1.3 Empirical Results
1.3.4 Illustrating the Impact of Major Events on Expectations
An extensive literature analyzes how structural shocks affect the dynamics of an econ-omy. Theoretical models are used to make predictions about the prospective reactions of economic variables to e.g. monetary policy (e.g. Mankiw and Reis, 2002) or uncertainty shocks (e.g. Bloom, 2009). The empirical literature studies whether predicted patterns appear in real-world data (e.g. Kilian 2009, Bernanke, Boivin, and Eliasz 2005). Without
13The model with no additional measurement arises if the loadingb is zero.
Table 1.3.3: Forecasting Experiment 1
Measure: MSFE relative MSFE (M SF E/M SF ERW)
Method: RW Quant. Qual. Qual.+∆r10 Qual.+∆oil Qual.+∆r1
Assessment Economic Situation (Germany)
Bal. 0.011 0.950 0.916·/NN 0.942·/· 0.917·/· 0.928·/·
Rise 0.005 0.927∗ 0.933·/· 0.929∗/· 0.905∗∗/·
Fall 0.003 0.925 0.972·/· 0.925·/· 0.982·/·
Assessment Economic Situation (United States)
Bal. 0.009 0.885∗∗ 0.901∗∗/HHH 0.902∗∗/· 0.904∗/· 0.903∗/·
Forecast Economic Situation (United States)
Bal. 0.014 0.918∗∗ 0.914∗/· 0.920∗/· 0.915∗/· 0.923∗/·
Forecast 3m-Interbank Rate (United States)
Bal. 0.022 0.890∗ 0.870∗/N 0.873∗/· 0.870∗/· 0.807∗∗/††
Rise 0.006 0.859∗ 0.846∗∗/· 0.862∗/· 0.827∗∗/††
Fall 0.011 0.904 0.908·/· 0.903·/· 0.820∗/††
The table documents the results of the first forecasting experiment, in which the next survey release is predicted from estimates produced on the last day of the current survey period. Column “MSFE” holds the mean squared forecast error of the random walk forecast. Columns titled “relative MSFE” show ratios of the MSFE of the method in question relative to the MSFE of the random walk forecast: “Quan.” is the method for quantitative responses (section 1.2.1) applied to the balance statistic of daily responses, “Qual.”
is the method for qualitative responses (section 1.2.2), “Qual.+∆r10/Qual.+∆oil/Qual.+∆r1” uses the method for qualitative responses augmented by an additional measurement variable (section 1.2.2.1) -where the first (r10) uses the day-to-day change in the yield on government bonds with a remaining maturity of nine to ten years, the second (oil) uses the day-to-day change in the log Brent crude oil price, and the third (r1) uses the change in the yield on government bonds with a remaining maturity of one year. The table summarizes results of different tests concerning forecast performance of the different methods: ∗/∗ ∗/∗ ∗∗ indicate significant rejections at the 10/5/1 percent levels of the hypothesis that the method in question does not yield better forecasts than the random walk following the Diebold and Mariano (1995) test; N/NN/NNNand H/HH/HHH indicate significant rejections at the 10/5/1 percent levels of the hypothesis that the model for qualitative responses forecasts the balance statistic equally well as the model for quantitative responses by the Diebold and Mariano (1995) test, whereHindicates that the null hypothesis is rejected in favor of the model for quantitative responses, and N indicates a rejection of equal predictive ability in favor of the model for qualitative responses. †/††/††† indicate significant rejections at the10/5/1percent levels of the hypothesis that the method in question does not forecast better than the method for qualitative forecasts with no additional measurement variable. The test is only applied to the methods that use an additional measurement. Therefore, the corresponding models nest the model with no additional measurement and we need to adjust the Diebold and Mariano (1995) test as suggested by Clark and West (2007). P-values for all tests are computed for one-sided alternative hypotheses.
Table 1.3.4: Forecasting Experiment 2
Measure: MSFE relative MSFE (M SF E/M SF ERW)
Method: RW Quant. Qual. Qual.+∆r10 Qual.+∆oil Qual.+∆r1
Assessment Economic Situation (Germany)
Bal. 0.011 0.950 0.917·/N 0.965·/· 0.889·/† 0.821∗∗/††
Rise 0.005 0.919∗ 0.926·/· 0.911∗/† 0.801∗∗∗/†††
Fall 0.003 0.930 1.006·/· 0.871·/· 0.865·/·
Assessment Economic Situation (United States)
Bal. 0.009 0.885∗∗ 0.893∗∗/· 0.849∗∗∗/†† 0.893∗∗/· 0.855∗/·
Forecast Economic Situation (United States)
Bal. 0.014 0.918∗∗ 0.904∗∗/NN 0.837∗∗/†† 0.904∗∗/· 0.912∗/·
Forecast 3m-Interbank Rate (United States)
Bal. 0.022 0.890∗ 0.848∗∗/NN 0.740∗∗/†† 0.846∗∗/· 0.753∗∗/†
Rise 0.006 0.845∗∗ 0.701∗∗∗/††† 0.839∗∗/· 0.801∗∗/††
Fall 0.011 0.877 0.801·/† 0.881·/· 0.800·/·
The table documents the results of the second forecasting experiment, in which the next survey release is predicted from estimates produced on the last day before the start of the following survey period.
Column “MSFE” holds the mean squared forecast error of the random walk forecast. Columns titled
“relative MSFE” show ratios of the MSFE of the method in question relative to the MSFE of the ran-dom walk forecast: “Quan.” is the method for quantitative responses (section 1.2.1) applied to the balance statistic of daily responses, “Qual.” is the method for qualitative responses (section 1.2.2),
“Qual.+∆r10/Qual.+∆oil/Qual.+∆r1” uses the method for qualitative responses augmented by an ad-ditional measurement variable (section 1.2.2.1) - where the first (r10) uses the day-to-day change in the yield on government bonds with a remaining maturity of nine to ten years, the second (oil) uses the day-to-day change in the log Brent crude oil price, and the third (r1) uses the change in the yield on government bonds with a remaining maturity of one year. The table summarizes results of different tests concerning forecast performance of the different methods: ∗/∗ ∗/∗ ∗∗ indicate significant rejections at the 10/5/1 percent levels of the hypothesis that the method in question does not yield better forecasts than the random walk following the Diebold and Mariano (1995) test; N/NN/NNN and H/HH/HHH indicate significant rejections at the 10/5/1 percent levels of the hypothesis that the model for qualita-tive responses forecasts the balance statistic equally well as the model for quantitaqualita-tive responses by the Diebold and Mariano (1995) test, where H indicates that the null hypothesis is rejected in favor of the model for quantitative responses, and N indicates a rejection of equal predictive ability in favor of the model for qualitative responses. †/††/†††indicate significant rejections at the10/5/1percent levels of the hypothesis that the method in question does not forecast better than the method for qualitative forecasts with no additional measurement variable. The test is only applied to the methods that use an additional measurement. Therefore, the corresponding models nest the model with no additional measurement and we need to adjust the Diebold and Mariano (1995) test as suggested by Clark and West (2007). P-values for all tests are computed for one-sided alternative hypotheses.
a doubt, expectations are an important channel in the transmission of a range of shocks to the real economy. In this section, I therefore choose some major economic events of the last few years, and analyze how they have changed expectations. One goal is to figure out whether my measure of expectations reacts in a plausible manner to such events and the shocks implicit in them.
In Figures 1.3.2a, 1.3.2b, and 1.3.3, I depict filtered and smoothed daily estimates14 of expectations (six-month horizon) about the economic situation in the U.S. or the Euro area around three major news events of the first decade of the 21st century. In each figure, I depict (1) the filtered (and smoothed) estimate of the balance of the probabilities of an optimistic responseπR,tand a pessimistic responseπF,tcomputed asE[πR,t|Yt]−E[πF,t|Yt] (and E[πR,t|YT] − E[πF,t|YT]), and (2) the balance statistic of the observed responses obtained by subtracting from the share of ”will rise” responses, the share of ”will fall”
responses on the respective day.
Figure 1.3.2a considers the impact of the September 11 attacks on economic expectations for the United States. The filtered estimate suggests a drop of more than 20 points in the next few days, which is a major deterioration of the economic outlook.15 The fact that the level of expectations before and after the event are relatively stable suggests that the attacks are the cause of the drop in expectations. By contrast, the smoothed estimate does not speak the same clear message: It suggests that expectations began to deteriorate before the actual event. Evidently, it tends to over-smooth transitions of the level of expectations before and after a shock.
Figure 1.3.2b considers an episode of the recent financial crisis in September and October 2008, when events started to overturn: On September 15, 2008 Lehman Brothers filed for the biggest bankruptcy in U.S. history, sending the Dow Jones down by almost five percent on a single day. On September 20, the U.S. government proposed a 700-billion-dollar bailout plan to address the severe situation of financial markets. At first, the plan was rejected by the House of Representatives on September 29, which brought about another seven percent slump in the value of the Dow Jones. A few days later, on October 3, the House approved a modified version of the plan, giving temporal relief to financial markets. In view of these events, the filtered estimate of expectations evolves in a counter-intuitive way: It increases shortly before the Lehman collapse, stays on the elevated level until the end of September, when it suddenly drops by more than 20 points. It takes the filtered estimate until the end of September to drop because no responses to the FMS
14Note, that the filtered estimate for datetuses all information datedtor earlier, whereas the smoothed estimate uses all information available until the end of the sample.
15For the balance to deteriorate by b points, if the share of optimistic responses goes back by r per-centage points, the share of pessimistic responses must rise by b−rpercentage points. A deterioration of 20 points would thus arise if e.g. the share of optimistic responses decreases by ten percentage points and the share of pessimistic responses increases accordingly.
were collected between September 16 and September 28. Evidently, the signal in long-term yields on government bonds was not sufficiently informative to guide the estimate.
In hindsight, the smoothed estimate produces a more sensible picture of the day-to-day time series of expectations. Specifically, the Lehman collapse is followed by a sudden ten-point drop in expectation, which is in turn followed by a short period of relief after the government’s proposal of the bailout plan. Thereafter, expectations slump by another 20 points at the time when uncertainty about the approval of the bailout plan is high.
Eventually, shortly after the plan is approved, expectation start to recover slowly but steadily.
Figure 1.3.3 depicts expectations with a six-month horizon about the economic situations in the Euro area around an episode of the sovereign debt crisis: In February 2012, the Greek government announced that it would offer a debt restructuring deal to its private creditors. The existence of a collective action clause meant that if two out of three creditors were to accept the offer, the remaining creditors could be forced into approving the deal.
It was questionable at the time, whether the offer would achieve the required acceptance rate and a success was important in at least two respects: First, the Eurogroup said that the deal was a crucial milestone of the second bailout package, on which the Greek government depended to sustain its financial solvency. Second, financial markets as well as European politicians were hoping for a success because of the fear that the sovereign debt crisis in the Euro area could worsen if Greece slipped into bankruptcy. There was great relief in the markets when, on March 8, 2012, the offer expired and it became public that 86% of creditors had accepted it, allowing an activation of the collective action clause.
In Figure 1.3.3, the filtered estimate shows that around March 8 expectations rose by roughly 15 points, reflecting that, in the eyes of German financial experts, the successful deal reduced the risk of contagion to other Euro area countries. Similar to the September 11 Attacks and for the same reasons outlined there, the smoothed estimate does not show the same abrupt change in expectations but rather a gradual shift.
The three cases considered suggest that when studying the impact of an event that falls in a survey period, the filtered estimate may be more meaningful, whereas between two survey periods one should rely on the smoothed estimate.