Forecasting Exports Across Europe: What are the Superior Survey Indicators?

In this study, we systematically evaluate the potential of a bunch of survey-based indicators from different economic branches to forecasting export growth across a multitude of European countries. Our pseudo out-of-sample analyses reveal that the best-performing indicators beat a well-specified benchmark model in terms of forecast accuracy. It turns out that four indicators are superior: the Export Climate, the Production Expectations of domestic manufacturing firms, the Industrial Confidence Indicator, and the Economic Sentiment Indicator. Two robustness checks confirm these results. As exports are highly volatile and turn out to be a large demand-side component of gross domestic product, our results can be used by applied forecasters in order to choose the best-performing indicators and thus increasing the accuracy of export forecasts.

Rationing car use based on license plate number has become a popular policy to address congestion and air pollution in heavily populated cities, 1 as opposed to the fuel taxes and road pricing widely suggested in the literature (see Sterner and Coria 2012). Several cities around the world have implemented such driving restrictions, e.g., Mexico City, Santiago, Sao Paulo, Bogotá, Quito, Beijing, Athens, and Paris. This paper examines the effects of the Pico y Placa (PYP) driving restriction program on car use and air pollution in Bogotá.
The research to date has focused on assessing the overall effectiveness of these schemes; however, no study has analyzed the effects of phased-in, or progressively implemented, programs. An appealing feature of driving restrictions is that they can be introduced in progressive stages. Gradual implementation may be appropriate in response to political resistance and public opposition as such factors may impede the immediate introduction of drastic restrictions. Since the increased stringency may potentially alter households' responsiveness to the program, investigating how the response varies across implementation stages and affects the effectiveness of the program is of great importance. Answering these questions is relevant for policy design and enriches the debate on the replication of phased-in programs.
To the best of my knowledge, this is the first paper on how drivers respond to a phased-in program. Bogotá introduced PYP in August 1998, restricting driving during peak hours (moderate restriction) to reduce congestion caused by light vehicles. In February 2009, the restriction was intensified, extending the program to 14 hours per day (drastic restriction) to further reduce congestion and traffic emissions. Although the 1. For example, drivers stuck on the most congested highway in the United States waste time and fuel equivalent to an annual cost of US$95 million (Texas A&M Transportation Institute 2011). Moreover, about 1.34 million premature deaths worldwide per year are attributable to outdoor air pollution (WHO 2011). government has released some reports on the program implementation (see Secretaría de Movilidad 2010), there is no reliable evidence on the effectiveness of the restrictions.
Rationing car use may have other potential benefits; apart from addressing congestion and air pollution, it might also reduce the crash risk and road and parking costs. 2 Nonetheless, studies on the effectiveness of driving restrictions yield conflicting conclusions. Eskeland and Feyzioglu (1997a), Davis (2008), and Gallego, Montero, and Salas (2013) found that the Hoy No Circula (HNC) program in Mexico City, which banned most drivers from using their vehicles one weekday per week, was ineffective.
HNC induced many households to buy additional cars (mainly old and highly polluting ones). In contrast, Carrillo et al. (in press) found that driving restrictions in Quito reduced air pollution by 11 percent. In the latter case, the uncertainty about the program's permanency may have kept households from buying a second car. Viard and Fu (2015) also report reduced air pollution after driving restrictions were introduced in Beijing. They argue that high compliance and vehicle ownership costs may explain the effectiveness of the program. However, around the time of the program implementation, other strict policies, for example, factory closures and suspension of construction projects, were imposed to cut pollution during the 2008 Olympics, and thus the improvements in air quality may not be entirely attributed to driving restrictions.
Moreover, these programs are quite controversial from a cost perspective. Using a contingent valuation survey for HNC, Blackman et al. (2015) found that the regulatory costs are substantial (1 percent of drivers' annual income).
The present study fills a gap in the literature by examining the effects of switching from moderate to drastic restrictions. Although the effect of multiple changes 2. Other common arguments are that such policies are easy to monitor, target the rich and the poor equally, and potentially induce use of public transport (Eskeland and Feyzioglu 1997a). in driving restrictions on pollution has been studied by Viard and Fu (2015), their analysis explores a complex mixture of changes in a very short period, which makes it difficult to clearly separate stringency levels. 3 The most closely related studies evaluating driving restrictions are Gallego, Montero, and Salas (2013) and Davis (2008). Unlike their studies, which offer a broad analysis of a drastic driving restriction, the present paper focuses on the effects of two stringency levels. Due to a lack of a measurement of car driving, as in Gallego, Montero, and Salas (2013), the present study uses carbon monoxide (CO) levels as a proxy for car use and measure of air quality, exploiting the strong correlation with traffic. Using hourly CO in a regression discontinuity model, the effects of moderate and drastic restrictions are estimated at different times of the day and week for a two-year symmetrical time window centered on the start of each PYP phase. Within this interval, prepolicy observations are used as a counterfactual of postimplementation observations, as in Davis (2008), allowing for a polynomial time trend to control for unobserved time-varying factors. 4 To shed light on the households' potential behavioral response to the policy, the effects of PYP on gasoline consumption and vehicle sales and registrations are examined and compared between phases. A set of robustness checks are also conducted to assess the sensitivity 3. The ban in Beijing was introduced on July 20, 2008, and lifted on September 20. It was adjusted and reintroduced on October 11 and again readjusted on April 11, 2009. During the development of the present research, a study by Lin, Zhang, and Umanskaya (2014) assessing driving restrictions in several cities, including Bogotá, became available. Their analysis focuses on the effectiveness of a mixture of driving restrictions (private and public transport) on several pollutants, but does not evaluate the effects of moderate and drastic bans on car sales and gasoline use. The authors argue that restrictions for other transport modes may have increased the cost of using them and did not result in a clearly improved air quality. 4. The difference-in-difference method would require the assumption of a similar CO trend in a comparable city that was not subject to the program before the policy was decreed. Because other Colombian cities differ in vehicle fleet composition, development, geography, and meteorological conditions and also have been affected by other driving restriction programs, that assumption is unlikely to be met by any other city. For instance, Colombia's second largest city, Medellín, had extended restrictions to motorbikes prior to the drastic phase in Bogotá. 5 of the estimates. Finally, Blackman et al.'s (2015) estimates of households' willingness to pay for being exempt from HNC are used to compute the costs imposed by PYP.
The results show that CO concentrations did not decrease in either PYP phase.
Relative to the moderate phase, there is even some indication that CO slightly increased in the morning peak during the drastic restriction. This outcome is consistent with mild evidence of increased gasoline consumption and vehicle ownership, which suggests that drastic restrictions tended to generate somewhat stronger counterproductive consequences than moderate restrictions. Considering that the welfare losses are substantial, price-based mechanisms such as congestion charges, which have been shown to be effective in other cities, might be considered as an alternative instrument to reduce driving and pollution. This paper is organized as follows. Section I describes PYP. The car use and pollution indicator is characterized in more detail in Section II. Section III describes the data. Section IV presents the econometric approach and Section V shows the results.
Section VI analyzes the effects of PYP on additional outcome variables. Section VII provides some cost calculations. Section VIII concludes the paper. Thus, instead of traffic counts, CO concentration is used as a proxy for car use.
CO has several advantages compared with other possible proxies (Gallego, Montero, and Salas 2013). First, CO is mainly emitted by traffic (85-98 percent of total CO 9. The decline in fines may have other potential explanations as well, e.g., weak monitoring or drivers gradually learning how to circumvent the policy. Yet several years into PYP, weak monitoring appears unlikely since the police had reasonably improved their control procedures. Paying bribes to police officers is also unlikely since a driver may have to pay several bribes in a single trip, making it very costly. 10. See Gallego, Montero, and Salas (2013) for a brief discussion on the use of traffic counts to evaluate car use. Certainly, vehicle speed is also an alternative measure to analyze congestion. However, there is no comprehensive data in Bogotá to conduct a reliable PYP assessment using car speeds. Constructing a vehicle speed database would be relevant for policy-making given that congestion is such that Bogotá is considered the second worst metropolitan area to drive in according to Waze (2015). emissions in Bogotá in 2001 and 2007) and mainly by gasoline-powered vehicles (Derwent et al. 1995 To illustrate, figure 1 depicts the diurnal pattern between CO concentration and traffic flow in the first quarter of 2010. The graph shows a close correlation between these variables, with the highest CO levels at peak traffic hours. When interpreting this relationship, it is important to consider that CO also depends on the vehicle pollution intensity, which is determined by the car fleet composition and emissions control technology (three-way catalysts (TWCs)). Therefore, given that used and old cars have higher emission rates than new ones, CO concentrations do not necessarily represent the total number of vehicles on the road. But certainly the observed rapid and sharp response of CO levels to traffic emissions enables monitoring of changes in car use.
Considering that meteorological variables such as wind speed, temperature, relative humidity, temperature inversion, 12 and rainfall can also alter CO concentrations (see 11. Although CO concentrations tend to be, on average, below the air quality standard, compliance with the standard does not imply complete protection for all people or that other traffic pollutants are not emitted. The vehicular activity, identified through CO levels, may be associated with volatile organic compounds (VOCs) and carbon dioxide (CO2) (see EPA 2010).
12. A temperature inversion occurs when a warm air layer moves over a cooler air mass near the earth's surface-the opposite of normal conditions. Hence, motor vehicle emissions are trapped near ground level. Omitting this effect potentially introduces a bias when using CO as a Boddy et al. 2005;Maffeis 1999), these factors and their nonlinearities need to be accounted for in the analysis.

III. DATA
This study uses historical CO and meteorological data to assess the effect of PYP on CO during two-year symmetrical time intervals centered on the start of each phase (August 18, 1997, through August 17, 1999, and February 7, 2008, through February 5, 2010; see 14. To allow for meaningful interpretations, this reading was converted from azimuth bearings to a set of dummy variables corresponding to the eight-point compass international convention.
15. Temperature at different heights was used to define temperature inversion. Thus, temperature inversion is an indicator variable that takes the value of one under these episodes, and zero otherwise. of all possible hourly observations for the period of interest were selected, 16 yielding a total of four monitoring stations for each PYP phase. 17 The hourly CO reporting of the selected stations ranges from 78 percent to 91 percent for the moderate phase and from 80 percent to 95 percent for the drastic phase. The hourly meteorological reporting varies from 86 percent to 97 percent and from 92 percent to 100 percent, respectively.

IV. ECONOMETRIC APPROACH
The effect of PYP on CO levels is analyzed using the following model: where yt is hourly CO concentration in logs at period t and PYP is an indicator variable equal to one after August 1998 during the moderate phase of PYP and zero otherwise, or equal to one after February 2009 for the drastic phase and zero otherwise. Note that vehicles become older over time and that the efficiency of their emission control technology usually deteriorates with age. Thus, since PYP may have affected the vehicle fleet composition, i.e., may have changed the ratio of old/new cars, CO emissions may also have been affected through this channel. However, these changes typically occur gradually and should not be a concern for identification.
Another possible threat to the validity of the estimates is that the households may have reacted to the announcement of the upcoming driving restriction program by buying additional vehicles and, as a result, increasing their driving and emissions before PYP implementation. I believe this effect is minor since, although households were informed about the policy in advance, they had little time to adjust. Each phase was announced in media only a few weeks ex ante. The moderate restriction was officially issued by decree almost 30 days in advance (July 15, 1998), but nothing was said about whether the program would be permanent. Then, four days before implementation, a decree added clarifications regarding exempt vehicles. Thus, given the uncertainty about the program's permanency, most households probably waited to buy a second car. As for the drastic restriction, the program was formally announced by decree one day in advance (February 5, 2009), that is, with even shorter notice than for the moderate phase. 18 Equation (1) is estimated for the two-year symmetrical time windows (1997-1999 and 2008-2010). 19 First, the impact of PYP on CO is analyzed for the series containing hours of the day (hereafter, "all hours") when traffic is most active (5:00 a.m.-9:00 p.m.). Although the moderate restrictions applied only to peak hours, this overall model provides insights into whether the program affected the average CO levels during the day. Second, to evaluate the effects of the program in restricted and nonrestricted periods, equation (1)  18. It is also reasonable to believe that people prefer to make decisions based on official messages, since it is not unusual that policy projects are abandoned due to public or political opposition.
19. CO and meteorological variables were found to be stationary according to the augmented Dickey Fuller (ADF) test. Hence, the models were estimated with the variables in levels.
20. Restricting the sample to time intervals was initially suggested by Davis (2008). For the moderate phase, the sample is restricted to 7:00 a.m. 21. Based on the autocorrelation function, in most cases residuals are serially correlated between three days and one week. In very few cases, correlation exceeds one week, e.g., the "all hours" model, although the magnitude and statistical significance of the autocorrelation coefficients decrease remarkably after that period. Therefore, to account for serial correlation, standard errors, in parentheses, are robust to heteroscedasticity and arbitrary correlation within one-week clusters.
This section presents and discusses the estimation results of equation (2). Additional robustness checks were also performed to analyze the stability of the results.

Main Results
Table 2 presents PYP estimates for different time-interval subsamples in the moderate and drastic phases using a fifth-order polynomial time trend. 22 The results indicate that PYP did not lead to reduced CO concentrations in either phase of the program for any of the subsamples. Three out of five PYP coefficients for the moderate phase are negative but statistically insignificant. For the drastic phase, all coefficients are positive, though the PYP estimate is statistically different from zero at the 10 percent significance level only for the morning peak model. In this case, PYP appears to have increased CO by 13 percent, but the estimate is less precise. Figure 2 plots, for each phase and all subsamples, the average weekly residuals from equation (1), excluding PYP, along a fifth-order polynomial time trend and PYP. The RD graphs show that the fifth-order polynomial tends to satisfactorily represent the evolution of the time trend.
Moreover, the observed discontinuities at the policy date are consistent with the obtained PYP estimates.
Intertemporal substitution is also analyzed as in Davis (2008). When comparing the PYP effect between time-interval subsamples of the moderate phase, the effect for off-peak and weekends relative to the evening peak is positive and statistically significant at the 5 percent level. Similarly, relative to the morning peak, the effect for weekends is positive, but the estimate is less precise. In the case of the drastic phase, all the relative effects between time-interval subsamples were statistically insignificant, 22. Meteorological variable coefficients for the "all hours" model are shown in table S.1 of the supplemental appendix. Most of these coefficients have the expected signs and are statistically significant. As regards the effect of temperature inversion, in both PYP phases, CO levels were 8 percent higher under these episodes than under normal conditions. 14 except the effect for weekends with respect to off-peak, which was also positive. These findings are in line with intertemporal substitution toward the unrestricted driving periods of each PYP phase. As Davis (2008) suggests, it is likely that this inclination to substitute may have occurred also across weekdays, which explains why there is not an absolute decrease in pollutants. Additionally, hypothesis testing was conducted to compare the size of the PYP effects between phases. I was only able to reject the null hypothesis that the drastic-phase impact was lower than or equal to the moderate-phase effect at the 5 percent significance level for the morning peak (see the bottom of table   2). This provides some indication that air quality tended to slightly deteriorate in the period of high travel demand during the drastic restriction relative to the moderate phase. Drivers may also potentially have increased car use. This conjecture is explored in detail in the Transport Costs section. Analyzing the households' behavioral adaptation is relevant as it may explain the lack of evidence of reduced CO in both phases.

Robustness Checks
A potential concern when estimating equation (1)  where Ct is the total gasoline consumption per capita in logs at time t, PYP1 is an indicator variable equal to one from August 1998 to January 2009 and zero otherwise, and PYP2 is an indicator variable equal to one after February 2009 and zero otherwise.
Pt is the gasoline price and Mt is the GDP per capita, both in logs at time t.
Furthermore, is a polynomial time trend, is a group of month-of-year dummies, and is the error term. and measure the effect of PYP on consumption. This specification is closely related to the work by Eskeland and Feyzioglu (1997a) for HNC in Mexico City, although the estimation here includes a polynomial time trend to 25. Monthly prices from 1999 to 2010 were obtained from http://www.ecopetrol.com.co/precios.htm. Information from the digital news archive El Tiempo (www.eltiempo.com) was used to fill some gaps in the series. The prices taken from the news corresponded to official prices and matched perfectly the prices for which information from Ecopetrol was also available. control for unobservables over time. Classical ADF tests indicate that consumption, prices, and GDP are integrated of order one, and the Bayesian information criterion (BIC) suggests that it is sufficient to consider a linear polynomial to adequately describe the underlying trend. The Engle and Granger test applied to the residuals in equation (2) supports that the series are cointegrated; hence, expression (2) provides the long-run elasticities. An error correction model (ECM) is also estimated to explore the short-run relationship. Panel (a) in table 5 shows the results of three alternative specifications of the consumption equation. Column (1) presents the estimates of equation (2) without controlling for seasonality, column (2) adds month-of-year dummies, and column (3) provides the estimates of equation (2)  Across specifications there is no evidence of a decrease in gasoline consumption as a result of PYP during the moderate phase. In contrast, the estimates of the effect of the drastic phase were shown to be positive and statistically different from zero at the 5 percent and 10 percent significance levels in the specifications without seasonal controls and DOLS, respectively. The long-run PYP effect on gasoline consumption during the drastic phase was around 10 percent, though its standard error in one of the regressions tends to be large. Across the three specifications, the estimates are precise enough that, relative to the moderate phase, the PYP effect on gasoline consumption in the drastic phase is positive and statistically significant at the 5 percent level, consistent with an 26. It consists of adding lags and leads of the first differences of price and GDP to the model. The DOLS estimator also deals with the concern of potential simultaneity bias among regressors. increase in driving for drastic restrictions and the slight CO increase in the morning peak. This finding is also in line with Eskeland and Feyzioglu (1997a), who found that HNC increased car use.
The long-run price elasticities oscillate between -0.12 and -0.18, though they are percent of the adjustments happen in the first month. As regards the elasticities, the price elasticity estimates show a higher response of the gasoline demand to pricing in the short run than in the long run. Eskeland and Feyzioglu (1997b), who studied the gasoline demand in Mexico, found results in the same direction.
Considering that gasoline consumption is responsive to prices in the short run and that the monthly gasoline demand in Bogotá decreased from 42 to 24 million gallons from 1996 to 2008, it seems reasonable that gasoline prices were responsible for some fraction of that decline right after a price increase. Thus, in some way, prices rather than PYP have tended to discourage driving. However, the fact that the elasticities are small limits the use of taxes as an effective tool to substantially reduce consumption. Taxes are also considered a blunt instrument as they disregard where and at what time of the day the driving occurs. In contrast, congestion tolls, which have been shown to be effective in dealing with congestion and pollution (see Börjesson et al. 2012;Coria et al. 2015) and more efficient than gasoline taxes (Parry 2002), can be spatially and temporally differentiated, charging driving on the most congested roads and during peak hours. Clearly congestion charges would be an alternative mechanism to achieve the goals that did not occur with PYP.

The Effect of PYP on Vehicle Registrations and Vehicle Sales
The lack of evidence of a decrease in CO levels in both PYP phases might be explained by an expansion in the vehicle stock or in the number of trips made. One way to analyze this type of behavioral response is to evaluate possible increases in the number of where Vt is vehicle registrations in logs at period t, is a set of lags of the dependent variable, is the error term, and other variables are as described above 27 (1) and (2) Vehicle ownership does not seem to have been affected by the moderate ban.
One potential explanation for this is that households may not have been inclined to own additional vehicles because they could still drive without restrictions during off-peak hours. During the moderate phase, households did not have prior experience with the 21 program and hence a precautionary attitude may have been to wait until the mayor announced PYP as a permanent policy and explore other adaptation mechanisms before increasing their car stock. For example, in this phase there was some evidence of intertemporal substitution between restricted and unrestricted hours, indicating that some households reallocated trips. Regarding enforcement, although it has been considered satisfactory, there are indications of weak monitoring in the moderate phase.
According to Acevedo (1998), there were too few police officers involved in the PYP enforcement (1,050 in total, of whom only 150 were motor officers), which may have affected compliance. 28 Also, drivers may have found routes where the probability of being caught in violation was very low. A newspaper reported that drivers were choosing secondary roads to avoid police controls and that some police officers had to focus on tasks unrelated to PYP enforcement, reducing the coverage in several areas (Nullvalue 1998).
Additionally, the following model is used to evaluate the effect of PYP on new sales: where St is monthly new regular car sales in logs at time t, is a set of lags of the dependent variable, Rt is GDP growth rate in logs at time t, is the error term, and other variables are as above. is the effect of PYP on new regular car sales in the drastic phase. The data spans from July 2000 to September 2011, and, due to the lack of pre-2000 sales data, the moderate phase cannot be assessed. The GDP growth rate is 28. Enforcement was mainly in the hands of the motor officers (caza -infractores), who patrolled the major city corridors. The other 900 police officers standing in the roads could only marginally support the motor officers' work as their main job was to focus on bottlenecks in traffic. When a driver was caught in violation by any of these officers, the driver was stopped and directed to a secondary road to receive the fine. According to the Metropolitan Police's organizational structure, the police functions are separated; hence police officers not tasked with improving transit were not involved in PYP enforcement.
included in the estimations as vehicle sales may be affected by the economy's performance. The variables exhibit nonstationarity and seasonality and appear not to be cointegrated under the Engel and Granger test. Thus, the variables are in first differences. BIC indicates that it is sufficient to include two autoregressive terms and a quadratic time trend. The added lags of the dependent variable also account for serial correlation.
Panel (b) of table 6 shows the estimates of equation (4) including GDP growth rate, adding a vehicle price index, and conducting two-stage least squares (2SLS) (see columns (1), (2), and (3), respectively). Although vehicle prices were not available, a price index was created using the ratio of the total monetary value of sales in the country to the total number of new regular car sales. 29 A possible concern when constructing the price index is that it might be endogenous as sales in Bogotá represent almost 50 percent of the total sales in Colombia. To address this issue, 2SLS is estimated by instrumenting the price index with the real exchange rate. 30 Across the three specifications, the short-and long-run effects of PYP2 were positive and statistically significant at the 10 percent level. The long-run estimates range from 5.5 percent to 9.3 percent, though two out of three of these estimates are less precise. This mild evidence of a growth rate increase in regular car sales relative to the moderate restriction is compatible with the results for vehicle registrations. As a robustness check, equation (4)   It is worth pointing out that these welfare losses are lower bound costs since other effects, for example, enforcement costs and impacts on commerce and labor, are not considered in the present analysis. These issues are outside the scope of this study but may increase the overall costs of the program.

VIII. SUMMARY AND CONCLUSIONS
Driving restrictions have been used in several cities around the world to deal with traffic congestion and air pollution. This study contrasts previous studies evaluating programs implemented in a drastic fashion by assessing the effects of shifting the regulation from moderate to drastic restrictions on car use and air quality. Neither moderate nor drastic restrictions in Bogotá were shown to be effective in improving air quality. Rather, it appears that the most stringent phase of the program may have induced more driving.
Relative to moderate restrictions, the drastic phase tended to slightly increase gasoline consumption, vehicle stock, and CO concentrations in the morning peak. Households seem to be responsive to drastic restrictions, finding alternative ways to avoid the ban.
These programs, besides being ineffective, were inefficient as they affected many households by increasing their commuting costs. This study questions the rationale for extending the program to other cities or making it more stringent.
Other instruments with the same aims as the PYP program need to be explored.
Road tolls have effectively been used in other cities to ration the scarce road infrastructure and deal with pollution externalities. The charged fees depend on the time of day and are higher during congestion-prone times. Remarkably, evidence for Stockholm shows that the fee elasticity of car use is greater in the long run, implying that the effect of the fee does not vanish over time (Börjesson et al. 2012). In addition, the collected fee revenues may be used to finance infrastructure. Moreover, given that driving is associated with other pollutants, the decrease in vehicle traffic has other positive effects, including a reduced climate impact. Bogotá might benefit from the lessons learned from these instruments. A simulation study would be useful to quantify the impact of congestion charges in Bogotá on traffic and air quality.

FIGURE 1. CO Concentrations and Traffic Flow
Source: CO data from RMCAB and traffic flow from the Transport Agency of Bogotá for the first quarter of 2010. Author's calculations.    week clusters. Estimates marked * p < 0.10, ** p < 0.05, *** p < 0.01.

Source:
Author's analysis based on data described in the text.  (c)) and include meteorological variables and indicator variables for month of the year, day of the week, and hour of the day.
Interactions between weekends and hour of the day are added only in the "all hours" model. Standard errors, in parentheses, are robust to heteroscedasticity and arbitrary correlation within one-week clusters.
Source: Author's analysis based on data described in the text. Interactions between weekends and hour of the day are added only in the "all hours" model. Standard errors, in parentheses, are robust to heteroscedasticity and arbitrary correlation within one-week clusters. Estimates marked * p < 0.10.
Source: Author's analysis based on data described in the text. and three regressions of the associated error correction models (panel (b)) for the period between January 1996 and Residualt-1 is the first lag of the residual estimated from the long-run equation. To account for serial correlation, the ECM in (1)-(3) includes the first lag of the dependent variable, whereas the ECM in (1) also adds the second and 11th lags. Standard errors reported in parentheses. Estimates marked * p < 0.10, ** p < 0.05, *** p < 0.01.
Source: Author's analysis based on data described in the text. To account for serial correlation, all specifications include the first, second, and 12th lag of the dependent variable.
Panel (b) shows PYP estimates from six regressions for the period July 2000-September 2011. The dependent variable is the first difference of monthly vehicle sales (regular cars) in logs. Columns (1)-(3) are specifications using data for Bogotá, while columns (4)-(6) employ data from other cities without restrictions. All regressions are fitted along a quadratic time trend. To account for serial correlation, all specifications include the first and second lags of the dependent variable. Standard errors in parentheses. Estimates marked * p < 0.10, ** p < 0.05, *** p < 0.01.
Source: Author's analysis based on data described in the text.
Supplemental Appendix of The More Stringent, the Better? Rationing Car Use in Bogotá with Moderate and Drastic Restrictions Appendix S1   (2) drastic restriction for the "all hours" model (5:00am-9:00pm). The dependent variable is carbon monoxide (CO) in logs. PYP is an indicator variable equal to one after August 18, 1998 and zero otherwise in column (1), and equal to one after February 6, 2009 and zero otherwise in column (2). Wind direction is a set of indicator variables corresponding to the 8-point compass. Regressions also include a polynomial time trend of degree five and indicator variables for month of the year, day of the week, hour of the day, and interactions between weekends and hour of the day. Standard errors, in parentheses, are robust to heteroscedasticity and arbitrary correlation within 1-week clusters. Estimates marked * p < 0.10, ** p < 0.05, *** p < 0.01. Source: Author's analysis based on data described in the text. Interactions between weekends and hour of the day are added only in the "all hours" model. To account for serial correlation, reported standard errors, in parentheses, are robust to heteroscedasticity and arbitrary correlation within 1-week clusters. Estimates marked * p < 0.10, ** p < 0.05, *** p < 0.01. Source: Author's analysis based on data described in the text.