Econometric modelling of Canadian long distance calling: A comparison of aggregate time series versus point-to-point panel data approaches

Aggregate time series and point-to-point panel data approaches to modelling Canadian long distance calling are compared. It is found that, while both models yield similar sets of aggregate elasticities, the point-to-point panel data models provide more detailed information on consumer behavior. Furthermore, the panel data models take advantage of the disaggregate data available and avoid the problems inherent in aggregate models when the rates of changes in the micro data are different.Cheng Hsiao's work is supported in part by National Science Foundation grant SES 88-21205. We wish to thank referees for helpful comments.     

Macroeconomic forecasting in the Euro area: Country specific versus area-wide information

This paper compares several time series methods for short-run forecasting of Euro-wide inflation and real activity using data from 1982 to 1997. Forecasts are constructed from univariate autoregressions, vector autoregressions, single equation models that include Euro-wide and US aggregates, and large-model methods in which forecasts are based on estimates of common dynamic factors. Aggregate Euro-wide forecasts are constructed from models that utilize only aggregate Euro-wide variables and by aggregating country-specific models. The results suggest that forecasts constructed by aggregating the country-specific models are more accurate than forecasts constructed using the aggregate data.     

A product diffusion model incorporating repeat purchases

A logistic-based model for forecasting the rate of product diffusion given aggregate time series data was constructed. The model differs from earlier models based on fitting the logistic to aggregate data in that it includes a submodel to separate replacement demand from first-time sales. We fit the theoretical model to data and show that forecasts will be significantly more accurate using this model instead of the logistic curve.     

Gaussian estimation of a second order continuous time macroeconometric model of the UK

This paper describes the first application to an economy-wide macroeconometric model of recently developed methods for the exact Gaussian estimation of higher order continuous time dynamic models. The new model is formulated as a system of second order differential equations, thus providing a much richer dynamic specification than the predominantly first order continuous time macroeconometric models developed during the last 15 years. It also makes intensive use of economic theory to obtain a parsimonious parametrization, is designed in such a way as to permit a rigorous mathematical investigation of its steady state and asymptotic stability properties, and makes systematic use of the assumption of long-run rational expectations. In addition to the exact Gaussian estimates of the structural parameters, the paper includes the first set of continuous lag distributions derived from estimates that take account of the exact restrictions on the distribution of the discrete data implied by a continuous time model. It also includes the first estimates of the coefficient matrices of the exact discrete model, in its VARMAX form, satisfied by the discrete stock and flow data generated by a higher order continuous time dynamic model.     

The Uzawa–Lucas model without scale effects: theory and empirical evidence

Numerous cross-sectional tests have been performed to evaluate the predictions of recent growth theories such as the Uzawa–Lucas growth model. In a series of papers and in his book, Jones [Q. J. Econ. 110 (1995a) 495; J. Political Econ. 103 (1995b) 759; The upcoming slowdown in US economic growth, Stanford University, Stanford] has shifted the attention toward the time series predictions of endogenous growth models. By contrasting endogenous growth models with facts, one is frequently confronted with the prediction that levels of economic variables, such as human capital, imply lasting effects on the per capita growth rate of an economy. As stylized facts show, measures of education or human capital in most advanced countries have dramatically increased, mostly more than the gross domestic production (GDP). Yet, the growth rates have roughly remained constant or even declined. In this paper we modify the growth effects of education and human capital in our variant of the Uzawa–Lucas growth models and test the model using time series data for the US and Germany from 1962.1 to 1996.4. We consider two versions. In the first, we treat the time spent for education as exogenously given and we neglect the external effect of human capital. In the second version, the time spent for education is an endogenous variable and the external effect of human capital is taken into account. Our results demonstrate that the model is compatible with the time series for aggregate data in those countries. The parameters fall into reasonable ranges.     

On the distributional effects of income in an aggregate consumption relation

Abstract .  In this paper we analyse the influence of characteristics of the income distribution in modelling aggregate consumption expenditure. We model the aggregate consumption relation of a heterogeneous population, using a statistical distributional approach of aggregation, and apply it to UK-Family Expenditure Survey data. A bootstrap test based on a non-parametric estimation methodology, which accounts for the presence of continuous and discrete variables, suggests that the mean and the dispersion of the income distribution significantly influence aggregate consumption expenditure. Also, the parameters of the aggregate relation are time varying. These findings have implications for constructing empirically sound models of aggregate consumption expenditure.     

Asymmetry in the business cycle: Evidence from the Australian labour market

Cyclical asymmetry has been recognised as a non-linear phenomenon in recent studies examining unemployment rate time series. The probalistic structure of such time series is different during upswings and downswings and models should reflect this change in structure by incorporating non-linearities to allow for the switching in optimising behaviours between the different phases. In this paper we use a number of established and new tests for identifying nonlinearities of different types as they occur in first-differenced time series of the seasonally-adjusted monthly Australian aggregate and regional unemployment rates. After identifying whether nonlinearity of a particular form is present in a given time series, the appropriate non-linear or linear model is fitted and the model analysed for cyclical behaviour.     

Trend-reverting fluctuations in the life-cycle model

Aggregate time series provide evidence of short-term dynamic adjustment that appears to be governed by complex or negative real eigenvalues. This finding is at odds with the predictions of reasonably parameterized, convex one-sector growth models with complete markets. We study life-cycle economies in which aggregate saving depends non-trivially on the distribution of wealth among cohorts. If consumption goods are weak gross substitutes near the steady-state price vector, we prove that the unique equilibrium of a life-cycle exchange economy converges to the unique non-monetary steady state via damped oscillations. We also discuss examples and extensions.     

Politico-economic inequality and the comovement of government purchases

This paper explores the implications of economic and political inequality for the comovement of government purchases with macroeconomic fluctuations. We set up and compute a heterogeneous-agent neoclassical growth model, where households value government purchases which are financed by income taxes. A key feature of the model is a wealth bias in the political aggregation process. When calibrated to U.S. wealth inequality and exposed to aggregate productivity shocks, such a model is able to generate weaker positive comovement of government purchases than models with no political wealth bias. The wealth bias that matches the cross-sectional campaign contribution distribution by income is consistent with the mild positive comovement of government purchases in the aggregate data. We thus provide an empirically relevant example where economic and political heterogeneity matter for aggregate dynamics.     

A structural time series approach to modelling multiple and resurgent meat scares in Italy

This paper exploits a structural time series approach to model the time pattern of multiple and resurgent food scares and their direct and cross-product impacts on consumer response. A structural time series Almost Ideal Demand System (STS-AIDS) is embedded in a vector error correction framework to allow for dynamic effects (VEC-STS-AIDS). Italian aggregate household data on meat demand is used to assess the time-varying impact of a resurgent BSE crisis (1996 and 2000) and the 1999 Dioxin crisis. The VEC-STS-AIDS model monitors the short-run impacts and performs satisfactorily in terms of residuals diagnostics, overcoming the major problems encountered by the customary vector error correction approach.     

Wage led aggregate demand in the United Kingdom

The wage led aggregate demand hypothesis is examined for the United Kingdom over the period 1971–2007. Existing studies disagree on the aggregate demand regime for the UK, and this appears to be due to differing empirical approaches. Studies relying on equation-by-equation estimation procedures tend to find support for wage led aggregate demand in the UK, while the single study using a multiple time series estimation procedure finds no support for the hypothesis. We test the wage led aggregate demand hypothesis in the UK using VAR models estimated on quarterly data employing an alternative identification strategy based on shocks to real earnings. The results provide support for the wage led aggregate demand hypothesis during the period of study. However, the expansionary effects of higher earnings seem to be limited and relatively short-lived.     

Rational Pessimism, Rational Exuberance, and Asset Pricing Models

The paper estimates and examines the empirical plausibility of asset pricing models that attempt to explain features of financial markets such as the size of the equity premium and the volatility of the stock market. In one model, the long-run risks (LRR) model of Bansal and Yaron, low-frequency movements, and time-varying uncertainty in aggregate consumption growth are the key channels for understanding asset prices. In another, as typified by Campbell and Cochrane, habit formation, which generates time-varying risk aversion and consequently time variation in risk premia, is the key channel. These models are fitted to data using simulation estimators. Both models are found to fit the data equally well at conventional significance levels, and they can track quite closely a new measure of realized annual volatility. Further, scrutiny using a rich array of diagnostics suggests that the LRR model is preferred.     

Intertemporal substitution and random changes in preferences: Time series evidence for four countries

This study attempts to estimate a model of intertemporal substitution taking into account serially correlated changes in preferences with quarterly aggregate time series data for the USA, Great Britain, West Germany and Switzerland. Thereby, necessary optimality conditions representing the trade-offs between present and future consumption, present and future leisure, as well as present consumption and leisure are estimated using the forward-filtered estimator of Hayashi and Sims. The estimation results obtained for the dynamic optimality conditions reject the model formulated. The analysis of the static optimality conditions gave more satisfactory results with respect to intertemporal substitution elasticity in consumption.     

Wind Power in Europe: A Simultaneous Innovation–Diffusion Model

The purpose of this paper is to provide a quantitative analysis of innovation and diffusion in the European wind power sector. We derive a simultaneous model of wind power innovation and diffusion, which combines a rational choice model of technological diffusion and a learning curve model of dynamic cost reductions. These models are estimated using pooled annual time series data for four European countries (Denmark, Germany, Spain and the United Kingdom) over the time period 1986–2000. The empirical results indicate that reductions in investment costs have been important determinants of increased diffusion of wind power, and these cost reductions can in turn be explained by learning activities and public R&D support. Feed-in tariffs also play an important role in the innovation and diffusion processes. The higher is the feed-in price the higher is, ceteris paribus, the rate of diffusion, and we present some preliminary empirical support for the notion that the impact on diffusion of a marginal increase in the feed-in tariff will differ depending on the support system used. High feed-in tariffs, though, also have a negative effect on cost reductions as they induce wind generators to choose high-cost sites and provide fewer incentives for cost cuts. This illustrates the importance of designing an efficient wind energy support system, which not only promotes diffusion but also provides continuous incentives for cost-reducing innovations.      

Forecast accuracy of small and large scale dynamic factor models in developing economies

Developing economies usually present limitations in the availability of economic data. This constraint may affect the capacity of dynamic factor models to summarize large amounts of information into latent factors that reflect macroeconomic performance. This paper addresses this issue by comparing the accuracy of two kinds of dynamic factor models at GDP forecasting for six Latin American countries. Each model is based on a dataset of different dimensions: a large dataset composed of series belonging to several macroeconomic categories (large scale dynamic factor model) and a small dataset with a few prescreened variables considered as the most representative ones (small scale dynamic factor model). Short‐term pseudo real time out‐of‐sample forecast of GDP growth is carried out with both models reproducing the real time situation of data accessibility derived from the publication lags of the series in each country. Results (i) confirm the important role of the inclusion of latest released data in the forecast accuracy of both models, (ii) show better precision of predictions based on factors with respect to autoregressive models and (iii) identify the most adequate model for each country according to availability of the observed data.     

THE TIME‐SERIES PROPERTIES OF UK INFLATION: EVIDENCE FROM AGGREGATE AND DISAGGREGATE DATA

This paper contrasts the time‐series properties of aggregate and disaggregate UK inflation. While aggregate inflation is found to be non‐stationary, unit root rejection frequencies are increasing when we use more disaggregate data. Structural break analysis suggests that structural shifts in monetary policy could alter inflation persistence. Additionally, panel evidence indicates that the unit root hypothesis can be rejected for sectoral inflation rates. Finally, we compare the persistence properties of UK inflation, finding statistically significant differences between aggregate and disaggregate series. Our analysis suggests that aggregation matters, which has important implications for econometric analysis and the conduct of monetary policy.     

Aggregation, Persistence and Volatility in a Macro Model

We introduce firm heterogeneity into the standard monopolistically competitive real business cycle (RBC) model. The fundamental equilibrium path is derived and the time–series properties of aggregate GDP are studied analytically. Although firms' productivities are subject to temporary shocks, the aggregate process displays a surprising novel form of nonlinearity and long memory which had not been built into the model at the outset. This aggregate GDP turns out to have very different properties from log–linear time–series models such as auto–regressive (AR) models and their extensions. It displays very strong persistence, which ends abruptly with a sudden change of tendency, giving its autocorrelation function (ACF) an S –shape. Although persistent, it is mean–reverting, unlike the everlasting memory of unit–root processes. Its volatility is of a greater order of magnitude than that of any of its components, so small micro–shocks can generate large macro fluctuations. It is also characterized by long, asymmetric cycles of random lengths. Increased monopoly power tends to reduce the amplitude and increase the persistence of business cycles. Strikingly, we find that the empirical ACFs constructed from GDP data for the U.K. and the U.S. display this characteristic S –shape.     

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 models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth 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.     

Macroeconomic effects of fiscal policy changes: A case of South Africa

This study develops comprehensive full-sector macro-econometric models for the South African economy with the aim of explaining and providing the macroeconomic effects of fiscal policy changes in the country. The models are applied to test the effectiveness of fiscal policy actions in an economic environment with existing structural supply constraints versus demand-side constraints and also to detect which components of the fiscal would be more effective in stabilising the economy. Based on the structure of the South African economy and the framework presented, the study concludes that the South African economy can be characterised as one which is embedded with structural supply constraints. Thus, a model which is suitable for policy analyses of the South African economy needs to capture the long-run supply-side characteristics of the economy. A price block is incorporated to specify the price adjustment between the supply-side sector and real aggregate demand sector. The models are estimated with time-series data from 1970 to 2011, capturing both the long-run and short-run dynamic properties of the economy. The results from the series of fiscal policy scenarios suggest that fiscal policy actions are more effective in an economic environment with limited or no supply constraints. Fiscal expansion or consolidation that comes more from government spending changes will be more effective in an economic environment where structural supply constraints are absent while tax revenue changes will be more effective in an economic environment where there exist major structural supply constraints.