Abstract
Business investment could be dampened by weak aggregate demand, the high cost of capital and macroeconomic uncertainty. The importance of each factor may vary both over time and across countries. In this chapter we use a panel of advanced economies to estimate a model of business investment based on the above mentioned factors. The main objective is to understand, through time-varying parameters estimations, how their relative importance has changed over time, in particular after the global financial crisis. The analysis reveals that all three factors matter for investment, and suggests a key role for countercyclical policies aiming at lowering interest rates, supporting aggregate demand, and restoring confidence on financial markets against unfavorable macroeconomic and financial developments, such as those that followed the global financial crisis and the debt crisis.
The views expressed are those of the authors and do not necessarily reflect those of the Bank of Italy.
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Notes
- 1.
See Jorgenson and Siebert (1968) for a more detailed description of the accelerator model.
- 2.
- 3.
The countries are Australia, Austria, Belgium, Canada, Finland, France, Germany, Greece, Ireland, Italy, Japan, the Netherlands, New Zealand, Portugal, Spain, Sweden, Switzerland, the United Kingdom and the United States.
- 4.
For Austria, Belgium, Greece, Ireland, Italy, Portugal, Spain and Switzerland data on non-residential investment are obtained by the QNA dataset by subtracting residential capital expenditure from total (private plus public) gross fixed capital formation. This means that for these countries the dependent variable is total (instead of private) non-residential investment. Notice, however that all results hold when we consider this measure for all the available countries (that is all the countries in the dataset, with the exception of Japan). Moreover the correlation between the two variables is 0.83.
- 5.
See Ferrara et al. (2018) for a comprehensive review.
- 6.
See Bekaert et al. (2013) for a decomposition of the VIX into two components, an uncertainty measure and a proxy for risk aversion.
- 7.
On the other hand, the correlation between \(UNC_{it}\) and expected demand in our sample is negative and quite low (\(-0.2\)), confirming that expected demand does not capture uncertainty.
- 8.
For instance the correlation between the EPU index and our main proxy for economic uncertainty in our dataset is indeed very low (0.15).
- 9.
This is however only available for a limited number of countries, so when we include this measure in our analysis, we restrict the sample to G7 countries.
- 10.
Although firms’ borrowing cost may be more accurately reflected by yields on corporate bond, this measure is not available for all the countries in our sample, thus, following previous studies we use 10-year government bond and abstract from the differences between business and government borrowing rates.
- 11.
Analysis on the determinants of investment at the micro-level have shown the relevance of firm-specific credit constraints (see, for instance, Cingano et al. (2016) and Buono and Formai (2018)). It is unlikely that our aggregate measure of user cost of capital can fully capture the extents of credit constraints restraining firms’ investment decisions. Unfortunately it is extremely hard to obtain a micro-funded measure which is comparable across all the countries in our sample. Barkbu et al. (2015) proxy credit rationing for euro-area countries by using results from the European Commission’s consumer and business survey. Since, from the supply side, credit constraints may also arise in response to economic uncertainty, it is plausible that their effect is partially captured by our proxy for uncertainty.
- 12.
These are Greece in 1995h2 (42 per cent), and 2012h1 (\(-23\) per cent); Ireland in 2003h2 (38 per cent), 2012h1 (39 per cent) and in 2012h2 (\(-30\) per cent) and New Zealand in 1991h1 (\(-21\) per cent). We add back these observations in a robustness check.
- 13.
Results are robust when country fixed effects are omitted from the model.
- 14.
Given \(g_s\) a semi-annual growth rate and \(g_y\) the corresponding annualized rate, \(\Delta g_s=x\) implies \(\Delta g_y\approx 2x\), if \(g_s\) is small. In our data, the semi-annual investment growth rate is on average 0.02.
- 15.
In unreported regressions we also insert the past value of one-year-ahead GDP, which is not significant. We also try a specification with both the GDP nowcast and the 1-year-ahead GDP forecast. In this case we find that only the first is significant. However, we think this variable is seriously flawed by endogeneity: thus in our main analysis we use the one-year-ahead GDP forecast.
- 16.
In the regressions uncertainty is normalized by subtracting the country-specific mean and dividing by country-specific standard deviation.
- 17.
An alternative interpretation of our results could be a non-linear, but constant, relationship between uncertainty and investment growth. If uncertainty was increasingly detrimental on investment when getting larger, the higher uncertainty in the second part of the sample would result in a higher coefficient if the non-linearity was not taken into account. To exclude this interpretation, we run the main specification by adding a quadratic term for uncertainty: this is never significant, while the linear term is basically unchanged in magnitude, being higher after 2008h1, and almost significant at conventional levels.
- 18.
Only for the last observation, 2016h2, uncertainty contributed positively to the investment growth rate of most countries.
- 19.
There is a lively academic and policy debate on how the decrease in the price of investment goods—due to rapid advances in technology—is shaping labour and the capital share of firms’ production function.
- 20.
In an unreported analysis we find that in each country in the dataset there is at least one structural break (however not necessarily in 2008h2). This result is obtained by performing a series of Wald test over a range of possible break dates in the sample.
- 21.
As a robustness check we estimate the model by imposing \(n=15\) and the results are confirmed.
- 22.
See Buono and Formai (2016) for further discussion on these two weighting schemes.
- 23.
- 24.
Results are robust to different choices of H.
- 25.
As the Exponential Kernel is backward-looking, the estimates \(\beta _t\) start at \(t=1999\). On the other hand, for the Gaussain Kernel in the first part of the dataset, estimations are obtained by using only future information, and we chose only to show results from 1995 onwards.
- 26.
When we perform regressions separating the effect of the borrowing cost of capital from that of the relative price of investment, we find quite stable results, without any clear trend.
- 27.
The peripheral European countries include: Italy, Spain, Portugal, Ireland and Greece.
- 28.
We also check whether the estimated year-fixed effects exhibit any time trend, and this in not the case. We also could not find evidence in favor of a liner trend in any of our main variables. This is not that surprising, at least for investment, expected demand and user cost of capital, as they are taken as first differences.
- 29.
Australia, Canada, France, Germany, Ireland, Italy, Japan, Netherlands, Spain, Sweden, the United Kingdom and the United States.
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Buono, I., Formai, S. (2018). Explaining Weak Investment Growth After the Great Recession: A Macro-Panel Analysis. In: Ferrara, L., Hernando, I., Marconi, D. (eds) International Macroeconomics in the Wake of the Global Financial Crisis. Financial and Monetary Policy Studies, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-79075-6_8
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