Comparisons of aggregate variable forecasts using aggregate and disaggregate models

When forecasting aggregate variables, a choice must often be made to either add up individual forecasts made at a disaggregate level or to simply forecast at the aggregate level. The presence of heterogeneity introduces aggregation bias and makes the disaggregates approach more preferable, while the presence of data and specification errors introduces relatively large variances in the disaggregate forecasts, making the aggregate approach more preferable. It is suggested that the mean square error is useful in evaluating the combined effects of heterogeneity and specification and data errors, and in facilitating comparisons between aggregate and disaggregate approaches to aggregate variable forecasting.     

Transferability of disaggregate mode choice models

Predictions of aggregate transport mode split for inter-city trips are derived from a disaggregate model of travel demand. A series of tests are performed to assess the limitations of the prediction methodology for disaggregate models, and it is shown that disaggregate models are capable of predictions across diverse travel situations. The disaggregate model predictions are compared with predictions derived from aggregate models such as are currently used in urban transportation planning, and it is shown that disaggregate models based on much smaller data sets predict better than aggregate models while requiring no more information about the predicted population.     

Oil Prices and Personal Consumption Expenditures: Does the Source of the Shock Matter?

This paper studies the effect of structural oil shocks on personal consumption expenditures (PCE). First, we estimate a nonlinear simultaneous equation model, compute impulse responses by Monte Carlo integration, and conduct a test of the symmetry of the impulse response functions. We find that aggregate PCE responds asymmetrically to positive and negative oil‐specific demand shocks. Second, we find that aggregate PCE responds negatively to positive oil demand shocks, while adverse oil supply shocks are of limited effect. Third, we find important heterogeneity in the magnitude, sign and timing of the disaggregate PCE responses to structural shocks in the crude oil market. Our results clearly indicate that the response of PCE to an unexpected oil price increase depends on the source of the oil price shock. Our findings are robust to different nonlinear transformations for the real price of oil.     

Demand for money in the Euro area

We analyzed the stability of the money demand function using panel data from January 1999 through March 2006, covering the 11 EU countries (Austria, Belgium, Finland, France, Germany, Ireland, Italy, Luxembourg, Netherlands, Portugal, and Spain). First, we found that the money demand function was stable with respect to M3. This arguably supports the suitability of the ECB's focus on M3 money supply in its monetary policy. Second, the stability of the money demand function was recognized with respect not only to M3, but also to M1 and M2. Accordingly, the ECB's adoption of M1 or M2 growth as a reference value should probably be considered depending on how conditions change going forward.     

The Instability of the Money Demand Function: An I(2) Interpretation

Some studies have suggested that although money and prices appear to be I(2) processes, real money balances are I(1) and this transformation preserves an important long-run relationship between money and prices. In this paper we present evidence indicating that the success of such a nominal-to-real transformation depends upon the particular monetary aggregate under consideration. It turns out that imposing long-run price homogeneity does not remove all I(2) components from a model of aggregate broad UK M4, but it does prove successful in the case of sectoral components of M4. Since recent research on money demand functions finds more stable relationships between sectoral components of M4 and aggregate demand, our analysis seems to point to a direct link between the existence of I(2) components and the stability of different money demand functions.     

Forecasting (aggregate) demand for US commercial air travel

We analyze whether it is better to forecast air travel demand using aggregate data at (say) a national level, or to aggregate the forecasts derived for individual airports using airport-specific data. We compare the US Federal Aviation Administration’s (FAA) practice of predicting the total number of passengers using macroeconomic variables with an equivalently specified AIM (aggregating individual markets) approach. The AIM approach outperforms the aggregate forecasting approach in terms of its out-of-sample air travel demand predictions for different forecast horizons. Variants of AIM, where we restrict the coefficient estimates of some explanatory variables to be the same across individual airports, generally dominate both the aggregate and AIM approaches. The superior out-of-sample performances of these so-called quasi-AIM approaches depend on the trade-off between heterogeneity and estimation uncertainty. We argue that the quasi-AIM approaches exploit the heterogeneity across individual airports efficiently, without suffering from as much estimation uncertainty as the AIM approach.     

On the Importance of Sectoral and Regional Shocks for Price‐Setting

We use novel disaggregate sectoral‐regional euro‐area data to investigate the sources of price changes, introducing a new method to extract factors from overlapping data blocks that allows for estimation of aggregate, sectoral, country‐specific and regional components of price changes. Our sectoral component explains much less variation in disaggregate inflation rates and exhibits much less volatility and more persistence than previous findings for the US indicate. Country‐ and region‐specific factors play an important role, emphasizing heterogeneity of inflation dynamics along both sectoral and geographical dimensions. Our results are incompatible with basic sticky‐information or Calvo‐type price‐setting models, but require multi‐sector, multi‐country models. Copyright © 2015 John Wiley & Sons, Ltd.     

Growth,unemployment and heterogeneity

The paper analyzes unemployment in a medium-run growth model, where aggregate demand and supply interact, using a top-down approach. The aim of the essay is the study of a nonlinear system where both aggregate demand and supply are endogenous and generate bounded unemployment, followed by a methodological effort direct to identify possible lines of convergence with the agent based models (ABM) approach. This is a by-product of the presence of heterogeneity in the model. Heterogeneity acts through two different channels and operates among class of agents: it comes into the aggregate consumption function where households are assumed employed or unemployed; it changes the learning process of pessimists and optimists. The analysis is carried on through simulations. The resulting system is fairly stable to changes in main structural parameters. On one hand, autonomous demand drives the dynamics of the system, while heterogeneity in the consumption function, due to the presence of unemployment, strengthens the links with supply aspects. On the other hand, both the rate of growth of labor productivity and labor supply are endogenous. Two major results are obtained. First, unemployment allows the so called Harrodian reconciliation between aggregate demand and supply. Second, unemployment remains bounded meaning that the interaction between aggregate demand and supply thwarts instability. These results are in keeping with those obtained by means of a bottom-up approach, typical of ABM. Possible explanations and implications of this convergence are put forward and open the venue to further deepening of complementarities among the two modeling strategies.

     

Linear prediction of temporal aggregates under model misspecification

This paper aims to demonstrate a possible aggregation gain in predicting future aggregates under a practical assumption of model misspecification. Empirical analysis of a number of economic time series suggests that the use of the disaggregate model is not always preferred over the aggregate model in predicting future aggregates, in terms of an out-of-sample prediction root-mean-square error criterion. One possible justification of this interesting phenomena is model misspecification. In particular, if the model fitted to the disaggregate series is misspecified (i.e., not the true data generating mechanism), then the forecast made by a misspecified model is not always the most efficient. This opens up an opportunity for the aggregate model to perform better. It will be of interest to find out when the aggregate model helps. In this paper, we study a framework where the underlying disaggregate series has a periodic structure. We derive and compare the efficiency loss in linear prediction of future aggregates using the adapted disaggregate model and aggregate model. Some scenarios for aggregation gain to occur are identified. Numerical results show that the aggregate model helps over a fairly large region in the parameter space of the periodic model that we studied.     

Inflationary equilibrium in a stochastic economy with independent agents

We prove the existence of stationary monetary equilibrium with inflation in a “Bewley” model with constant aggregate real variables but with idiosyncratic shocks to the endowments of a continuum of individual agents, when a central bank stands ready to borrow or lend fiat money at a fixed nominal rate of interest and the agents face borrowing constraints. We also find that, in the presence of real micro uncertainty about individual endowments, the rate of inflation is higher (equivalently, the real rate of interest is lower) than it would be in a “certainty-equivalent economy”; to wit, one in which every agent’s endowment is replaced by its expected value. Thus, underlying microeconomic uncertainty and borrowing constraints are shown to generate additional inflation.     

Inference in some disaggregated models with special covariance structure

This paper is concerned with parameter identification and estimation in linear structural equation models. We consider a special class of models which arises naturally when we disaggregate economic relations. We show that substantial gains in identification and estimation efficiency for ‘macro’ economic parameters can arise from disaggregation and analysis of the resulting ‘micro’ model.     

Refining Stylized Facts from Factor Models of Inflation

Factor models of disaggregate inflation indices suggest that sectoral shocks generate the bulk of sectoral inflation variance, but no persistence. Aggregate shocks, by contrast, are the root of sectoral inflation persistence, but have negligible relative variance. We show that simple factor models do not cope well with essential features of price data. In particular, sectoral inflation series are subject to features such as measurement error, sales and item substitutions. In factor models, these blow up the variance of sector‐specific shocks, while reducing their persistence. We control for such effects by estimating a refined factor model and find that inflation variance is driven by both aggregate and sectoral shocks. Sectoral shocks, too, generate substantial inflation persistence. Both findings contrast with earlier evidence from factor models, but align well with recent micro evidence. Our results have implications for the foundations of price stickiness, and provide quantitative inputs for calibrating models with sectoral heterogeneity. Copyright © 2014 John Wiley & Sons, Ltd.     

Consumption and labor supply

We present a new econometric model of aggregate demand and labor supply for the United States. We also analyze the allocation full wealth among time periods for households distinguished by a variety of demographic characteristics. The model is estimated using micro-level data from the Consumer Expenditure Surveys supplemented with price information obtained from the Consumer Price Index. An important feature of our approach is that aggregate demands and labor supply can be represented in closed form while accounting for the substantial heterogeneity in behavior that is found in household-level data. As a result, we are able to explain the patterns of aggregate demand and labor supply in the data despite using a parametrically parsimonious specification.     

Does Liquidity Matter? Properties of a Divisia Monetary Aggregate in the Euro Area*

This paper builds a quarterly Divisia monetary aggregate for the euro area using area‐wide data over the sample period from 1980 to 2000, finding two main results. First, it is found that the demand for this monetary aggregate has been well behaved and relatively stable over the last two decades. Secondly, the Divisia‐weighed monetary aggregate is found to have interesting information content from a forward‐looking perspective. This lends support to the view that money and – in a broader sense – liquidity services should be assigned an important role in shaping monetary policy in the euro area, although the policy maker is not interested in monetary aggregates per se.     

A PLS path model to investigate the relations between institutions and human development

The paper studies the relations between institutions and human development, in particular the causal effects of the different types of institutions on different components of human development. We assume development to be created by aggregate demand; in particular that aggregate demand determines the material components of human development. We thus divide institutions into those that create demand and those that are determined by the whole process of development. Similarly we divide human development in its three traditional components (economic development, health, knowledge). Both human development and institutions are assumed to be multidimensional constructs; all the main components of these constructs are defined as latent variables, and the relations between them as structural relations. A partial least squares (PLS) path model is developed: it is the aggregation (and simultaneous estimation) of an outer model relating observed or manifest variables to their own latent variable and of a structural model (inner model) relating some endogenous latent variable to other latent variables. From the goodness of fit point of view, our results seem to validate our theoretical assumptions.     

Disaggregate evidence on the persistence of consumer price inflation

This paper uses disaggregate inflation data spanning all of consumption to examine: (i) the persistence of disaggregate inflation relative to aggregate inflation; (ii) the distribution of persistence across consumption sectors; and (iii) whether persistence has changed. Assuming mean inflation to be unchanged, disaggregate persistence inflation is consistently below aggregate persistence. Taking into account an early 1990s shift in mean inflation identified by break tests yields much lower estimates of both aggregate and disaggregate persistence for 1984–2002. But with the mean break, average disaggregate persistence is actually as great as aggregate inflation persistence. A factor model provides a natural framework for interpreting the relationship between aggregate and disaggregate persistence. Copyright © 2006 John Wiley & Sons, Ltd.     

Towards estimating the demand for money in China

The demand for money in China is estimated separately for the periods before and after the economic reform. Besides the traditional transactions demand variable, the expected rate of inflation (as a measure of the opportunity cost of holding money) and the monetization process are also incorporated into the demand function. The preliminary results show that the demand for money in China has changed in response to the institutional changes during the economic reform. Adding the monetization and inflation expectation variables into the money demand function has enhanced significantly its explanatory power.     

Money demand and the role of monetary indicators in forecasting euro area inflation

This paper examines the stability of money demand and the forecasting performances of a broad monetary aggregate (M3), excess liquidity and excess inflation in predicting euro area inflation. The out-of sample forecasting performances are compared to a widely used alternative, the spread of interest rates. The results indicate that the evolution of M3 is still in line with money demand, even when observations from the economic and financial crisis are included. Both excess measures and the spread are useful for predicting inflation.     

Aggregate Versus Disaggregate Data in Measuring School Quality

This article develops a measure of efficiency to use with aggregated data. Unlike the most commonly used efficiency measures, our estimator adjusts for the heteroskedasticity created by aggregation. Our estimator is compared to estimators currently used to measure school efficiency. Theoretical results are supported by a Monte Carlo experiment. Results show that for samples containing small schools (sample average may be about 100 students per school but sample includes several schools with about 30 or less students), the proposed aggregate data estimator performs better than the commonly used OLS and only slightly worse than the multilevel estimator. Thus, when school officials are unable to gather multilevel or disaggregate data, the aggregate data estimator proposed here should be used. When disaggregate data are available, standardizing the value-added estimator should be used when ranking schools.     

Robust estimation for structural spurious regressions and a Hausman-type cointegration test

This paper analyzes an approach to correcting spurious regressions involving unit-root nonstationary variables by generalized least squares (GLS) using asymptotic theory. This analysis leads to a new robust estimator and a new test for dynamic regressions. The robust estimator is consistent for structural parameters not just when the regression error is stationary but also when it is unit-root nonstationary under certain conditions. We also develop a Hausman-type test for the null hypothesis of cointegration for dynamic ordinary least squares (OLS) estimation. We demonstrate our estimation and testing methods in three applications: (i) long-run money demand in the U.S., (ii) output convergence among industrial and developing countries, and (iii) purchasing power parity (PPP) for traded and non-traded goods.