"Aggregation Bias" DOES explain the PPP puzzle

This article summarizes our views on the role of an "aggregation bias" in explaining the PPP Puzzle, in response to the several papers recently written in reaction to our initial contribution. We discuss in particular the criticisms of Imbs, Mumtaz, Ravn and Rey (2002) presented in Chen and Engel (2005). We show that their contentions are based on: (i) analytical counter-examples which are not empirically relevant; (ii) simulation results minimizing the extent of "aggregation bias"; (iii) unfounded claims on the impact of measurement errors on our results; and (iv) problematic implementation of small-sample bias corrections. We conclude, as in our original paper, that "aggregation bias" goes a long way towards explaining the PPP puzzle

Measuring trade in value added with Firm-Level Data. NBB Working Paper No 378, November 2019

Global Value Chains have proliferated economic policy debates. Yet a key concept – trade in value added –is likely mismeasured because of sectoral aggregation bias stemming from reliance on inputoutput tables. This paper uses comprehensive firm-level data on both domestic and international transactions to study this bias. We find that sectoral aggregation leads to overstated trade in value added and, correspondingly, understated import content of gross exports. The economic magnitude of the estimated bias varies from moderate to large – at 2-5 p.p. of gross exports for Belgium and 17 p.p. for China. We study how the interplay between within-sector heterogeneities in firm import and export intensities and firm size determine the magnitude of the sectoral aggregation bias

Does consistent aggregation really matter?

Consistent aggregation ensures that behavioural properties which apply to disaggregate relationships apply also to aggregate relationships. The agricultural economics literature which has tested for consistent aggregation or measured statistical bias and/or inferential errors due to aggregation is reviewed. Tests for aggregation bias and errors of inference are conducted using indices previously tested for consistent aggregation. Failure to reject consistent aggregation in a partition did not entirely mitigate erroneous inference due to aggregation. However, inferential errors due to aggregation were small relative to errors due to incorrect functional form or failure to account for time series properties of data.Research Methods/ Statistical Methods,

DOES CONSISTENT AGGREGATION REALLY MATTER?

Consistent aggregation assures that behavioral properties, which apply to disaggregate relationships also, apply to aggregate relationships. The agricultural economics literature is reviewed which has tested for consistent aggregation or measured statistical bias and/or inferential errors due to aggregation. Tests for aggregation bias and errors of inference are conducted using indices previously tested for consistent aggregation. Failure to reject consistent aggregation in a partition did not entirely mitigate erroneous inference due to aggregation. However, inferential errors due to aggregation were small relative to errors due to incorrect functional form or failure to account for time series properties of data.Research Methods/ Statistical Methods,

ACCOUNTING FOR AGGREGATION BIAS IN ALMOST IDEAL DEMAND SYSTEMS

This study revisits the consistent aggregation (over households) property of almost ideal demand system (AIDS) models and presents a method to explicitly account for expenditure aggregation bias when estimating the aggregate AIDS model with time-series data. Ignoring aggregation bias can lead to biased and inconsistent parameter estimates and can cause aggregate demand functions to be inconsistent with the demand functions at the individual household level. Recognizing the general limited information contained in aggregate time-series data for explicitly modeling aggregation bias, we present a new method of constructing an aggregation bias term that is derived from the proportions of household in different income groups. This information is generally available in developed economies. We use this framework to estimate aggregate meat demand within a complete demand system based on U.S. annual expenditure data.Demand and Price Analysis,

Temporal Aggregation and Structural Inference in Macroeconomics

This paper examines the quantitative importance of temporal aggregation bias in distorting parameter estimates and hypothesis tests. Our strategy is to consider two empirical examples in which temporal aggregation bias has the potential to account for results which are widely viewed as being anomalous from the perspective of particular economic models. Our first example investigates the possibility that temporal aggregation bias can lead to spurious Granger causality relationships. The quantitative importance of this possibility is examined in the context of Granger causal relations between the growth rates of money and various measures of aggregate output. Our second example investigates the possibility that temporal aggregation bias can account for the slow speeds of adjustment typically obtained with stock adjustment models. The quantitative importance of this possibility is examined in the context of a particular class of continuous and discrete time equilibriurn models of inventories and sales. The different models are compared on the basis of the behavioral implications of the estimated values of the structural parameters which we obtain and their overall statistical performance. The empirical results from both examples provide support for the view that temporal aggregation bias can be quantitatively important in the sense of Significantly distorting inference.

Euro Area Inflation: Aggregation Bias and Convergence

EMU monetary policy targets aggregate Euro Area inflation. Concerns are growing that a focus on aggregate inflation may cause national inflation rates to diverge. While different explanations for diverging aggregate Euro Area inflation have been brought forward, the very impact of aggregation on divergence has however not been studied. We find a striking difference in convergence depending on the level of aggregation. While aggregate national inflation rates are diverging, disaggregate inflation rates are converging. We find that aggregation appears to bias evidence towards non-convergence. Our results are consistent with prominent theoretical and empirical evidence on aggregation biasEuro Area Inflation; Aggregation Bias; Convergence

"Aggregation Bias" DOES Explain the PPP Puzzle

This article summarizes our views on the role of an "aggregation bias" in explaining the PPP Puzzle, in response to the several papers recently written in reaction to our initial contribution. We discuss in particular the criticisms of Imbs, Mumtaz, Ravn and Rey (2002) presented in Chen and Engel (2005). We show that their contentions are based on: (i) analytical counter-examples which are not empirically relevant; (ii) simulation results minimizing the extent of "aggregation bias"; (iii) unfounded claims on the impact of measurement errors on our results; and (iv) problematic implementation of small-sample bias corrections. We conclude, as in our original paper, that "aggregation bias" goes a long way towards explaining the PPP puzzle.