Detrending and the Money-Output Link: International Evidence
Bibliographic record
Abstract
1. Introduction and Background The question of whether there exists an empirical link between nominal money and real output has been subjected to a variety of modern econometric techniques, producing conflicting results. For example, Stock and Watson (1989) use a vector autoregressive (VAR) model that accounts for several important economic variables and find that money exerts a statistically significant effect on real economic activity. Friedman and Kuttner (1992, 1993), on the other hand, show that using the same specification as Stock and Watson but extending their sample through the 1980s obviates the money-income link. Friedman and Kuttner's results indicate that interest rates are relatively more useful in explaining movements in output.' Thoma (1994) also reports that changes in money do not have a statistically significant impact on output in the United States. More recent studies, both theoretical and empirical, also have shown money to have little or no direct effect on economic cycles. Rudebusch and Svensson (1999, 2002), for example, conclude that the behavior of money (real or nominal) has no marginally significant impact on deviations of real output from potential (the output gap) once past movements in the gap and real rates of interest are accounted for. Such findings, on the basis of what Meyer (2001) refers to as the consensus macro model, have achieved an influential position among macroeconomists and policymakers.2 Other studies challenge the argument that money does not affect real output. An early study by Christiano and Ljungqvist (1988) using a bivariate VAR model reports the existence of a statistically significant money-to-output relation in U.S. data. Davis and Tanner (1997), using monthly U.S. data extending back to the mid-1800s, find that even after interest rate effects are allowed for, money remains statistically significant in explaining short-run movements in real output. Using a rolling regression approach, Swanson (1998) reports a statistically significant relation between money-measured as simple sum aggregates and as Divisia measures-and output, even after an interest rate spread variable is added to the model. Hafer and Kutan (1997) considered whether different stationarity properties of the data have influenced reported outcomes. Since most prior studies assume difference stationarity, Hafer and Kutan demonstrate that estimating VAR models that include money and interest rate variables under the existence of trend stationarity can dramatically affect the conclusion. Indeed, they find that using a trend-stationarity assumption yields the finding that money significantly affects real output movements in the United States. A common characteristic of this literature is its focus on the United States. There are a few exceptions. For example, Krol and Ohanian (1990) apply the Stock-Watson specification to data for Canada, Germany, Japan, and the UK. Although money (actual and detrended) significantly affects output in the UK, Krol and Ohanian find no such affect in Japan, Canada, and Germany. They conclude that although detrending the growth rate of U.S. money affects conclusions about the role of money, little is gained from this approach when applied to the other countries. Another exception to the U.S. focus is a recent study by Hayo (1999). Using data from 14 European Union (EU) countries plus Canada, Japan, and the United States, Hayo shows that money-output test results are not sensitive to the use of data in levels versus data in differences. An obvious question to ask, then, is whether nominal money is relatively more useful than interest rates in explaining movements in real output across a wider variety of countries that includes industrial and developing economies. Although the studies of Krol and Ohanian and Hayo represent a broader analysis, they too focus on the money-output relation in relatively industrialized, financially developed countries. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".