A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

Rebound Effects from Increased Efficiency in the Use of Energy by UK Households

In this paper, we use CGE modelling techniques to identify the impact on energy use of an improvement in energy efficiency in the household sector. The main findings are that 1) when the price of energy is measured in natural units, the increase in efficiency yields only to a modification of tastes, changing as a result, the composition of household consumption; 2) when households internalize efficiency, the improvement in energy efficiency reduces the price of energy in efficiency units, providing a source of improved competitiveness as the nominal wage and the price level both fall; 3) the short-run rebound can be greater than the long run rebound if the household demand elasticity is the same for both time frames, however, the short run rebound is always lower than in the long-run if the demand for energy is relatively more elastic in the long-run; 4) the introduction of habit formation changes the composition of household consumption, modifying the magnitude of the household rebound only in the short-run. In this period, household and economy wide rebound are lowest for external habit formation and highest when consumers’ preferences are defined using a conventional utility function.

The effects of agglomeration on wages: evidence from the micro-level

This paper estimates individual wage equations in order to test two rival non-nested theories of economic agglomeration, namely New Economic Geography (NEG), as represented by the NEG wage equation and urban economic (UE) theory , in which wages relate to employment density. The paper makes an original contribution by evidently being the first empirical paper to examine the issue of agglomeration processes associated with contemporary theory working with micro-level data, highlighting the role of gender and other individual-level characteristics. For male respondents, there is no significant evidence that wage levels are an outcome of the mechanisms suggested by NEG or UE theory, but this is not the case for female respondents. We speculate on the reasons for the gender difference.

Regime-Switching Cointegration

We develop methods for Bayesian inference in vector error correction models which are subject to a variety of switches in regime (e.g. Markov switches in regime or structural breaks). An important aspect of our approach is that we allow both the cointegrating vectors and the number of cointegrating relationships to change when the regime changes. We show how Bayesian model averaging or model selection methods can be used to deal with the high-dimensional model space that results. Our methods are used in an empirical study of the Fisher effect.

Pension reform in a rapidly ageing country: the case of Ukraine

Ukraine has a rapidly ageing and declining population. A dynamic forward-­looking Computable General Equilibrium (CGE) model with an explicitly modelled Pay‐As‐You-­Go pension scheme is constructed to perform simulations of different pension reform scenarios and investigate the impact of population ageing on a wide range of macroeconomic variables. It is shown that, changes in age structure will result in a significant negative impact on the economy and stability of the pension system. Analysis of the potential changes to the pension system is limited to modelling an increase of the pension age, keeping either the workers’ contribution rate or replacement rate constant.

Forecasting with Medium and Large Bayesian VARs

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Time-consistent fiscal policy under heterogeneity: Conflicting or common interests?

This paper studies the aggregate and distributional implications of Markov-perfect tax-spending policy in a neoclassical growth model with capitalists and workers. Focusing on the long run, our main fi ndings are: (i) it is optimal for a benevolent government, which cares equally about its citizens, to tax capital heavily and to subsidise labour; (ii) a Pareto improving means to reduce ine¢ ciently high capital taxation under discretion is for the government to place greater weight on the welfare of capitalists; (iii) capitalists and workers preferences, regarding the optimal amount of "capitalist bias", are not aligned implying a conflict of interests.

Joint estimates of automatic and discretionary fiscal policy for the OECD

Official calculations of automatic stabilizers are seriously flawed since they rest on the assumption that the only element of social spending that reacts automatically to the cycle is unemployment compensation. This puts into question many estimates of discretionary fiscal policy. In response, we propose a simultaneous estimate of automatic and discretionary fiscal policy. This leads us, quite naturally, to a tripartite decomposition of the budget balance between revenues, social spending and other spending as a bare minimum. Our headline results for a panel of 20 OECD countries in 1981-2003 are .59 automatic stabilization in percentage-points of primary surplus balances. All of this stabilization remains following discretionary responses during contractions, but arguably only about 3/5 of it remains so in expansions while discretionary behavior cancels the rest. We pay a lot of attention to the impact of the Maastricht Treaty and the SGP on the EU members of our sample and to real time data.

When Do We Learn to Cooperate? The Role of Social Learning in Social Dilemmas

In this paper, I look at the interaction between social learning and cooperative behavior. I model this using a social dilemma game with publicly observed sequential actions and asymmetric information about pay offs. I find that some informed agents in this model act, individually and without collusion, to conceal the privately optimal action. Because the privately optimal action is socially costly the behavior of informed agents can lead to a Pareto improvement in a social dilemma. In my model I show that it is possible to get cooperative behavior if information is restricted to a small but non-zero proportion of the population. Moreover, such cooperative behavior occurs in a finite setting where it is public knowledge which agent will act last. The proportion of cooperative agents within the population can be made arbitrarily close to 1 by increasing the finite number of agents playing the game. Finally, I show that under a broad set of conditions that it is a Pareto improvement on a corner value, in the ex-ante welfare sense, for an interior proportion of the population to be informed.

Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.

Regional Policy Spillovers: The National Impact of Demand-Side Policy in an Interregional Model of the UK Economy

UK regional policy has been advocated as a means of reducing regional disparities and stimulating national growth. However, there is limited understanding of the interregional and national effects of such a policy. This paper uses an interregional computable general equilibrium model to identify the national impact of a policy-induced regional demand shock under alternative labour market closures. Our simulation results suggest that regional policy operating solely on the demand side has significant national impacts. Furthermore, the effects on the non-target region are particularly sensitive to the treatment of the regional labour market.

Estimation of the Spatial Weights Matrix under Structural Constraints

While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only partially identi ed, and is fully identifi ed under the structural constraint of symmetry. For the spatial error model, we propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial di¤usion in housing demand.

What drives urban consumption in mainland china? The role of property price dynamics

This paper adds to the literature on wealth effects on consumption by disentangling house price effects on consumption for mainland China. In a stochastic modelling framework, the riskiness, rate of increase and persistence of house price movements have different implications for the consumption/housing ratio. We exploit the geographical variation in property prices by using a quarterly city-level panel dataset for the period 1998Q1 – 2009Q4 and rely on a panel error correction model. Overall, the results suggest a significant long run impact of property prices on consumption. They also broadly confirm the predictions from the theoretical model.