending the input–output model with assets

In this paper, the input–output model is extended with assets. It allows us to examine the various assets that are held and used in production. The requirements of assets that must be held by each sector can thus be specified. Extending the input–output model with assets provides a better alternative to the capital stock matrix in the standard Systems of National Accounts. The input–output model is extended by taking the depreciation of fixed assets into full account. This extension allows for the calculation of total holding coefficients that express the amount of assets that are required to be held in each sector in order to satisfy a unit of final demand. In addition, a dynamic version of the extended model is presented. The extended input–output model has been widely applied in China for various purposes.     

INCOME DISTRIBUTIONS IN INPUT-OUTPUT MODELS

The analysis of income distribution (ID) has traditionally been of prime importance for economists and policy-makers. However, the standard input–output (I–O) model is not particularly well equipped for studying current issues such as the consequences of decreasing access to primary inputs or the effects of specific redistributive policies. This paper addresses this gap in the existing literature. We propose that IDs can excellently be studied by restructuring the I–O relations. A new coefficients matrix is defined, the so-called augmented input coefficients matrix. This matrix is the sum of the intermediate input coefficients matrix and newly constructed matrices of sector-specific input coefficients that represent the existing distribution of income. We show that shifts in the distribution can be modelled by attributing weights to these matrices and vary these according to system-specific rules. Numerical illustrations based on the existing literature are given throughout the paper.     

Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances

We propose a new class of models specifically tailored for spatiotemporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, that is, SARAR(1, 1), by exploiting the recent advancements in score‐driven (SD) models typically used in time series econometrics. In particular, we allow for time‐varying spatial autoregressive coefficients as well as time‐varying regressor coefficients and cross‐sectional standard deviations. We report an extensive Monte Carlo simulation study in order to investigate the finite‐sample properties of the maximum likelihood estimator for the new class of models as well as its flexibility in explaining a misspecified dynamic spatial dependence process. The new proposed class of models is found to be economically preferred by rational investors through an application to portfolio optimization.