# RDP 2020-08: Start Spreading the News: News Sentiment and Economic Activity in Australia 5. News Sentiment and the Economy

## 5.1 Identification

To identify the effect of changes in news sentiment on both consumer and business sentiment as well as other economic activity indicators, we loosely follow the model of Barsky and Sims (2012). In particular, we estimate impulse response functions using the local projections (LP) method of Jordà (2005). This is similar to the standard vector autoregression (VAR) approach but less restrictive. However, the non-parametric nature of LP comes at an efficiency cost and may suffer from excessive variability. Thus, we also estimate impulse responses using the B-splines smooth local projections (SLP) method proposed by Barnichon and Brownless (2019), which aims at striking a balance between the VAR and LP approaches. The SLP method preserves the flexibility of LP while offering efficiency gains by imposing that impulse responses are polynomial functions of the forecast horizons.

In effect, the identification strategy is the same as estimating a VAR with economic activity, consumer sentiment and news sentiment to consider the partial derivatives of the activity indicators at various horizons with respect to innovations in the news sentiment indicator. The result coincides with that of a recursive system with the news sentiment variable ordered last (Plagborg-Møller and Wolf 2019). In this setting, we define a temporary ‘shock’ as the change in news sentiment that is orthogonal to current and lags of all other variables in the regression. We are not able to distinguish shocks to ‘news’ (or fundamentals such as productivity) from shocks to ‘sentiment’ (or non-fundamental ‘animal spirits’).

Specifically, for each forecast horizon, h, we run a regression of the relevant activity indicator, ${\gamma }_{i}$, on contemporaneous and lagged values of all relevant economic variables including itself, Yt, and of the NSI:

(1) $y i,t+h = α i h + β i h NS I t + ∑ τ=1 Q δ i,τ h NS I t−τ + A i h ∑ t=0 Q Y t−τ + ε i,t+h$

where the lag length, Q, is chosen by a combination of information selection criteria. The impulse responses of the economic variable to a change in news sentiment are traced out by the estimates of the $\beta$s in Equation (1). To construct the confidence bands, we adopt the Barnichon and Brownlees (2019) heuristic procedure and estimate the asymptotic variance of the B-splines parameters using the Newey-West estimator.

## 5.2 Response of Survey-based Sentiment to a News Sentiment Shock

Survey-based measures of sentiment are closely monitored by economic analysts and policymakers as timely indicators of economic activity (Hüfner and Schröder 2002; Aylmer and Gill 2003). Figure 5 plots the NSI along with the ANZ–Roy Morgan consumer sentiment index (CSI) over time, where both series are normalised to have a mean of zero and a standard deviation of one. The two series have a correlation coefficient of 0.52 over the full sample, and the correlation is only slightly weaker when economic downturns are removed from the sample.

We also show that news sentiment appears to lead the survey-based consumer sentiment measure by estimating Equation (1) where yi is the consumer sentiment index. We choose four lags for the weekly model and two lags for the monthly model, as suggested by a combination of information selection criteria. We find that a temporary one standard deviation increase in the weekly NSI (7 net positive words per 1,000 words) is associated with 3 to 4 per cent of a standard deviation increase in the CSI (roughly half an index point) in the following few weeks (Figure 6). Similarly, a one standard deviation increase in the monthly NSI is associated with 3 to 5 per cent of a standard deviation increase in the CSI over a similar timeframe. The responses get weaker as the horizon lengthens and become statistically insignificant after roughly 12 periods. So we find that the NSI can predict movements in consumer sentiment, though the size of the effect is small, which argues against a causal interpretation.

## 5.3 Response of Economic Activity to News Sentiment Shock

To evaluate the effect of changes in news sentiment on economic activity, we estimate Equation (1) on a monthly basis with two lags as chosen by a combination of information selection criteria. The activity indicators included are the NAB measure of current business conditions (to capture overall economic activity), the NAB measure of capital spending (to proxy for business investment) and the change in the unemployment rate (to capture labour market conditions).[5] We also include the NAB survey-based measure of current business conditions as a control variable.[6]

Figure 7 presents the impulse responses of the monthly indicators to a temporary news sentiment shock, which are traced out by the estimates of the $\beta$s in Equation (1). The results indicate that a temporary increase in news sentiment is associated with higher sentiment about business conditions, higher capital spending and a lower change in the unemployment rate.

The effect of a NSI shock on capital spending is persistent, staying positive for almost 12 months, while the effect on unemployment is shorter lived. The effects are statistically significant even after controlling for the business conditions index, suggesting that the text-based sentiment measure contains information orthogonal to survey-based measures. While this is again suggestive of a causal role for the NSI, we cannot rule out the possibility that the NSI is simply a better measure of current economic activity than some of the other survey indicators.

In terms of economic magnitude, a one standard deviation shock to news sentiment is associated with capital spending rising by about 10 to 15 per cent of a standard deviation and the change in unemployment rate falling by 0.01 to 0.03 percentage points in the months following the shock. The effect on capital spending appears meaningful but the effect on the unemployment rate is very small. By comparison, these responses are faster (and about the same size) than the responses to a one standard deviation shock to business conditions (Figure 8).

We also run similar but separate regressions to estimate the responses of economic indicators to news uncertainty shocks. Figure 9 shows that a temporary increase in the NUI is associated with lower business conditions, lower capital spending and higher unemployment. The responses to both NSI and NUI shocks remain generally unchanged having controlled for each other (Appendix A), suggesting that both news sentiment and uncertainty have independent roles to play in predicting economic activity in a timely fashion.

Taken together, we believe this analysis is suggestive of a causal interpretation in that changes in news sentiment drive survey-based measures of sentiment and economic activity more generally. In particular, changes in news-based sentiment precede movements in survey-based sentiment measures. This may be because consumers rely on high-frequency information that is broadcast through the news media. Also, the NSI predicts changes in business conditions over and above survey-based sentiment indicators and measures of current business conditions. So the information in news media may be capturing a broad audience and be more representative than survey data.

However, there are reasons to be cautious about a causal interpretation. For instance, the size of the effect of news sentiment on economic activity is small. Relatedly, in unreported results, we find that changes in news sentiment cannot explain broad indicators of business conditions, such as GDP growth. This suggests the NSI provides limited information content at a lower (quarterly) frequency. So, overall, it may be that changes in the NSI provide a very timely (and well measured) indicator of fluctuations in economic activity rather than causing changes in the behaviour of consumers and business managers. Future research could look to separate the effects of sentiment and news shocks to more directly test for causality.

## Footnotes

Apart from the unemployment rate, these measures are probably best thought of as capturing changes in nominal economic conditions rather than real activity. [5]

Estimates using other controls such as the consumer sentiment index and the business confidence index yield similar results and can be found in Appendix A. [6]