An HEI-Disaggregated Input-Output Table for Scotland

This paper describes how the education sector of the Scottish Input-Output tables is disaggregated to identify a separate sector for each of Scotland’s twenty Higher Education Institutions (HEIs). The process draws on accounting and survey data to accurately determine the incomes and expenditures of each institution. In particular we emphasise determining the HEIs incomes source of origin to inform their treatment, as endogenous or exogenous, in subsequent analyses. The HEI-disaggregated Input- Output table provides a useful descriptive snapshot of the Scottish economy and the role of HEIs within it for a particular year, 2006. The table can be used to derive multipliers and conduct various impact studies of each institution or the sector as a whole. The table is furthermore useful to calibrate other multi-sectoral, HEI disaggregated models of regional economies, including Social Accounting Matrix (SAM) and computable general equilibrium (CGE) models.

An HEI-Disaggregated Input-Output Table for Wales

This paper describes how the education sector of the Welsh Input-Output tables is disaggregated to identify a separate sector for each of Wales’s twelve Higher Education Institutions (HEIs). The process draws on accounting and survey data to accurately determine the incomes and expenditures of each institution. In particular we emphasise determining the HEIs incomes source of origin to inform their treatment, as endogenous or exogenous, in subsequent analyses. The HEI-disaggregated Input-Output table provides a useful descriptive snapshot of the Welsh economy and the role of HEIs within it for a particular year, 2006. The table can be used to derive multipliers and conduct various impact studies of each institution or the sector as a whole. The table is furthermore useful to calibrate other multi-sectoral, HEI-disaggregated models of regional economies, including Social Accounting Matrix (SAM) and computable general equilibrium (CGE) models.

An HEI-Disaggregated Input-Output Table for Northern-Ireland

This paper describes how the education sector of an Input-Output table for Northern Ireland is disaggregated to identify a separate sector for each of the four Northern Irish Higher Education Institutions (HEIs). The process draws on accounting and survey data to accurately determine the incomes and expenditures of each institution. In particular we emphasise determining the HEIs incomes source of origin to inform their treatment, as endogenous or exogenous, in subsequent analyses. The HEI-disaggregated Input-Output table provides a useful descriptive snapshot of the Northern Irish economy and the role of HEIs within it for a particular year, 2006. The table can be used to derive multipliers and conduct various impact studies of each institution or the sector as a whole. The table is furthermore useful to calibrate other multisectoral, HEI-disaggregated models of regional economies, including Social Accounting Matrix (SAM) and computable general equilibrium (CGE) models.

Disaggregating the Household Sector in a 2004 UK Input Output Table and Social Accounting Matrix by Income Quintiles

This paper disaggregates a UK Input-Output (IO) table for 2004 based on household income quintiles from published survey data. In addition to the Input-Output disaggregation, the household components of a UK Income Expenditure (I-E) account used to inform a Social Accounting Matrix (SAM),have also been disaggregated by household income quintile. The focus of this paper is on household expenditure on the UK energy sector.

On monetary policy and stock market anomalies

This study utilizes a macro-based VAR framework to investigate whether stock portfolios formedon the basis of their value, size and past performance characteristics are affected in a differentialmanner by unexpected US monetary policy actions during the period 1967-2007. Full sample results show that value, small capitalization and past loser stocks are more exposed to monetary policy shocks in comparison to growth, big capitalization and past winner stocks. Subsample analysis, motivated by variation in the realized premia and parameter instability, reveals that monetary policy shocks’ impact on these portfolios is significant and pronounced only during the pre-1983 period.

Large Time-Varying Parameter VARs

In this paper we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.

Reasons why emergency department providers do not rely on the pneumonia severity index to determine the initial site of treatment for patients with pneumonia.

BACKGROUND: Many emergency department (ED) providers do not follow guideline recommendations for the use of the pneumonia severity index (PSI) to determine the initial site of treatment for patients with community-acquired pneumonia (CAP). We identified the reasons why ED providers hospitalize low-risk patients or manage higher-risk patients as outpatients. METHODS: As a part of a trial to implement a PSI-based guideline for the initial site of treatment of patients with CAP, we analyzed data for patients managed at 12 EDs allocated to a high-intensity guideline implementation strategy study arm. The guideline recommended outpatient care for low-risk patients (nonhypoxemic patients with a PSI risk classification of I, II, or III) and hospitalization for higher-risk patients (hypoxemic patients or patients with a PSI risk classification of IV or V). We asked providers who made guideline-discordant decisions on site of treatment to detail the reasons for nonadherence to guideline recommendations. RESULTS: There were 1,306 patients with CAP (689 low-risk patients and 617 higher-risk patients). Among these patients, physicians admitted 258 (37.4%) of 689 low-risk patients and treated 20 (3.2%) of 617 higher-risk patients as outpatients. The most commonly reported reasons for admitting low-risk patients were the presence of a comorbid illness (178 [71.5%] of 249 patients); a laboratory value, vital sign, or symptom that precluded ED discharge (73 patients [29.3%]); or a recommendation from a primary care or a consulting physician (48 patients [19.3%]). Higher-risk patients were most often treated as outpatients because of a recommendation by a primary care or consulting physician (6 [40.0%] of 15 patients). CONCLUSION: ED providers hospitalize many low-risk patients with CAP, most frequently for a comorbid illness. Although higher-risk patients are infrequently treated as outpatients, this decision is often based on the request of an involved physician.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

Beyond Intermediates: The Role of Consumption and Commuting in the Construction of Local Input-Output Tables

It is a well-established fact in the literature on simulating Input-Output tables that mechanical methods for estimating intermediate trade lead to biased results where cross-hauling is underestimated and Type-I multipliers are overstated. Repeated findings to this effect have led to a primary emphasis on advocating the accurate estimation of intermediate trade flows. This paper reviews previous research and argues for a qualification of the consensus view: When simulating IO tables, construction approaches need to consider spill-over effects driven by wage and consumption flows. In particular, for the case of metropolitan economies, wage and consumption flows are important if accurate Type-II multipliers are to be obtained. This is demonstrated by constructing an interregional Input-Output table, which captures interdependencies between a city and its commuter belt, nested within the wider regional economy. In addition to identifying interdependencies caused by interregional intermediate purchases, data on subregional household incomes and commuter flows are used to identify interdependencies from wage payments and household consumption. The construction of the table is varied around a range of assumptions on intermediate trade and household consumption to capture the sensitivity of multipliers.

The added value from a general equilibrium analyses of increased efficiency in household energy use

The aim of the paper is to identify the added value from using general equilibrium techniques to consider the economy-wide impacts of increased efficiency in household energy use. We take as an illustrative case study the effect of a 5% improvement in household energy efficiency on the UK economy. This impact is measured through simulations that use models that have increasing degrees of endogeneity but are calibrated on a common data set. That is to say, we calculate rebound effects for models that progress from the most basic partial equilibrium approach to a fully specified general equilibrium treatment. The size of the rebound effect on total energy use depends upon: the elasticity of substitution of energy in household consumption; the energy intensity of the different elements of household consumption demand; and the impact of changes in income, economic activity and relative prices. A general equilibrium model is required to capture these final three impacts.

Data-based priors for vector autoregressions with drifting coefficients

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.

Consumption Decisions When People Value Conformity

In this paper we assume that for some commodities individuals may wish to adjust their levels of consumption from their normal Marshallian levels so as to match the consumption levels of a group of other individuals, in order to signal that they conform to the consumption norms of that group. Unlike Veblen’s concept of conspicuous consumption this can mean that some individuals may reduce their consumption of the relevant commodities. We model this as a three-stage game in which individuals first decide whether or not they wish to adhere to a norm, then decide which norm they wish to adhere to, and finally decide their actual consumption. We present a number of examples of the resulting equilibria, and then discuss the potential policy implications of this model.

Modeling Dependence Structure and Forecasting Portfolio Value-at-Risk with Dynamic Copulas

We study the asymmetric and dynamic dependence between financial assets and demonstrate, from the perspective of risk management, the economic significance of dynamic copula models. First, we construct stock and currency portfolios sorted on different characteristics (ex ante beta, coskewness, cokurtosis and order flows), and find substantial evidence of dynamic evolution between the high beta (respectively, coskewness, cokurtosis and order flow) portfolios and the low beta (coskewness, cokurtosis and order flow) portfolios. Second, using three different dependence measures, we show the presence of asymmetric dependence between these characteristic-sorted portfolios. Third, we use a dynamic copula framework based on Creal et al. (2013) and Patton (2012) to forecast the portfolio Value-at-Risk of long-short (high minus low) equity and FX portfolios. We use several widely used univariate and multivariate VaR models for the purpose of comparison. Backtesting our methodology, we find that the asymmetric dynamic copula models provide more accurate forecasts, in general, and, in particular, perform much better during the recent financial crises, indicating the economic significance of incorporating dynamic and asymmetric dependence in risk management.

Modelling Housing Prices using a Present Value State Space Model

This paper introduces a State Space approach to explain the dynamics of rent growth, expected returns and Price-Rent ratio in housing markets. According to the present value model, movements in price to rent ratio should be matched by movements in expected returns and expected rent growth. The state space framework assume that both variables follow an autoregressive process of order one. The model is applied to the US and UK housing market, which yields series of the latent variables given the behaviour of the Price-Rent ratio. Resampling techniques and bootstrapped likelihood ratios show that expected returns tend to be highly persistent compared to rent growth. The Öltered expected returns is considered in a simple predictability of excess returns model with high statistical predictability evidenced for the UK. Overall, it is found that the present value model tends to have strong statistical predictability in the UK housing markets.