Nowcasting US GDP: The role of ISM business surveys

We study the role of the well-known monthly diffusion indices produced by the Institute for Supply Management (ISM) in nowcasting current quarter US GDP growth. In contrast to the existing literature on ISM surveys, we investigate their marginal impact on these nowcasts when large unbalanced (jagged edge) macroeconomic data sets are used to generate them in real time. We find evidence that these ISM indices are helpful in improving the nowcasts when new ISM information becomes available at the beginning of the month, ahead of other monthly indicators. Furthermore, while the existing literature has focused almost exclusively on manufacturing information, here we establish the increasingly significant role of the recently created non-manufacturing ISM diffusion indices in such nowcasting contexts.     

Real Geographies,Real Economies and Soft Spatial Imaginaries: Creating a ‘More than Manchester’ Region

This article develops a framework for analysing region‐building processes as spatiotemporal constructs, involving competing spatial imaginaries and attempts at consolidating these through institution building. Central here is the performative role of what we refer to as ‘soft space imaginaries’ in the ‘phased’ building of regions for planning and economic development over time. We demonstrate how this understanding can be used to examine the phased enactment of successive waves of region‐building by tracing the evolution of multiple soft spatial imaginaries in north‐west England. The analysis exposes the variable logics, alliances of actors, and tactics used to build momentum and secure legitimacy around preferred imaginaries which advocates often promoted on the grounds that they somehow reflected ‘real geographies’ or ‘real economies’. In this context, soft space imaginaries are seen to play an integral role in intellectual case making about the contemporaneous form and purpose of subnational governance. Yet our analysis also exposes the durability of past soft space imaginaries and their continued impact on efforts to build new soft spaces. What emerges is an understanding of soft space imaginaries as more than just superficial representations. They can help determine where government investment is channelled and into what kinds of policies.     

A number‐of‐modes reference rule for density estimation under multimodality

We consider kernel density estimation for univariate distributions. The question of interest is as follows: given that the data analyst has some background knowledge on the modality of the data (for instance, ‘data of this type are usually bimodal’), what is the adequate bandwidth to choose? We answer this question by extending Silverman's idea of ‘normal‐reference’ to that of ‘reference to a Gaussian mixture’. The concept is illustrated in the light of real data examples.     

Real‐Time Nowcasting of GDP: A Factor Model vs. Professional Forecasters

We perform a fully real‐time nowcasting (forecasting) exercise of US GDP growth using Giannone et al.'s (2008) factor model framework. To this end, we have constructed a real‐time database of vintages from 1997 to 2010 for a panel of variables, enabling us to reproduce, for any given day in that range, the exact information that was available to a real‐time forecaster. We track the daily evolution of the model performance along the real‐time data flow and find that the precision of the nowcasts increases with information releases and the model fares well relative to the Survey of Professional Forecasters (SPF).     

Nowcasting in real time using popularity priors

We construct a “Google Recession Index” (GRI) using Google Trends data on internet search popularity, which tracks the public’s attention to recession-related keywords in real time. We then compare nowcasts made with and without this index using both a standard dynamic factor model and a Bayesian approach with alternative prior setups. Our results indicate that using the Bayesian model with GRI-based “popularity priors,” we could identify the 2008Q3 turning point in real time, without sacrificing the accuracy of the nowcasts over the rest of the sample periods.     

Forecasting US output growth using leading indicators: an appraisal using MIDAS models

We evaluate the predictive power of leading indicators for output growth at horizons up to 1 year. We use the MIDAS regression approach as this allows us to combine multiple individual leading indicators in a parsimonious way and to directly exploit the information content of the monthly series to predict quarterly output growth. When we use real‐time vintage data, the indicators are found to have significant predictive ability, and this is further enhanced by the use of monthly data on the quarter at the time the forecast is made. Copyright © 2009 John Wiley & Sons, Ltd.     

Financial nowcasts and their usefulness in macroeconomic forecasting

Financial data often contain information that is helpful for macroeconomic forecasting, while multi-step forecast accuracy benefits from incorporating good nowcasts of macroeconomic variables. This paper considers the usefulness of financial nowcasts for making conditional forecasts of macroeconomic variables with quarterly Bayesian vector autoregressions (BVARs). When nowcasting quarterly financial variables’ values, we find that taking the average of the available daily data and a daily random walk forecast to complete the quarter typically outperforms other nowcasting approaches. Using real-time data, we find gains in out-of-sample forecast accuracy from the inclusion of financial nowcasts relative to unconditional forecasts, with further gains from the incorporation of nowcasts of macroeconomic variables. Conditional forecasts from quarterly BVARs augmented with financial nowcasts rival the forecast accuracy of mixed-frequency dynamic factor models and mixed-data sampling (MIDAS) models.     

Nowcasting with payments system data

We consider the potential usefulness of a large set of electronic payments data, comprising the values and numbers of both debit card transactions and cheques that clear through the banking system, for the problem of reducing the current-period forecast (‘nowcast’) loss for (the growth rates of) GDP and retail sales. The payments system variables capture a broad range of spending activity and are available on a very timely basis, making them suitable current indicators. We generate nowcasts of GDP and retail sales growth for a given month on seven different dates, over a period of two and a half months preceding the first official releases, which is the period over which nowcasts would be of interest. We find statistically significant evidence that payments system data can reduce the nowcast error for both GDP and retail sales growth. Both debit transaction and cheque clearance data are of value in reducing nowcast losses for GDP growth, although the latter are of little or no value when debit data are also included. For retail sales, cheque data appear to produce no further nowcast loss reductions, regardless of whether or not debit transactions are included in the nowcasting model.     

Estimation of Common Factors under Cross‐Sectional and Temporal Aggregation Constraints

The paper estimates a large‐scale mixed‐frequency dynamic factor model for the euro area, using monthly series along with gross domestic product (GDP) and its main components, obtained from the quarterly national accounts (NA). The latter define broad measures of real economic activity (such as GDP and its decomposition by expenditure type and by branch of activity) that we are willing to include in the factor model, in order to improve its coverage of the economy and thus the representativeness of the factors. The main problem with their inclusion is not one of model consistency, but rather of data availability and timeliness, as the NA series are quarterly and are available with a large publication lag. Our model is a traditional dynamic factor model formulated at the monthly frequency in terms of the stationary representation of the variables, which however becomes nonlinear when the observational constraints are taken into account. These are of two kinds: nonlinear temporal aggregation constraints, due to the fact that the model is formulated in terms of the unobserved monthly logarithmic changes, but we observe only the sum of the monthly levels within a quarter, and nonlinear cross‐sectional constraints, since GDP and its main components are linked by the NA identities, but the series are expressed in chained volumes. The paper provides an exact treatment of the observational constraints and proposes iterative algorithms for estimating the parameters of the factor model and for signal extraction, thereby producing nowcasts of monthly GDP and its main components, as well as measures of their reliability.     

Regression Models with Data‐based Indicator Variables*

Ordinary least squares estimation of an impulse‐indicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a t‐distribution, that test is inconsistent: one solution is to form an index of indicators. We provide Monte Carlo evidence that including a plethora of indicators need not distort model selection, permitting the use of many dummies in a general‐to‐specific framework. Although White's (1980) heteroskedasticity test is incorrectly sized in that context, we suggest an easy alteration. Finally, a possible modification to impulse ‘intercept corrections’ is considered.     

Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP*

In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed‐frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high‐ and low‐frequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low‐frequency variables like gross domestic product (GDP) exploiting information in a large set of higher‐frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real‐time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state‐space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed‐frequency techniques that can exploit timely information from business cycle indicators.     

Beyond Circular Thinking: Geographies of Transit‐Oriented Development

Transit‐oriented development (TOD) plays a significant role within contemporary planning policies for ‘smart growth’ and sustainable development, particularly in Europe and North America. As a well‐rehearsed practice, this planning model is due for critical assessment and improvement in terms of its ability to incorporate dynamic and heterogeneous socio‐spatial processes as matters of concern. Analyses of the conditions for ‘making TOD work’ in the scholarly and professional literatures tend to focus on the ‘node’ and ‘place’ qualities. While elaborations on node analysis (primarily based on accessibility measurements) abound within empirical research, discussions of place‐specific assets are limited in scope and often spatially bounded by the circle defined by a 10‐minute walk. This essay examines the use of this generic ‘circle’ model, and how it normatively frames how place is understood in TOD studies. We argue that the circle enhances a Euclidean understanding of the site, which favours a static and homogeneous spatial analysis of accessibility and density rather than (other) place qualities relating to dynamic socio‐spatial processes. Finally, we argue that relational geography can facilitate an analysis of place qualities beyond the circle—one in which both the continuities and shifting settings of the wider context are meaningfully taken into consideration.     

Further Evidence on PPP Adjustment Speeds: the Case of Effective Real Exchange Rates and the EMS*

Two different approaches intend to resolve the ‘puzzling’ slow convergence to purchasing power parity (PPP) reported in the literature [see Rogoff (1996) , Journal of Economic Literature, Vol. 34.] On the one hand, there are models that consider a non‐linear adjustment of real exchange rate to PPP induced by transaction costs. Such costs imply the presence of a certain transaction band where adjustment is too costly to be undertaken. On the other hand, there are models that relax the ‘classical’ PPP assumption of constant equilibrium real exchange rates. A prominent theory put together by Balassa (1964, Journal of Political Economy, Vol. 72) and Samuelson (1964 Review of Economics and Statistics, Vol. 46) , the BS effect, suggests that a non‐constant real exchange rate equilibrium is induced by different productivity growth rates between countries. This paper reconciles those two approaches by considering an exponential smooth transition‐in‐deviation non‐linear adjustment mechanism towards non‐constant equilibrium real exchange rates within the EMS (European Monetary System) and effective rates. The equilibrium is proxied, in a theoretically appealing manner, using deterministic trends and the relative price of non‐tradables to proxy for BS effects. The empirical results provide further support for the hypothesis that real exchange rates are well described by symmetric, nonlinear processes. Furthermore, the half‐life of shocks in such models is found to be dramatically shorter than that obtained in linear models.     

Who is considered an ‘older worker'? Extending our conceptualisation of ‘older’ from an organisational decision maker perspective

A refinement of the construct of age, specifically ‘older’, is recognised as a critical measurement concern for experts in both ageing research and policy formation. In this context, we set out to both chronologically define an ‘older worker’ and to identify on what basis the age of ‘older’ is determined. In doing so, we draw on open‐ended survey data (collected in 2011) from a sample of 407 organisational decision makers across all industries in Ireland. Our focus was specifically on the perspective of organisational decision makers because these individuals will be instrumental in facing the challenges associated with workforce ageing. The results show that workers are considered as ‘older’ at a younger age than might be expected and that decision makers conceptualise workers as ‘older’ using various approaches in the organisational context. Our findings contribute to the literature in three ways: firstly, by providing an important empirically derived understanding of the term ‘older worker’; secondly, by empirically examining previously suggested ‘possible’ indicators of age; and thirdly, by demonstrating that these indicators are conceptually and empirically distinct, advancing theory on the concept of age in the workplace.     

REAL‐TIME DATA AND FISCAL POLICY ANALYSIS: A SURVEY OF THE LITERATURE

This paper surveys the empirical research on fiscal policy analysis based on real‐time data. This literature can be broadly divided into four groups that focus on: (1) the statistical properties of revisions in fiscal data; (2) the political and institutional determinants of projection errors by governments; (3) the reaction of fiscal policies to the business cycle and (4) the use of real‐time fiscal data in structural vector autoregression (VAR) models. It emerges that, first, fiscal revisions are large and initial releases are biased estimates of final values. Secondly, strong fiscal rules and institutions lead to more accurate releases of fiscal data and smaller deviations of fiscal outcomes from government plans. Thirdly, the cyclical stance of fiscal policies is estimated to be more ‘counter‐cyclical’ when real‐time data are used instead of ex post data. Fourthly, real‐time data can be useful for the identification of fiscal shocks. Finally, it is shown that existing real‐time fiscal data sets cover only a limited number of countries and variables. For example, real‐time data for developing countries are generally unavailable. In addition, real‐time data on European countries are often missing, especially with respect to government revenues and expenditures. Therefore, more work is needed in this field.     

Financialization,Urban Governance and the Planning System: Utilizing ‘Development Viability’ as a Policy Narrative for the Liberalization of Ireland's Post‐Crash Planning System

Drawing upon the Irish case, this article explores the interaction between the financialized economy and the urban planning system. While considerable scholarship has examined the financialization of real estate, it remains unclear how planning systems are being repurposed to facilitate a finance‐led regime of urban growth or how the ‘real estate–financial complex’ seeks to enact planning policy transformations that support its interests. This article explores how such actors have advanced the concept of ‘financial viability of development’ as a means of influencing the post‐crisis re‐regulation of Irish planning policy. This group has argued that housing construction in post‐crash Ireland is unviable given the high development finance costs, onerous planning gain contributions and the lack of development certainty in the planning process. As such, housing construction has been at an all‐time‐low, leading to a new crisis in affordable housing provision. In response, a complicit state has further liberalized the planning system, introducing an array of policies that are evermore facilitative of development interests. Empirical findings, based on interviews with developers, lobbyists and planners, emphasize the importance of informal access to policymakers, the wielding of ‘expert knowledge’ and media management to co‐opt the state into adopting financial viability within planning policymaking.     

Dealing with Benchmark Revisions in Real‐Time Data: The Case of German Production and Orders Statistics*

Benchmark revisions in non‐stationary real‐time data may adversely affect the results of regular revision analysis and the estimates of long‐run economic relationships. Cointegration analysis can reveal the nature of vintage heterogeneity and guide the adjustment of real‐time data for benchmark revisions. Affine vintage transformation functions estimated by cointegration regressions are a flexible tool, whereas differencing and rebasing work well only under certain circumstances. Inappropriate vintage transformation may cause observed revision statistics to be affected by nuisance parameters. Using real‐time data of German industrial production and orders, the econometric techniques are exemplified and the theoretical claims are examined empirically.     

Horizontal vs. Vertical Interdependence in Multinational Activity*

This paper sheds light on interdependencies in multinational activity that are brought about by (horizontal) trade in final goods and (vertical) trade in intermediate goods (within and between host countries). We use a panel data set of US foreign affiliate sales to 16 developed countries in seven industries over the period 1983–2000, distinguish between horizontal and vertical interdependence in multinational enterprise activity and allow for both market size (demand)‐related as well as remainder linkage effects. Evidence suggests that vertical interdependence is somewhat more important than horizontal interdependence and, hence, vertical motives of multinational activity tend to dominate horizontal ones.

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Detection of Structural Change in the Long‐run Persistence in a Univariate Time Series*

In this paper, we investigate a test for structural change in the long‐run persistence in a univariate time series. Our model has a unit root with no structural change under the null hypothesis, while under the alternative it changes from a unit‐root process to a stationary one or vice versa. We propose a Lagrange multiplier‐type test, a test with the quasi‐differencing method, and ‘demeaned versions’ of these tests. We find that the demeaned versions of these tests have better finite‐sample properties, although they are not necessarily superior in asymptotics to the other tests.