Aggregate vs Disaggregate Data Analysis - a Paradox in the Estimation of a Money Demand Function of Japan Under the Low Interest Rate Policy

We use Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of a liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglected heterogeneity among micro units. We provide necessary and sufficient conditions for the existence of cointegrating relations among aggregate variables when heterogeneous cointegration relations among micro units exist. We also conduct simulation analysis to show that when such conditions are violated, it is possible to observe stable micro relations, but unit root phenomenon among macro variables. Moreover, the prediction of aggregate outcomes, using aggregate data is less accurate than the prediction based on micro equations and policy evaluation based on aggregate data ignoring heterogeneity in micro units can be grossly misleading.

[1]  P. Zaffaroni.  Contemporaneous aggregation of linear dynamic models in large economies , 2004 .

[2]  M. Pesaran.  Aggregation of linear dynamic models: an application to life-cycle consumption models under habit formation , 2003 .

[3]  S. Hall,et al.  S.G.B. Henry: a memoir and a festschrift essay , 2003 .

[4]  K. Abadir,et al.  Aggregation, Persistence and Volatility in a Macro Model , 2002 .

[5]  T. W. Anderson.  Reduced rank regression in cointegrated models , 2002 .

[6]  M. Hashem Pesaran,et al.  Cross-sectional aggregation of non-linear models , 2000 .

[7]  M. Pesaran.  On Aggregation of Linear Dynamic Models , 1999 .

[8]  Mario Forni,et al.  Aggregation of linear dynamic microeconomic models , 1999 .

[9]  Marco Lippi,et al.  Aggregation and the Microfoundations of Dynamic Macroeconomics , 1998 .

[10]  C. Hsiao.  Statistical Properties of the Two-Stage Least Squares Estimator Under Cointegration , 1997 .

[11]  Cheng Hsiao,et al.  A Panel Analysis of Liquidity Constraints and Firm Investment , 1997 .

[12]  Cheng Hsiao,et al.  Cointegration and Dynamic Simultaneous Equations Model , 1997 .

[13]  Arnold Zellner,et al.  Models, prior information, and Bayesian analysis☆ , 1996 .

[14]  Ryuzo Miyao.  Does a Cointegrating M2 Demand Relation Really Exist in Japan , 1996 .

[15]  M. Pesaran,et al.  Estimating Long-Run Relationships From Dynamic Heterogeneous Panels , 1995 .

[16]  H. Bohn.  The Sustainability of Budget Deficits in a Stochastic Economy , 1995 .

[17]  Arthur Lewbel,et al.  Aggregation with Log-Linear Models , 1992 .

[18]  S. Johansen.  Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models , 1991 .

[19]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[20]  M. Pesaran,et al.  Econometric Analysis of Aggregation in the Context of Linear Prediction Models , 1989 .

[21]  S. Johansen.  STATISTICAL ANALYSIS OF COINTEGRATION VECTORS , 1988 .

[22]  W. Fuller,et al.  LIKELIHOOD RATIO STATISTICS FOR AUTOREGRESSIVE TIME SERIES WITH A UNIT ROOT , 1981 .

[23]  C. Granger.  Long memory relationships and the aggregation of dynamic models , 1980 .

[24]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[25]  J. Hausman.  Specification tests in econometrics , 1978 .

[26]  Dale J. Poirier,et al.  A NOTE ON THE INTERPRETATION OF REGRESSION COEFFICIENTS WITHIN A CLASS OF TRUNCATED DISTRIBUTIONS , 1978 .

[27]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[28]  K. Brewer.  Some consequences of temporal aggregation and systematic sampling for ARMA and ARMAX models , 1973 .

[29]  G. C. Tiao,et al.  Asymptotic behaviour of temporal aggregates of time series , 1972 .

[30]  D. Lindley,et al.  Bayes Estimates for the Linear Model , 1972 .

[31]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[32]  K. P. Prodromidis,et al.  The Demand for Money: Theories and Evidence , 1970 .

[33]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[34]  Marc Nerlove,et al.  The Dynamics of Supply: Estimation of Farmers' Response to Price. , 1960 .

[35]  Z. Griliches,et al.  Is aggregation necessarily bad , 1960 .

[36]  H. Theil,et al.  Linear Aggregation of Economic Relations. , 1955 .

[37]  Kenneth J. Arrow,et al.  A Textbook of Econometrics. , 1954 .

[38]  Hiroshi Fujiki.  Money Demand near Zero Interest Rate: Evidence from Regional Data , 2002 .

[39]  C. Hsiao,et al.  Is There a Stable Money Demand Function under the Low Interest Rate Policy? A Panel Data Analysis , 2002 .

[40]  Hiroshi Fujiki,et al.  Monetary Policy under Zero Interest Rate --- Viewpoints of Central Bank Economists --- , 2001 .

[41]  B. Bernanke,et al.  Japanese Monetary Policy: A Case of Self-Induced Paralysis? , 2000 .

[42]  Cheng Hsiao,et al.  Bayes Estimation of Short-run Coefficients in Dynamic Panel Data Models , 1998 .

[43]  Werner Hildenbrand,et al.  Market Demand: Theory and Empirical Evidence , 1994 .

[44]  Arthur Lewbel,et al.  Aggregation and Simple Dynamics , 1994 .

[45]  Anna J. Schwartz,et al.  Alternative Approaches to Analyzing Economic Data , 1991 .

[46]  Daniel E. Sichel,et al.  The demand for money , 1990 .

[47]  P. Trivedi.  Distributed Lags, Aggregation and Compounding: Some Econometric Implications , 1985 .

[48]  D. Jorgenson,et al.  Transcendental Logarithmic Utility Functions , 1975 .

[49]  D. Laidler.  The Demand for Money: Theories and Evidence , 1973 .

[50]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[51]  Stephen M. Goldfeld,et al.  The Demand for Money Revisited , 1973 .

[52]  A. Deaton,et al.  COMPLETENESS , DISTRIBUTION RESTRICTIONS AND THE FORM OF AGGREGATE FUNCTIONS , 2022 .