Testing the Empirical Relevance of the ‘Saving for a Rainy Day’ Hypothesis in US Metro Areas

Anundsen, André K.; Nymoen, Ragnar

doi:10.1111/obes.12310

Cited by 3 publications

(3 citation statements)

References 88 publications

(110 reference statements)

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“…If this is not the case, the vector autoregression is misspecified and the impulse responses that it yields are potentially biased. 16 Coibion (2012) has drawn attention to the fact that in Romer and Romer's (2004) results, the peak responses of industrial production and unemployment to a change in the federal funds rate are roughly six times larger than in a standard monetary vector autoregression. In the online appendix to this paper, we revisit this issue by estimating the response of industrial production and the real interest rate to monetary shocks in six different ways.…”

Section: What Do We Do With These Shocks?mentioning

confidence: 99%

Identification in Macroeconomics

Nakamura

Steinsson

2018

Journal of Economic Perspectives

338

119

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This paper discusses empirical approaches macroeconomists use to answer questions like: What does monetary policy do? How large are the effects of fiscal stimulus? What caused the Great Recession? Why do some countries grow faster than others? Identification of causal effects plays two roles in this process. In certain cases, progress can be made using the direct approach of identifying plausibly exogenous variation in a policy and using this variation to assess the effect of the policy. However, external validity concerns limit what can be learned in this way. Carefully identified causal effects estimates can also be used as moments in a structural moment matching exercise. We use the term “identified moments” as a short-hand for “estimates of responses to identified structural shocks,” or what applied microeconomists would call “causal effects.” We argue that such identified moments are often powerful diagnostic tools for distinguishing between important classes of models (and thereby learning about the effects of policy). To illustrate these notions we discuss the growing use of cross-sectional evidence in macroeconomics and consider what the best existing evidence is on the effects of monetary policy.

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Section: What Do We Do With These Shocks?mentioning

confidence: 99%

Identification in Macroeconomics

Nakamura

Steinsson

2018

Journal of Economic Perspectives

338

119

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“…In recent years, in particular following the global financial crisis that became an income and job crisis, several empirical contributions have recognised the importance of debt and of housing prices in the complex processes that drive economic fluctuations in advanced economies with liberalised credit markets. For example, three studies with consistent findings for the US saving rate dynamics were presented by Carroll et al (2012), Mian et al (2013) and Anundsen and Nymoen (2019). Using a dataset with long historical time series for 14 advanced economies, Jordà et al (2013) concluded that financial factors play an important role in the modern business cycle (see also Anundsen et al (2016)).…”

Section: Introductionmentioning

confidence: 85%

Estimation of Effects of Recent Macroprudential Policies in a Sample of Advanced Open Economies

Nymoen

Pedersen²,

Sjåberg³

2019

IJFS

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We used a time-series cross-section dataset to test several hypotheses pertaining to the role of macroprudential policy instruments in the management of the financial cycle in advanced open economies. The short-run effects are most significant for caps on loan to value and income (LTV and LTI) and risk weights (RW). The long-run coefficients of credit growth with respect to the indicators of amortisation requirements (Amort) and RW are also significant. The estimation results when house price growth is the dependent variable are consistent with these results. Our findings do not support that Basel III type countercyclical buffer (CCyB) has affected credit growth, and we suggest that the variable is mainly a control in our dataset. In that interpretation, it is interesting that the estimated coefficients of the other instruments are robust with respect to exclusion of CCyB from the empirical models. The main results are also robust to controls in the form of impulse indicator saturation (IIS), which we employed as a novel estimation method for macro panels.

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“…We follow Eitrheim et al . (2002) and Anundsen and Nymoen (2019) and formulate the ‘saving for a rainy day’ hypothesis as

y_{t} - c_{t} \approx - {false\sum}_{i = 1}^{\infty} ρ^{i} E_{t} Δ y_{t + i} + ς,

where the saving ratio,

\frac{S_{t}}{Y L_{t}}

, is approximated by the logarithms of income to consumption ratio, y t − c t , and labour income, yl t + i , is replaced by income, y t + i . An important time series property, which we shall utilize in the nested CVAR, is that the saving ratio is stationary, I (0), and thus that income and consumption are cointegrated with a coefficient equal to one when income is non‐stationary, I (1).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

The Consumption Euler Equation or the Keynesian Consumption Function?*

et al. 2020

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We formulate a general cointegrated vector autoregressive (CVAR) model that nests both a class of consumption Euler equations and various Keynesian‐type consumption functions. Using likelihood‐based methods and Norwegian data, we find support for cointegration between consumption, income and wealth once a structural break around the time of the financial crisis is allowed for. The fact that consumption cointegrates with both income and wealth and not only with income points to the empirical irrelevance of an Euler equation. Moreover, we find that consumption equilibrium corrects to changes in income and wealth, but not that income equilibrium corrects to changes in consumption, which would follow from an Euler equation. We also find that most of the parameters stemming from the class of Euler equations are not corroborated by the data when conditional expectations of future consumption and income in CVAR models are considered. Only habit formation seems important in explaining Norwegian consumer behaviour. Our estimated conditional Keynesian‐type consumption function implies a first year marginal propensity to consume (MPC) out of income of close to 40%.

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Testing the Empirical Relevance of the ‘Saving for a Rainy Day’ Hypothesis in US Metro Areas

Cited by 3 publications

References 88 publications

Identification in Macroeconomics

Identification in Macroeconomics

Estimation of Effects of Recent Macroprudential Policies in a Sample of Advanced Open Economies

The Consumption Euler Equation or the Keynesian Consumption Function?*

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