REGIONAL POPULATION–EMPLOYMENT DYNAMICS ACROSS DIFFERENT SECTORS OF THE ECONOMY*

ABSTRACT An important subset of the literature on agglomeration externalities hypothesizes that intrasectoral and intersectoral relations are endogenously determined in models of local and regional economic growth. Remarkably, structural adjustment models describing the spatio‐temporal dynamics of population and employment levels or growth traditionally do not include intersectoral economic dynamics. This paper argues and shows that allowing for economic linkages across sectors in these models adds considerable value, especially in forecasting. An econometric model of population–employment dynamics, in which sectoral variations in economic development are explicitly taken into account, is applied to a large urban planning policy proposal in the Netherlands. The empirical analyses suggest that population dynamics are largely exogenous, population changes drive employment in particular in the industry and retail sectors, and employment in all sectors depends strongly on intersectoral dynamics. Intersectoral dynamics appear as important drivers of regional sectoral employment changes; they are even more important than population changes, and their effect shows up clearly even within the Dutch institutional context where strict regulatory housing and planning restrictions are enforced.     

FORECASTING METROPOLITAN EMPLOYMENT USING AN EXPORT-BASE ERROR-CORBECTION MODEL

This paper shows that a time-series version of export-based location quotients, which decompose area employment into export and local components are co-integrated. Given the statistically established co-integrating relation between the time series for export and local employment, the Granger Representation Theorem tells us that an error-correction mechanism model is the appropriate time-series model specification. A set of forecasting experiments is carried out, in order to test whether the error-correction model produces better long-run forecasts that hold together in economically meaningful ways. Given the computer hardware and software that currently exist, the model developed here represents a simple, low-cost method for accurately forecasting local-area employment in the tradition of past work on export-base models.     

ECONOMETRIC MODELING OF INTERREGIONAL LABOR MARKET LINKAGES*

ABSTRACT. This paper presents a statistical procedure for empirically establishing linkages between regional economies. Using Granger's definition of causality and the final prediction error technique for lag-length specification suggested by Hsiao, a linked, multiregional employment model is developed. The forecast performance of the linked model is compared to models which do not include interregional linkages. It is shown that forecast error is reduced by taking into account employment interrelationships between regions.     

Predictability of ionospheric variations for quiet and disturbed conditions

Empirical models can predict the average monthly behaviour of the ionosphere and allow for day-to-day variability. Significant departures from these predicted conditions can be forecast using a range of solar, magnetic and ionospheric observations. The main improvements needed in the prediction models is that they should be made more precise, by introducing variable prediction limits, and should be improved by harnessing the impressive power of the current physical models so that ionospheric data can be assimilated in near real time. This strategic objective will lead to ionospheric weather forecasting.     

Three Tools for Forecasting Federal Elections: Lessons from 2001

What is the best way to predict Australian federal election results? This article analyses three forecasting tools: opinion polls, economic models, and betting odds. Historically, we find that opinion polls taken close to the election are quite accurate, while economic models provide better medium-run forecasts. The November 2001 federal election largely follows this pattern, although the economic models provided more accurate projections than recorded through the 1990s. Against these, we compare betting odds, analysing a rich data source from one of Australia's largest bookmakers, Centrebet. The betting market not only correctly forecast the election outcome, but also provided very precise estimates of outcomes in a host of individual electorates. Betting fluctuations present an intriguing quantitative record of the shifting fortunes of the campaign. Particularly in marginal seats, the press may have better served its readers by reporting betting odds than by conducting polls. We conclude that the results of these three models can help determine how important the events of August and September 2001 were in deciding the outcome of the election.     

基于APSO—SVR的山岳风景区短期客流量预测

根据山岳风景区短期客流量小样本、非线性等特征,本文提出基于自适应粒子群算法（Adaptive Particle Swarm Optimization,APSO）的支持向量回归（Support Vector Regression,SVR）模型,融合SVR处理小样本、非线性预测特性和APSO优化SVR参数的能力对山岳风景区短期客流量进行预测。来自山岳风景区黄山2008年～2011年暑期相关日数据的验证结果表明：与PSO—SVR、GA—SVR和BPNN等模型相比,APSO。SVR模型的预测准确性更高、误差更小,是进行山岳风景区短期客流量预测的有效工具。     

九寨沟风景区日游客量预测研究

要实现风景区科学规划与永续利用,游客数量的预测是关键,其中短期的游客数量预测则是景区资源统一管理与合理调度的直接依据。本文分析了九寨沟的游客到达历史数据,建立了游客到达的日预测回归模型和BP修正模型,实际数据证明该模型有效。     

Leading Indicators and Spatial Interactions: A Crime-Forecasting Model for Proactive Police Deployment

We develop a leading indicator model for forecasting serious property and violent crimes based on the crime attractor and displacement theories of environmental criminology. The model, intended for support of tactical deployment of police resources, is at the microlevel scale; namely, 1-month-ahead forecasts over a grid system of 141 square grid cells 4000 feet on a side (with approximately 100 blocks per grid cell). The leading indicators are selected lesser crimes and incivilities entering the model in two ways: (1) as time lags within grid cells and (2) time and space lags averaged over grid cells contiguous to observation grid cells. Our validation case study uses 1.3 million police records from Pittsburgh, Pennsylvania, aggregated over the grid system for a 96-month period ending in December 1998. The study uses the rolling-horizon forecast experimental design with forecasts made over the 36-month period ending in December 1998, yielding 5076 forecast errors per model. We estimated the leading indicator model using a robust linear regression model, a neural network, and a proven univariate, extrapolative forecast method for use as a benchmark in Granger causality testing. We find evidence of both the crime attractor and displacement theories. The results of comparative forecast experiments are that the leading indicator models provide acceptable forecasts that are significantly better than the extrapolative method in three out of four cases, and for the fourth there is a tie but poor forecast performance. The leading indicators find 41–53% of large crime volume changes in the three successful cases. The corresponding workload for police is quite acceptable, with on the average 5.2 potential large change cases per month to investigate and with 31% of such cases being positives.     

THE FUTURE TRAJECTORY OF U.S. CO2 EMISSIONS: THE ROLE OF STATE VS. AGGREGATE INFORMATION*

ABSTRACT This paper provides comparisons of a variety of time‐series methods for short‐run forecasts of the main greenhouse gas, carbon dioxide, for the United States, using a recently released state‐level data set from 1960–2001. We test the out‐of‐sample performance of univariate and multivariate forecasting models by aggregating state‐level forecasts versus forecasting the aggregate directly. We find evidence that forecasting the disaggregate series and accounting for spatial effects drastically improves forecasting performance under root mean squared forecast error loss. Based on the in‐sample observations we attempt to explain the emergence of voluntary efforts by states to reduce greenhouse gas emissions. We find evidence that states with decreasing per capita emissions and a “greener” median voter are more likely to push toward voluntary cutbacks in emissions.     

Macroeconomic Effects of 21st Century Trade and Investment Agreements: The Case of the Trans‐Pacific Partnership

Following United States withdrawal, the Trans‐Pacific Partnership agreement (TPP) is likely to be replaced or complemented by a series of bilateral deals between the US and TPP partners. In this case, TPP will shape trade, finance and public policy globally even without formal US participation. Proponents of TPP emphasize its prospective economic benefits, with economic growth increasing due to rising trade volumes and investment. Widely cited projections suggest modest GDP gains after 10 years, varying from less than half a percentage point in the USA to 13 per cent in Vietnam. However, these projections assume full employment and constant income distribution in all countries, excluding some of the major risks of trade liberalization. This article provides alternative projections of the TPP's economic effects using the United Nations Global Policy Model, which allows for changes in employment and income distribution. Using this model, the authors obtain very different results. They find that the benefits to economic growth are even smaller than those projected with full‐employment models, and are negative for Japan and the USA. More importantly, they find that the TPP will likely lead to losses in employment and increases in inequality.     

A Spatial Prior for Bayesian Vector Autoregressive Models

In this paper we develop a Bayesian prior motivated by cross-sectional spatial autoregressive models for use in time-series vector autoregressive forecasting involving spatial variables. We compare forecast accuracy of the proposed spatial prior to that from a vector autoregressive model relying on the Minnesota prior and find a significant improvement. In addition to a spatially motivated prior variance as in LeSage and Pan (1995) we develop a set of prior means based on spatial contiguity. A Theil-Goldberger estimator may be used for the proposed model making it easy to implement.     

ECONOMIC FUNDAMENTALS IN LOCAL HOUSING MARKETS: EVIDENCE FROM U.S. METROPOLITAN REGIONS

ABSTRACT.  This paper investigates the effects of national and regional economic conditions on outcomes in the single-family housing market: housing prices, vacancies, and residential construction activity. Our three-equation model confirms the importance of changes in regional economic conditions, income, and employment on local housing markets. The results also provide the first detailed evidence on the importance of vacancies in the owner-occupied housing market on housing prices and supplier activities. The results also document the importance of variations in materials, labor and capital costs, and regulation in affecting new supply. Simulation exercises, using standard impulse response models, document the lags in market responses to exogenous shocks and the variations arising from differences in local parameters. The results also suggest the importance of local regulation in affecting the pattern of market responses to regional income shocks.     

Land Absorption in U.S. Metropolitan Areas: Estimates and Projections from Regional Adjustment Models

This article adapts a regional adjustment model to estimate and project the spatial outcome of population and employment growth in U.S. metropolitan areas. The three-equation multiplicative model of population change, employment change, and land absorption is estimated using three-stage least squares to account for endogeneity among the dependent variables and contemporaneous correlation across the system of equations. In addition to the core model, alternative specifications are estimated, imposing the initial conditions of size, land availability, and economic structure. The stability of the solutions is then examined using reduced-form equations estimated via the seemingly unrelated regression equations approach. The results reveal substantive evidence that population and employment growth are jointly determined, of how the two affect the outcome of land development, and, perhaps most importantly, stable and fractionally reasonable estimates at projected equilibrium points. Lastly, the adapted model controlling for the initial condition of land availability is used to project patterns of land consumption at equilibrium in 50 rapid-growth metropolitan areas.     

Urban,Suburban, and Exurban Sprawl in the Rocky Mountain West: Evidence from Regional Adjustment Models*

Abstract. This article adapts a regional adjustment model to examine land use change in the Rocky Mountain West region of the United States. Three interrelated questions motivate the research. How does the proliferation of urban, suburban, and exurban sprawl in the Rocky Mountain West relate to the population and employment growth process? Are population and employment endogenously determined there? And what does this imply for the sustainability of economic development in the region? Through a series of regional adjustment models, the empirical analysis links population and employment growth in the Rocky Mountain West to explicit spatial outcomes and delivers substantive evidence of endogeneity between the two. The results suggest that the long‐term prosperity of the region depends on the preservation of the high quality of life it offers, and that greater intergovernmental coordination, careful infrastructure planning, and attention to the character of its economic structure may help to accomplish this. Future research should focus on looking deeper into certain explanatory variables used in this analysis and on developing a better picture of what the spatial equilibrium that regional adjustment models emulate may look like.     

SUPPLY-SIDE MULTIREGIONAL INPUT-OUTPUT MODELS*

ABSTRACT This paper introduces a new class of supply-side multiregional input-output (MRIO) models and provides the necessary and sufficient conditions on the regional trade matrices that ensure that a generalized supply-side model will be convergent. The paper also introduces a new version of the row coefficient model, conceived as the “mirror image” of the Chenery-Moses demand-side column coefficient MRIO model. Given that the conventional supply-side input-output model has been shown to perform equally as well as the demand side model in forecasting exercises, the supply-side MRIO model is expected to be of value for policy and planning purposes. Moreover, this model is of potential theoretical value for a broad synthesis of demand-side and supply-side MRIO models.     

THE IMPACT OF SPECIFIC-SECTOR CHANGES IN EMPLOYMENT ON ECONOMIC GROWTH, LABOR MARKET PERFORMANCE AND MIGRATION

ABSTRACT.  It is common in empirical regional economics to use total employment as an explanatory variable while investigating issues such as the level and distribution of income and migration. This paper argues that sector-specific changes in employment and labor market performance can have different effects on economic growth, the collection of tax revenue, migration, and the level and distribution of household income. As such, it is important to model sectors separately. We find that expansions in employment opportunities for a high-wage sector such as computer manufacturing or bioengineering, a medium-wage sector manufacturing, and the lower-wage sector of retailing have differing economic consequences for a small city. We use a data intensive computable general equilibrium model to obtain these results.     

SPATIAL DISCONTINUITY FOR THE IMPACT ASSESSMENT OF THE EU REGIONAL POLICY: THE CASE OF ITALIAN OBJECTIVE 1 REGIONS*

The economic impact of the Regional Policy of the European Union is still controversial. This paper exploits administrative boundaries as spatial discontinuity to estimate the causal effect of this policy on the Italian Objective 1 regions’ employment. The analysis, developed both in a border strategy framework (municipalities contiguous to the policy‐change boundary) and with more traditional RDD models balanced by spatial forcing variables (centroids’ distance and coordinates), shows that the EU Regional Policy produced a positive impact on employment levels, without any displacement of economic activities away from nontreated regions and a concentration of the impact in key economic sectors.     

对旅游就业弹性测量的实证研究 --以浙江省为例

本文对旅游就业弹性进行了线性测量,较之于以往点弹性的测量能够更好的反映旅游经济增长与旅游就业增长之间的关系,测算出的旅游就业弹性大于第三产业的总体水平,因此得出结论：发展旅游业有利于更好地促进就业。本文的创新点在于提出了旅游就业人数的核算方法和旅游业就业弹性的线性测量方法。     

A Hybrid Framework for Space–Time Modeling of Environmental Data. 环境数据时空建模的混合框架

The space–time autoregressive integrated moving average (STARIMA) model family provides useful tools for modeling space–time processes that exhibit stationarity (or near stationarity) in space and time. However, a more general method for routine use and efficient computation is needed to model the nonlinearities and nonstationarities of environmental space–time series. This article presents a hybrid framework combining machine learning and statistical methods to address this issue. It uses an artificial neural network (ANN) to extract global deterministic (nonlinear) space–time trends and a STARIMA model to extract local stochastic space–time variations in data. A four‐stage procedure is proposed for analyzing and modeling space–time series. The proposed framework and procedures are applied to forecast annual average temperature at 137 national meteorological stations in China. The results demonstrate that the hybrid framework achieves better forecasting accuracy than the STARIMA model alone. This finding suggests that the combination of machine learning and statistical methods provides a very powerful tool for analyzing and modeling space–time series of environmental data that have strong spatial nonlinear and nonstationary components.     

The emergence of agropastoralism: Accelerated ecocultural change on the Andean altiplano, ∼3540–3120 cal BP

In the fourth millennium BP, there were major environmental and cultural changes on the Andean altiplano of South America, but the chronology remains vague. A recent synthesis describes a slow, gradual transition from hunting and gathering to agropastoralism. This proposal is tested by refining the date of the onset of more humid and stable conditions, around 3550 cal BP, based on a Bayesian model of 26 dates from Lake Wiñaymarka and an updated calculation of the lacustrine offset. This is compared to Bayesian models of 191 dates from 20 archaeological sites, which incorporate a number of recently processed radiocarbon dates. A synthesis is presented of 15 full coverage surveys, a summed probability distribution, and a Bayesian model of the transition to ceramics, which together support a scenario of a very rapid demographic increase. Fourteen models from archaeological sites are cross-referenced in a composite model, which identifies a brief, altiplano-wide emergence of agropastoralism with starting and ending boundaries of 3540 and 3120 cal BP, respectively. This starting boundary correlates strongly with the onset of improving environmental conditions, indicating synchronous cultural and environmental change. The suite of accelerating cultural changes included a marked reduction in mobility, a demographic surge, increased subsistence diversity, the adoption of ceramics, farming and the integration of camelid herding into a remarkably resilient economic strategy still in use today. This is a highly relevant but yet to be used comparative case study for the variable tempos of ‘big histories’, and ecocultural interactions that generate rapid, emergent episodes of wide-spread and enduring cultural change.