Welcome to Access to Trader, the number one community for those who are committed to taking control of their trading in order to achieve success, profitability, and longevity. Thank you for joining us. Here's Dan Shapiro to help you find your edge, master your process, and own your future. Hey guys, good morning everybody, and welcome to another edition of the AccessToTrader.com weekend update show. Hope everybody is doing well. Hope everybody had a really good trading week. We'll get to the events. Incredible market does continue just for a second. If you are brand new to the channel guys, the only thing we ask is take a second if you could be so kind, like the video, share the video, subscribe if you haven't done so already. It'll help the channel grow. It'll help us continue to push forward on a day to day basis with unbiased technical analysis. So let's talk about the week. A lot of the things happened this week. Market continues to push higher highs and higher highs and higher highs for the week. Look at the numbers for the week guys. You got the QQQs up 1.4% for the week, 6.6% year to date. This is after being up 54% in 2023. The S&P 500 up 1.6% for the week, up almost 7% year to date after being up 20% in 2023. But the one that is really pushing through and a lot of people are using this as a barometer now is the semiconductors. The SMH is staggering numbers. Semiconductors for the week up 3.4%, 19% year to date. That is a very, very big number. And obviously, again, look at the strongest stocks in the market. They're all semis. The earnings had phenomenal run. If you go back to October 31, which really wasn't that much far off. We were at 400 and the stock has basically doubled since October 31, which is absolutely phenomenal. SMCI continues to trade like a low-flow stock having 100-200 point ranges now every single day is holding up. But there's a two-way channel developing. We'll get to that in a second. I think it could be a pretty good value for all you guys who trade the semiconductor names. You have names like Meta continuing to pull through. And although the one really weird thing about Meta, and I've traded it now four or five times since the earnings, and no complaints, no complaints. I will get to the pivots on Friday in a second. It's giving you those $2, $3 moves, $4 moves, but they're not giving that two, three, four-day continuation, which is very, very odd, especially after a company has come out with such great earnings, come out with this distribution of their first quarterly dividend ever. So it's kind of weird that it hasn't had that multi-day push that a lot of companies that have a positive earnings report tend to do. But again, something very odd there. Name like Microsoft, right? Microsoft has been pretty steady forming a base here above the 10-day moving average, which is obviously now the line in the sand. Again, the coolest thing about trading technicals, instead of trading your opinion, you have a line in the sand on the downside and you have a level of interest where stocks need to reclaim to the upside. And that's the cool part about it is, again, we're not guessing. Every trade that I put on, I'm trying to win my interval. Basically, the way I trade, I only have six candles throughout the day, six one-hourly candles. There's a top of the range, the bottom of the range, there's a middle of the range, what we call the sneaky pivot. Again, if you watch the PS60 workshops, you kind of an inclination of what that means, and that is it. So I'm not trying to see, you know, guess where the stock is going to be three hours from now on the close next Thursday or next Valentine's Day. It's winning our interval. And because there's such a definitive line in the sand and a lot of these stocks, both on the upside and the downside, it makes things a lot easier. Your FOMO is going away because you're just waiting for that channel to confirm. Again, not the easiest thing to do, especially when you're still in your honeymoon stage as a brand new trader. But as you get older, as you get more screen time, it's much easier to kind of sit back, relax. And this is kind of where we always talk about, especially I always try to reiterate on social media. After my initial game plan is done, whether it's a long side, short side, I'm not actively looking for trades. The trades will actively find you, whether you find some option flow, repeat buyers coming in with short term expiration, one side or the other, or an alert that you set for yourself three weeks ago. So for example, on eSignal here, and I use eSignal Classic, not eSignal, no legacy, excuse me. I use eSignal Legacy and the most amazing part is just to give you an idea how long I'm trading. If you go to about, this is not too many people here use eSignal Legacy, but I created this profile in May 2001. Just to give you an idea how long I've been trading, but most important is every single year that goes by, you're trading less and you're getting better value for more. Again, it's not how many trades you put on, it's how many trades you put on properly. So that part of your game is going to kind of get better as you get into the game year by year, goes by, it gets much easier to kind of wait for value to take place. But overall, again, incredible market continues. This week we saw FOMC minutes pretty much reiterating what we saw in the last conference, nothing really earth shattering there. The market continues to push higher. The question is for how long? And now we want to make sure that our rose-colored glasses are off, right? As much as there's some really great setups continue to the long side for next week. I'm going to give you guys a couple of short setups that look pretty good as well. And years ago, I traded really a couple of really big euphoria markets. The first one was obviously Dotcom. I wasn't ready for the rug pull. Didn't believe that the market was ever going to end. It was a disaster. That 2004 to 2007 run was pretty euphoric. Not it is how it is today. Wasn't really ready for the mortgage crisis. Really wasn't ready for potential Armageddon. Had a really, really negative opinion there. Even when you go back to the start of the recent COVID rallies, a lot of people weren't prepared for 2022. I was because, again, the time will make you better. Time will make you smarter. At least in my case, less dumber.  You're not going to have a linear move in your career stocks. You're not going to have a linear run. There will be aggressive pulls. And what we saw, if you guys remember what we saw a week ago, right? We talked about a potential rug pull on this inverted hammer. The next day, the NASDAQ went down 2%. So you have to see the signs. You have to see the signs. You have to appreciate the trading both sides of the market. So let's talk about some ideas, right? Let's talk about some ideas for next week. Obviously, technically, there's nothing really much to talk about if you look at the QQQs. Again, you know, we're kind of, you know, you're not going to really start getting defensive until the QQs start losing the 10-day moving average. A close below 433, you should start taking a little bit of a risk, at least for that one day.  I'm holding these things for a while, and that's fine. This broadcast is not really for position traders that are holding a position for seven months. This is a day-to-day issue. That's, again, I could be bullish once at stock one day. I could be bearish the stock the next day. It's basically based on the formation of the channel that it's about to confirm. So going into this week, I don't use the word line in the sand. But again, you want to be a little bit less aggressive overnight if you see the QQs lose the 10-day moving average. So far, they held them and they're building upon them in the last couple of days because of NVIDIA's earnings and some of the really, really big moves in the semiconductor. But watch, you know, watch the potential line close above below 433. If we could close below 433 in the NASDAQ 100, you might get some value to the downside. Obviously, to the upside, it just keeps on grinding and grinding. You look at the SPYs, kind of the same thing.  You want to watch the previous days low of 507. That was yesterday's low. Watch that 507 level because if the bearers start taking control and start putting in the ceiling below 507, we could get a back test to 503, which correlates back to the 5-day moving average. So keep an eye on that as well. And the semiconductors, right? You can see here pretty visual. Again, before you understand all the nuances and moving parts of technical analysis, sometimes your eyeballs are the best, you know, are the best version of identifying trends. You can see here this linear regression line, which a lot of people don't use. I have been using it for years. You can see here that the SMH has stopped twice at 206. Everybody see that, guys? That was Thursday's low. That was Friday's low. If you were looking for a poll, for example, on a video or SMCI, I'll get to SMCI in a second, you want to start watching that 206 level because if that 206 level starts to lose on the semiconductors, even if it's for a gravity type of trade, right? Gravity top of trade. Again, I don't want to use the word blow off top, but a gravity sort of trade, the semiconductor is going to need to lose 206. If they start to lose 206, then you could get a move all the way down to the 10-day moving average of roughly 202 and change. So that's definitely a really good line in the sand because it held back-to-back days of support on that linear regression line. So let me give you guys some ideas for Monday. We'll start with the upside channels, right? Amazon looks good. Looks good held up for the last couple of days. Jeff Bezos has now sold a ton of stock. It looks like the market has embraced that selling in the last few weeks. We'll see. The key is Amazon needs to get above Friday's channel and start to build. We saw some pretty decent short-term expiration bets for the 180, 185s going into April and May. So this is definitely one that stood out. Does it mean it's going to rally on Monday? It doesn't mean anything. It still needs to confirm Friday's channel, but at least you can tell compared to its peers in the mega-cap technology group, it's held up very, very well, now it's one day away from reclaiming Friday's channel. And if the market does rally on Monday, it can give a good push. Sticking with the semiconductor names, Qualcomm, not there just yet, right? Not there just yet. It's not imminent there. It's not imminent yet, but it's a nice-looking channel. It really is. Keep an eye for the January highs this week. It's not imminent, guys. It's not imminent. It's still three, four hours away. But keep an eye, set an alert on Qualcomm. If it could start building above the January highs, going into this week in the hot space, it could be very good. The same thing with arm holdings. Arm had a phenomenal, phenomenal three-day run into earnings, after earnings, excuse me, going from in the 70s all the way up to 164. It's just consolidating here. It's consolidating here basically about a week and a half now below this channel if the semiconductors continue to push. Keep an eye on this thing above this channel as well. Looks very good, right? Looks very, very good as well. So keep an eye on that. Let me see what else I want to give you guys from the long side. Take a look. It's not a sexy stock. It's not. It's not every single thing needs to be sexy. But look at New Corp, right? In the steel group. That's a beautiful-looking chart. It's not going to be a fast-moving stock. It's not going to up $10 on one day, $20 on the next day. If you do trade a non-megacap technology name, this is a good-looking chart. Keep an eye on this for this week. Again, it's not something that I'm going to look at. But I found this chart. It's a beautiful consolidation. It's a month-month-and-change consolidation. It's a beautiful trade. It's a potentially really good trade. So keep an eye on it for this week. Unfortunately, not all roses are blooming, right? Let me give you guys two there or not, right? So here is Apple. Apple has not participated whatsoever in this whole dramatic market. It's not that far away from the bottom of the range. And it's also not even close to the top of the range. The longer Apple cannot rally. And if we do get some sort of pull in the next couple of weeks or the next couple of days, whatever the case may be, watch Apple below this bottom of the range, guys. I'm telling you, this thing is not participated at all. Honestly, I think I'm going to start looking at this thing this week if it does rally into any type of supply, start rejecting it. It just can't rally. It shows you here. Every single time it rallies into supply, what happens? It gets rejected. Into supply gets rejected. Into supply gets rejected. So any strength this week, at least in the beginning, I want to see if it gets rejected off supply again, maybe start shorting it against supply on the way down. But the one that is staring out like a sore thumb is Tesla. We got long Tesla above this break about a week ago, had a great run. I'm not complaining on the run. Went from the $196.50 area to $205 and change pre-market the next day, which was great. The problem is it failed this range. It started putting in lower highs and keep this in mind. This is really the one that has never rallied. That didn't come close to rallying with anything else. And as soon as it lost the 50-day moving average, if you guys have been watching this broadcast for a long time, you kind of know what happens. You lose the 50-day. It's a wrap. It's a short-term, long-term, medium-term wrap. And that's exactly what's been happening, then blown up on earnings. I'm watching the bottom of the range here this week. Guys, I'm telling you, if Tesla starts losing the bottom of the range, and despite the aggressive call buying that we saw this week, again, my initial interpretation of this is, hey, this thing is putting in now three days of lower highs, it lost back its range, date two below the five-day moving average. The key is, if it loses the bottom of the range here, that started in the beginning of February, we're going to have a big trade potential on the way down. This is definitely the one we want to watch this week. And SMCI, again, I continue to reiterate the point, SMCI is not for me. And the reason why I say this, I trade stocks that have a lot of liquidity. AMD, Tesla, NVIDIA, right? Amazon, Apple, Meta, maybe Meta's not a great example, there's still a lot of liquidity, right? SMCI for me trades $2 spreads, hundreds share lots.  And by the way, if I'm wrong, I'm down 10 points. Not really my cup of joe. Again, I'm turning 15 June, I'm not turning 15. However, for all you junkies out there, and again, and I don't mind missing a setup on SMCI because I can make up for it in another area in the tech space. Maybe it won't be as sexy as a hundred point move, but I don't need a hundred point move to get a lot of meat on the ball. SMCI had a shelf registration this week, it's had an incredible run, and it's setting up very, very tight. You see how the, forget about the top of the channel here. You see how it held the 10-day moving average, that's the green line here. Do you guys remember the last time it lost the 10-day moving average? It lost the 10-day moving average, the stock went from 690 all the way, excuse me, it lost, it went from 800 all the way down to 692 while it's setting up exactly the same way, right? Again, this is a very big copycat business stocks to have a very short memory. Watch SMCI this week, especially if it correlates with, what was the number I said on the SMHs. If it correlates on the 206 pole on the SMHs, guys, watch SMCI. If it loses the 10-day moving average, you've got like 150 points on the next support. So again, for all you guys who are trading, especially on the option side, keep an eye on this thing, but it has to lose the 10-day moving average for this trade to potentially work. So really, really big potential there. So we're kind of set up, you know, we're set up for both the upside, the downside this week. The key is now, stay patient, don't guess, don't anticipate, just let it play out and let it work. So let's talk about the pivots from Friday. First of all, if you guys remember Wednesday's broadcast, I turned around and said, we have to wait for pullbacks. We have to wait for pullbacks. Wait for pullbacks. Everything's up like 300 points. We had a crazy good, good move on the video on Friday. Number one, this was, you know, this was a sneaky pivot. I'll talk about the sneaky pivot in a second. So this is the Note Friday. This is the Note Friday pre-market. Congratulations to everyone that was holding. I held the runner overnight. Excuse me. I sold it right before the 800 push the previous night around the 795s area. Not sure we could have played it in the video better from the previous day. First, we caught a massive washout into the 740s that exploded back into the 780s. And then there was an unbelievable pivot, a 778 sneaky pivot. Again, if you watch the PS60 workshops, you kind of know what that is. If you want to keep a runner, God bless. Again, here is the sneaky pivot. Even if you didn't catch the washout, here is the sneaky pivot. 778 needs to build to reclaim the previous candle.  So just a phenomenal move. Absolutely phenomenal move. Friday, there was two areas of interest on meta. 490 needs to build. 490 went to 494 pretty quickly. I unfortunately missed. It just went too fast. The liquidity got dried up really quickly. I missed the 490 to 494. I caught the remount, the bounce from 491 into the 493s before the market got pulled. AMD came close to confirming. Never did. Tesla came close to confirming. Never did. PHAT, nice little move here. For all you guys who traded, $11 stock rejected three times needs to build. Here was PHAT. Went from like 11 to 1140s. Nothing crazy. But again, is that a good thing? I don't know. Not really my thing. And then you had Dock U waiting for a flush that didn't come. Square had a nice pre-market move. What if I get dollar and change? Nothing crazy. Again, had good earnings. Rivian, day two got destroyed. Rivian, 11 if it builds below can flush. Here was Rivian. Again, Rivian double downgrade on Friday. Lost the 11. Went all the way down to 10. Really nice move there. Congratulations what you guys got there. Amazon, again, continues to act really, really well. Only went up about 30 cents, 40 cents or so. Didn't give us a second entry. But again, acts really, really well. This was big. Congratulations for you guys who caught CVNA. Not really my thing, but 7150 rejected three times needs to build. Here is CVNA. Here was a 60 minute view. Here was a 60 minute view. Rejected 7150, 7150. Took out the 7150, went to 76 and change before it got pulled. Great, great move there as well. There was one sneaky pivot. Here it is. Sneaky pivot on square 8023. Rejected a supply, needs to reclaim for an afternoon push. Nice push. Here's a sneaky pivot on square right over here. See this here here? 8023. 8023 was supply. It got above 8023 and put up a $2 move before the market got pulled. Really good trading week. Nvidia obviously was the star. But overall, great action. Great, great action for all you guys who are brand new to us. And so exactly what we've been doing for the last couple of weeks. Again, you see, we don't need to go to the market. We just need a range, whether it's to the upside and to the downside. And that's the name of the game. Guys, God bless everybody. Stay blessed. Stay healthy with God's help. I'll see you all on Monday. Take care.


First of all, welcome to everybody. So we will have presentations in each session, one after the other, and we will take questions altogether at the end. I will give the speakers now 22 minutes. I'll keep the time. And for questions, please write them in the chat. I will not look at the raised hands in Webex. I will look at the chat. Please put your affiliation, your name, if it's not already clear, your question so that I can maybe group them together. And if it's a clarification question, can you please write, clarify? So maybe the authors, the co-authors of the speaker could already solve some of these clarification questions in the chat. So we have more time for more substantial discussions at the end. Now, you are all muted. At some point, I may ask some of you to read your question aloud or to just ask your question. In that case, please accept the request to unmute, and then you will be able to speak. Once you mute yourself again, you will have to be given the possibility again. So you're locked, so to speak for this session. Now, let me get to the content and put in a plug for Prisma. Isabel already mentioned that we have a research network within the European System Central Bank called Prisma, which stands for a Price Set in Microdata Analysis Network. The following two papers are indeed by members, participants in this research network. And so I thought to remind you of that. You have information on the web page of the ECB. Subscribe to that page, put it in your bookmarks, because as the papers come out, we will list them there so you can stay on track with the production, research production of this network. Now, Andrea, you can start to share your slides. You are presenter now. You should be able to share your slide. This is a paper joined with Alvarez, Gotier, Le Bihane, and Lippi. Andrea, yes, we see your slides. Very good. Yes, very good. Hello, everyone. Thank you very much for having us. I'm going to present the paper Empirica Investigation of the Sufficient Statistic for Monetary Shock. This paper is co-authored with Alvarez, Gotier, Le Bihane, and Lippi. In recent works, Alvarez, Lippi, and Cooutors have established a new theoretical result for the propagation of Monetary Shock. This result is called the Sufficient Statistic Proposition. This proposition tells us that in a multi-sector economy with frictional price setting, the cumulative response of output of an industry where Monetary Shock is equal to the ratio of the kurtosian frequency of price changes times delta, the Monetary Shock, divided by epsilon times six, where epsilon is the labor elasticity. When the cumulative response, to be precise, is the cumulative input response function, which is the integral of the input response function. From now on, I will call this object as the response. The novelty of this result stands in underlining the importance of the kurtosis in explaining the propagation of Monetary Shock. Kurtosis, intuitively, is important because it measures the lack of the so-called selection effect. On the other hand, the importance of frequency is pretty well known in the literature because frequency represents the time units of the model. This is a theoretical result that holds in a broad class of models. For example, it holds in random-menu cost models as the one of Calvo, Calvo Plus, or Golsofer Lucas. It also holds in rational-attention models as the one of Reis, but it's also all in multi-product models. This is the theoretical result that holds under some assumption. For example, in the model, the model must feature small inflation. Furthermore, the firms, when they adjust their prices, must set their price equal to the optimal one. We say in the literature, we say that the first must close their gap. Furthermore, the shock in the model are all albronian. For example, this result does not hold in a model with price plans or in a model with temporary price changes. The aim of our paper is to empirically test and explore the validity of this theoretical result. To do this, we use micro and sectoral data on consumer price and producer price data for the French economy. And we are going to exploit the across-sector variability. The main challenge of our work is to estimate the response of different sectors to a more high shock and to estimate the micromoments of the same sectors. For example, we are going to estimate the frequency and the kurtolis of price changes. Before telling the empirical strategy that we're going to adopt, I want to state the sufficient statistic proposition as the response of prices and not as the response of output. I want to do this because we have better and more data for prices. After some algebra that I'm not showing, we can state the sufficient statistic proposition in the following way. The response of prices in sector J up to time T must be equal to the monetary shock delta times T minus the ratio of the frequency and the kurtolis of price changes times delta, the monetary shock divided by six. Furthermore, we can disentangle the effect of kurtolis and frequency to see if both of them are statistically significant. To do this, we can take a forced order theory expansion around the mean of the frequency and the kurtolis. When we do this, we obtain that the response of prices must be equal to a constant minus delta, the monetary shock times the mean of the kurtosis divided by six times the mean of the frequency times the kurtosis plus the same coefficient times the frequency. From this to equation is quite clear how we are going to test the sufficient statistic proposition. We want to run a cross sector regression in view of this equation. This is exactly what we are going to do and we report the two regression in this table. The first regression that we're going to want to run is that to regress the response of prices over a constant and the ratio of the kurtosis and the frequency. Once we normalize the monetary shock delta to minus 1%, we have the prediction for our coefficients. According to the theory, alpha, the coefficient of the constant must be negative and equal to minus t. The coefficient on the ratio must be equal to one over six. However, if in the model we allowed for strategic complementalities, we have one more degree of freedom for the coefficient meter. As I told you before, we took a table expansion of the first order to inspect the significance of frequency and kurtosis. When we do this, we can run a regression of the response of prices over a constant, the frequency and the kurtosis. The theory in this case predicts that the coefficient on the frequency and the kurtosis must be the same in absolute volume. Please remember this to regression because I have our two baseline regression which we are going to estimate later. But however, before estimate them, let me tell you the empirical strategy that we adopt. Our empirical strategy will consist in three steps, using a French data on PPI and CPI. In the first step, we want to construct the left-hand side variable of the above-mentioned regression. To do this, we implement the factor lamented here using sectoral and aggregate time series. This way, we can obtain the response of different sectors of prices. In the second step, we are going to use microdata on CPI and PPI and we will construct the frequency and the kurtosis of price changes. Once we have constructed the frequency and kurtosis, we are done with the right-hand side objects of our regression. And this is possible, and then it's possible in the third step to relate them using an act of sector regression, regressing the response of prices over the frequency and the kurtosis, which is, this is exactly our third step. Let me start with the first step. In the first step, as I told you, we implement the factor lamented here following Bernanke-Pobben-Erias. In our case, the factor lamented here is just a VR in which the interest rate, in our case, the three-month variable is known. And there are some unknown factors that must be estimated. We estimate these unknown factors using a large number of time series in which we include the sectoral PPI and CPI. Using the factor lamented VR is possible to retrieve the inputs response function of this large number of time series that we use to estimate the factor. For example, in our case, we are able to estimate the sectoral inputs response function of price of PPI and CPI. Once we have the inputs response function is straight forward to estimate, to calculate the cumulative inputs response function, which is just the integral of the inputs response function. We will integrate the inputs response function up to two years or three years. As every VR, we need an identification assumption to retrieve the monetary shocks. To do this, our bench line identification is to use a Cholesky identification plus a long-run restriction. The long-run restriction in our case is that all the sectoral prices must have the same response in the long run. In our case, they must have the response of minus 1%. The other two alternative identification that we're going to use is a Cholesky without long-run restriction and use high-frequency data as instrumental variable. In this figure, we report the estimated inputs response function using our benchmark identification, means long-run restriction plus Cholesky. On the left panel, we have the inputs response function for PPI. On the right one, we have the response for CPI. The dashed red line is the inputs response function for some sectors. The thick red line is just an unweighted leverage of them. And the blue line is the inputs response function of an aggregate measure of the PPI on the left panel for CPI. On the X axis, we have the time after the shock. And on the Y axis, we have log points in relation from the step state. From this figure, for example, from the PPI, the left panel, you can see that all the inputs response function in the long-run goes back to the level minus 1%, minus 1%. And furthermore, you can see that there is a variability in which the different sectors responds to a more day shock. Once we have the inputs response function, as I told you, it's possible to construct the cumulative inputs just integrating the dashed red line for each sector up to two years or three years. This way, we have construct the left-hand side variables of our main regression. We can now proceed with the second step. In the second step, we want to construct the right-hand side object, which are the frequency and kurtosis of price changes. For PPI, we have around 120 sectors. For CPI, instead, we have around 220 sectors. In the figure, we have in the top panel the histogram for the frequency of price changes. In the bottom, we have the histogram for the kurtosis of price changes. In the yellow, we have the histogram for PPI. In the white, we have the one for CPI. From the top panel, you can see that there are some sectors for CPI that have a very low frequency. These are the services. Furthermore, from the bottom panel, you can see that the distribution of the kurtosis of CPI is more spread with respect to the one of PPI. However, both of them have a mean kurtosis of around 4.5. Now that we have constructed the left-hand side object and the right-hand side object of our baseline specification, we can run our regression across sectors. This is exactly what we are going to do now. So we regress the response of prices over a constant and the ratio of the kurtosis and frequency of price changes. In this table, we report the result using producer prices. In the first two columns, we have the response estimated using the Cholesk identification plus the long-run restriction. In the third and fourth column, we have the response estimated using the Cholesky without using, in this case, the long-run restriction. In the last two columns, instead, we have the identification using high-frequency data plus instrumental variable plus long-run restriction. In the first row, you can see the coefficient of the ratio of the kurtosis and the frequency. You can see that the coefficient is always positive and statistically significant different from zero as what the sufficient statistic result predicts. Furthermore, the theory also predicts that the constant should be negative and statistically significant different from zero. And this is exactly what we obtain. Furthermore, as I told you, we can disentangle the effect of the tossing frequency using a forced order theory expansion. When we do this, we obtain that the response of prices must be, we can run a regression of the response of prices over a constant, the frequency, and the kurtosis of best changes. Our result are reporting this table and also in this table, we are still using on the producer prices. The column of this table are the same as the previous one. In the first row, we report the coefficient for the frequency which is always negative and statistically significant different from zero which is what the sufficient statistic result predicts. Furthermore, in the second row, we can see that the kurtosis is always statistically significant from zero and positive. The constant, furthermore, is negative and statistically significant. These are all in line with what the theory predicts. Furthermore, the theory predicts that the coefficient of frequency and kurtosis should be the same in absolute value. For example, inspecting the second column, we can see that the two coefficients are not far from each other. Indeed, when we run an F test under the noun that the two coefficients are the same in absolute value, we couldn't reject the noun. There is a message in the chat, but okay. Go ahead. No problem. Sorry, sorry. We can even further push the theory. Indeed, the theory predicts that the derivative of the response of prices with respect to odd moments as for example, the mean and the issuance. Calculated at zero mean as soon as must be equal to zero. This means that other moments are the mean and the issuance should not explain the propagation of more than short. We can even also run a regression in which we add these two moments and we can also add the standard deviation of prices. Also, these moments should not be informative of the propagation of more than short. To do this, we run a regression of the response of prices over a constant plus the ratio of the kurtosis and the frequency plus the mean, the standard deviation and the skewness of price changes. The result of this specification are reported in this table. This table is still only using producer prices data. The column of the table are exactly the same as the previous two tables. In the first row, we see that the coefficient of the ratio of the kurtosis and frequency also in this case is still statistically significant different from zero and positive. Furthermore, the coefficient is very similar, it's very close to the one that you obtain our first regression, which was the regression in which we were not including the mean, the skewness and standard deviation. From the second, the third and the third row, you can see that other moments are not statistically significant in explaining the propagation of monetized shocks. Until now, I have only speak about the result for producer prices. Now I go through the result using consumer prices. The result are shown in this table, but we report only the main two regression that was shown. So we, in the top panel, we report the result from regressing the response of prices over the ratio of kurtosis and frequency. In the bottom panel, we report the results when we regress the response of prices over the frequency and the mean. From the first row, we can see that in this case, in the majority of the cases, the coefficient of the kurtosis and the frequency is positive and statistically significant. However, this is not the case in the first two columns. In the first two columns, yes. Five more minutes. Yes, thank you. Yes, thank you. Furthermore, we can see that the constant in the top panel, also in this case, is negative and statistically significant from zero. In the bottom panel, we can see that the coefficient on the frequency in all our specification is still negative and statistically significant from zero. Instead, the coefficient on the kurtosis is always positive but statistically significant in the majority of the cases. This is not the case when we look at the third and fourth column. We believe that the result for CPI are weaker than the one of PPI because in CPI we have some sectors with lots of discounts and the sufficient statistical result does not hold in a model with discounts. Indeed, not reported, but when we run a regression excluding regression for CPI, excluding clothing and foods, the result for CPI are much more in line with the one of PPI. Let me conclude. In this paper, we have provided an empirical test for the sufficient statistic result. We have done these exploiting the across sector variability using PPI and CPI. We have found evidence for the sufficient statistic in particular using PPI. Our result for RLS robust when we use consumer data. What we have found is that the ratio of the kurtosis frequency as they predict a sign and the statistically significant difference on zero. Furthermore, we couldn't reject the hypothesis that the coefficient of the kurtosis frequency have the same effect in mind, implying that also the kurtosis is important in explaining the propagation of model shock, which is exactly the novelty behind the sufficient statistic proposition. Furthermore, we have also found that other moments as they mean the skewness and the standard deviation are not statistically significant in explaining propagation of model shock. Our results, all for several robustness checks that we have not reported here. For example, the odds when we remove potential outliers of the response of crisis or of the kurtosis or of the frequency. We believe that the result for CPI are weaker because in CPI, there are some sectors that have lots of discounts and the sufficient statistic result does not apply in a model for with discounts. Thank you very much. I'm happy to answer any question later, but now there is the presentation of Peter. Peter, the floor is yours. Okay, so thanks a lot. It's great to be here. It's a joint work with Raphael Schoenland and Jesse Wurston and who might be here answering some of your questions if you have during the presentation. So the paper is about measuring price selection in microdata. And the question is that we are revisiting is basically a classic question. So it's that the rigidity of price level influences the real effects of monetary policy as well as the amplification through demand channels. And we know from previous research that prices change infrequently and in standard models, low frequency actually implies that the price level is rigid. However, in models which are microfunded by fixed menu cost of adjustment, the price level can stay flexible even if a small fraction adjusts because large price changes are going to be selected. And why is this the case? It's because of the menu cost, it's optimal. So it's a fixed cost of adjustment. So if you need to change prices, it's optimal for you as a price setter to concentrate on the most mispriced products. And then when an aggregate shock hits, the adjusting prices are going to be those that are most mispriced. And because of this, they change a lot. And this together, so the interaction of the individual mispricing and the aggregate shock is going to raise the flexibility of the price level. And this can potentially be so big as to make monetary policy completely ineffective in influencing the real economy. So in this paper, we are going to revisit this, also when Lucas critic to price rigidity by establishing new facts using micro data. So we are going to generate proxies for mispricing. We are going to identify aggregate shocks and we are going to measure selection as the impact of this gap and shock interaction. So a micro-micro interaction on the probability of the price changes. In particular, we are asking whether is it true that prices with large gaps are changed with higher probability than those with small gaps when an aggregate shock hits? So in this sense, we are measuring whether selection is present in the data. And what do we find? We find actually evidence for state dependence in the sense that the price change probability as well as the size increases with the price gap. But we find no evidence of selection and I'm going to be clear about how we define it exactly. But in, so what we find is that the gap, this mispricing is basically immaterial than an aggregate shock hits. Instead, the price response, the price level response is going to be state dependent but through a gross extensive margin. So it's going to adjust through the shift in the share of price increases versus price decreases. That's going to be the main state dependence channel. And this, we find that these evidence provides guidance for model choice and some policy implications. In particular, the results are consistent in mildly state dependent models and sizeable monetary non neutrality. So let me just go over to clarify the notions of kind of the main idea behind selection. And for this, let me use an intuitive framework by Cavalier angle. The starting point is that there are price adjustment frictions which are going to lead to lumpy price adjustment. And an important state variable, product level state variable in these models is the price gap. So how far the price of the product is from its optimal price. The important thing is that the optimal price is unobserved. And because of these lumpy kind of costs or adjustment, the price of the product only adjusts occasionally while the optimal price is influenced continuously by both product level and aggregate factors and what comes out from these micro funded models is that there is going to be a dispersed distribution of this price gap. And this is what is shown on the right hand side. So the focus of our analysis is going to be the shape of the adjustment hazard. So the adjustment hazard is just reports the probability of adjustment as a function of the gap as a function of the mispricing. And in a simple menu cost model, this becomes a step function. So for small gaps below a threshold, there is going to be no adjustment. And above a threshold, there is going to be adjustment with probability one. So then together, so the gap density together with the probability with this hazard function is going to imply the price change distribution. So that's going to be in this case, shown in the dark black area. So these are the price decreases. And in this setup, the price changes are going to be large because only those prices change, which are higher, which have price gaps, which are higher than this inaction. This inaction band or threshold. And importantly, this is not what we call selection. So this is just normal time, high price changes. This is actually what we will refer to as stay dependence. This is, but what's in instead selection is what happens when an aggregate shock hits. So an aggregate shock in this setup hits by shifting the hazards to the left. And then because of the aggregate shock, there are going to be new decreases, which are going to appear. And selection is going to be large. If these new decreases are large, they are far from their optimum because these new decreases are going to determine the flexibility of the price level, how they are going to influence the aggregate prices. In SS models, these are far from their optimum, the new adjusters. So their adjustment is going to be large and the price level is going to be flexible. In Calvo model, the hazard is flexible. So there are going to be basically no new adjusters. There's going to be no selection. And what Kolozov and Lukas showed that in a model with large selection, the input responses after a monetary policy shocks are going to be, so the real effects, which are shown here are going to be smaller and temporary while in a Calvo model all without any selection are going to be large and persistent. So selection is really important, even though here the frequency of the price adjustment is the same across the two models. So what we now do in the paper, so the data we are, I'm going to show you, but in the paper we also show robustness results using PPI data is supermarket scanner data. And we like it because it's very granular. So products are identified at the barcode level. It also has a wide coverage in the US as well as 12 years of weekly data. So the granularity gives us high quality information about close substitutes and the time series is going to give us, so it's long enough for us to identify aggregate fluctuations. So we are doing some standard cleaning in the data. We are filtering out temporary discounts and we also move from weekly data to monthly frequency using the monthly mode as monthly prices. So this figure shows how our supermarket price index does in the aggregate. The orange line shows CPI food at home sub-index while the dark red line shows the reference price index. So the sales filtered supermarket index. As you can see, the index captures the business cycle of variation of the series well. It is not perfect in matching the level of the inflation because it ignores new product introductions. And that's well known, but this is not something that we are focusing on. So our question is really about what business cycle responses to shocks. So our starting point is that even though the optimal prices at the product level and because of that, the gap is unobservable, an important component of it is observable. And this important component is basically how far a price is from the average price of close competitors after content is reached. After controlling for the competitors or the other stores kind of persistent characteristics and basically price differences because of regional variation and amenities. And the idea here is that stores when we are comparing the exact same products, stores don't want no mispricing.  They could increase their profits by increasing their prices. So in particular, what we do, we take the sales filtered reference prices and calculate the gap, as I said, as a difference from the exact same product from the price in competing stores. And also we are controlling for a store fixed effect by calculating just the fixed effects regression of the form here, as seen on this slide. So with this, we are eliminating permanent differences in store pricing. And in our analysis, we are also going to use lag gap. In that sense, it's pre-determined. It's before an aggregate shock hits. It's a measure of initial mispricing. So let me show you some data. So here, what we do is calculate these gaps and just show you the density of the gap. And what you can see here is that the density itself has a really sizable dispersion and also fat tails, even though we are filtering out sales and also store fixed effects. It's also important to point out that most of the distribution is between minus 20 and plus 20%. So this is where the relevant relevant ranges are still. What we are showing here is the size of the price change conditional on having a particular price gap. And what you see here is that on average, there is almost one-to-one relationship between the gap and the size. So basically, on average, when firms change their prices, they are closing the gap. So in that sense, this justifies other factors justifies us using it as a relevant component of the gap. And the last figure I'm showing you here is an adjustment hazard in the data. So here, you can see the probability of the adjustment conditional on the gap. And what you can see right away is that it has this familiar V-shaped form, which is in line with state dependence. And so it clearly increases with the distance from zero. What you can also see is that it's a symmetric. So there is a higher probability of adjustment if your price gap is negative. So your price is below your competitors' prices. You don't like it. So you like it less when it's above. And it's also, I mean, it's in the eye of the beholder, but we would like you to kind of see that in the relevant range, minus 20 plus 20 range, it's almost piecewise linear. So there is not much nonlinearity happening in this range. Actually, it becomes even less if we control for heterogeneity, unobserved heterogeneity using store item fix effects as we do in the regressions to come. So now we have this measure of the price gap. Now we need an aggregate shock. And for this, because our sample is between 2001-2012, we are concentrating on the credit shock. But we also, in the paper, we also show that the results are robust using monetary policy shocks. And we identify it using timing restrictions following Gil Kristens-Zakrajek's paper so we use their excess bond premium measure, which is a default free corporate spread, and identify credit shocks as a shift and increase in the excess bond premium without any contemporaneous effect on activity, price, and interest rate. And to show you how it looks like in the dynamics or how these shocks looks like, we just run a series of OS regressions like the local projections analysis where we look at how these credit shock influences different aggregate variables using a set of controls. And for controls, we are using 1 to 12 once legs of CPI industrial production, the one year rate and the excess bond premium. So how the shocks looks like. So if there is a tightening of the credit conditions, the excess bond premium increases and stays high for almost a year. Monetary policy eases, but it's not sufficient to offset the real effects of this tightening. So industrial production drops, a ham shape pattern, and core CPI is declining and the peak effect of the decline is around two years after the shock. If we look at for the same shock, what happens to our supermarket price index, we see that the effects are very similar than the core CPI. So there is a gradual response not unlike the core CPI and the peak effect is not before the 24 months. So we are going to use this 24 months period in our following analysis. So with this product level proxy and the aggregate shock, we can now assess that is it true whether the new adjusters after a shock have large gaps. And we do this by looking at selection as an interaction of the aggregate shock and the product level proxy in a linear probability of the model of price adjustment. So the form of our main regression is the following. So in the left hand side, we have an indicator of price increases and price decreases separately for a product in a particular store between the current period and 24 months in the future. Peter, you have five minutes. Thanks a lot. And our regressors, so we are interested in how this adjustment probability influenced by the gap, the price gap itself, the aggregate shock itself and their interaction. And our focus is going to be whether the interaction is significant because if it's significant, it means that over the normal effects, large shocks, so large products with large gaps are response with higher probability than the aggregate. So we are going to run a series of controls, aggregate controls as before. We have also the age of the price as a control variable. And then we set the regression with product store level fixed effects as well as calendar months fixed effects and cluster the standard errors across categories and time. So this is the main results of the paper and in the paper we have a series of kind of a lot of robustness tests and actually the results stay similar. What we find is that the gap itself has a very strong effects on the increases and it's as expected. So a higher gap decreases the price increase probability and increases the price decrease probability. The shock itself has also a significant effect. So a tightening of the credit conditions reduces the price increase probability and increases the price decrease probability but the selection effects or their interaction are not significant and this is consistent with a lot of the robustness regression. So in that sense, we find evidence for state dependence. The gap raises frequency and the adjustment in the cross-section in the sense that the aggregate shock shifts the probability of increases versus decreases irrespective of the gap basically all around the distribution but no evidence for selection. And actually you might vary that we are imposing linearity even though this is not necessarily the case. So here what we are doing as a robustness we look at, we create groups and run a regression basically looking at how these different groups respond to the aggregate shock as the function of the gap and as a function of the interaction. And what you see here is that these regression actually justifies our assumption of linearities. It's not exactly linear but it's very close to linear and the effects are very significant for the direct effects of the gaps but stay insignificant for the interaction terms. And as I said in the paper we have a series of robustness tests. So what did we learn from this? We're getting to two minutes. Thanks. So what we point out is that it's useful to separate the extensive margin effect that Kabalyera Engel emphasized into a gross extensive margin effect and the selection. And the gross extensive margin is the shift between price increases versus price increases and the selection is better large gaps just with high probability condition on the shock. And we point out that if you have a linear probability model that I'm showing here where the hazard function is linear as well as flat then what happens is that an aggregate shock is going to shift the hazard function to the left but this shift is going to have basically the same effect as we have seen in our data. In particular there's going to be a uniform shift in the probability of adjustment for example here for a tightening the decreases go up but this is going to be uniform across the distribution of the gaps. There's going to be no selection. The new adjusters are not going to be larger than otherwise. So just to overview the effects we argue that our data points to having gross extensive margin and no selection this is inconsistent with time dependent models which have none of them but this is also inconsistent with the SS models with strongly convex hazards because they should have both selection and gross extensive margin if the model is kind of linear or close to linear the hazard then it is consistent with the data in particular there is no selection but there is gross extensive margin effect so there is state dependent effects but not selection. And just to show how the data can be used we are also looking at we are taking a model of the shelf in particular roots for its rational attention, extension of the Colossal and Lucas menu cost model and use it to calibrate the density of the price gaps and the hazard function to show that then if we are using a model we can use the frame, the evidence we have to assess the non-neutrality to the cargo and here you can see how the model calibrated how we managed to calibrate the model to get to the the data and it is kind of in line with the evidence and then after we calibrated the model we can ask the extent of monitoring non-neutrality we are doing the same exercises as Woodford did in particular looking at monetary shocks and how they influence the price level in a series of regressions and we are asking how close the price level reaction is to the cargo effect to the cargo case when this information friction parameter is very high or when this information friction parameter is very low and what we find is that for the calibration that matches our data the effects are 20% higher than cargo even though in an SS model it would be almost 5 and a half times as flexible in an SS framework so the estimated information friction parameter here implies high monitoring non-neutrality so I'm right out of time so let me just conclude so we use a granular supermarket and in a paper also PPI data to measure selection we found evidence for state dependence in the sense that adjustment probability and the size increases with the gap but no evidence for selection in the sense that condition on the adjustment sorry the adjustment is independent of the price gap instead the state dependent adjustment is going through the gross extensive margin so the shift between increases versus decreases and these results are inconsistent with standard time dependent and state dependent models but consistent with the mildly state dependent models for example the one with tight information constraints as in Woodford which also imply sizeable monitoring non-neutrality thanks a lot and looking forward to the discussion we have a very dense chat but for now mostly questions about the first paper and Francesca has been very active in responding to some of the questions as co-author I think there is still one by Daniel outstanding which is about the econometrics and so Daniel asked about how worried you are Andrea about having generated regressors in your regression what could be the effect in terms of significance of your results your okay now I could not mute myself I do not like that now I can and he is right the regressors are generated but we have lots of micro price change data so we should be able to estimate quite precisely the kurtosian frequency and furthermore if that is not the case we should have attenuation bias in our baseline regression when you run over the ratio so in that case the coefficient should be downward bias and so it just our results should be quite consistent with that but he is right the frequency and kurtosis are generated and then there was a question where if I may where Francesco already replied partly on Matt was asking about fixed effects so there has been a back and forth in the chat about did you control for fixed effect but can I add a twist to that something I do not understand yet to myself is when you want to test for sufficient statistics what is really that test you know it is within a model then what is the role of unobservables which you would try to capture with fixed effects in the test for sufficient statistics so would you want to saturate or do you think it is actually effective? Yeah exactly that is a really good point that also we try to respond in our favor that for us it is not obvious that you should add fixed effect like if there are difference across sector we want to exactly exploit the difference so yeah our baseline indeed is without fixed effect when you add fixed effect our result we can but that is also as Francesco was saying because we have 40 dummies and we have 120 sectors so there are lots of dummies for like you end up with every sector more in average free free sector free observation you know so it's it's quite normal that the results will be weakened I don't know if I answered your question to me yes I think Matt can write if it's not satisfied but now we have also a question for Peter from which Peter can read but I read it a lot for everybody if did you try some form of order probit Peter instead of linear probability on another channel of course maybe why did you choose linear probability Yes so let me try to share a slide showing showing those results so here so as a robustness this is a very important question so linear probability models are kind of an approximation and needs to be checked whether more challenging setups are kind of the results stay robust or not so here we show results the order probit is the third column and the coefficients obviously here cannot be directly compared to the linear probability model but in terms of the patterns they are very similar so in the sense of the gap having it expected so it's the same significant effects the shock itself also having a significant effect with an expected sign and the selection staying insignificant we also run regressions with multinomial probit here was the only point when we find actually some significance for the selection effect but it was not very strong and only for rise decreases the detailed results you can also find in the paper so if I see no other really pending questions in the chat more of a discussion maybe also between Anton and Francesco and everybody else as well okay so we have from Cristiane a question Peter what would it take to apply the results on the Prisma data right the methodology well these are I think part of the Prisma data but you I think it's a completely it's a completely fair fair question so this is US this is now concentrating on the US data but we are just to confirm we are working on looking at another analysis using Euro area supermarket data the difference there is that for the Euro area data we don't yet have long enough time series to run this kind of selection analysis analysis of both the combining the cross section with the aggregate shock instead what we do is document the cross sectional moments and then look at so kind of identify the parameters of a theoretical model and then try to draw comparisons between the US and Euro area and the work is in progress so we don't yet have results but we are close so I think hopefully I'm going to be able to report these results soon enough but unfortunately we had a big shock so as we get the data from 1920 and so on that will help Peter you have a question from Alexander Jung from the ECB you can read it off or I can read it off how dependent are your results on the selection versus state dependent on the data set and then the pricing may be different on geographic levels yes so I think that's that's fair what what we what we tried so in sometimes are how we compare prices is we compare it nationally but because we want kind of a large enough comparison but we control for or kind of permanent differences in geography so we control for store fixed effects so in that sense we are taking into account some of these heterogeneity but it's true that in our with our data we could actually run robustness looking at only certain geographies, certain markets how it goes but how it does we haven't done it yet but then we need to restrict to goods which have enough which are kind of more popular which are more available in more of the stores but I think it's a good idea we should probably do that so we have and it will have to be the final question I'm afraid from Christoph on evidence that once the capital adjusted firms remain on the average price level or they move around it or they just start getting another gap so what evidence we showed is that on average they close the gap over two year over two year horizon so in that sense you should I'm conditional on changing they are closing it after that so we haven't really looked at kind of dynamics after this gap gap happened I expect this gap to be opened up again over time and then closed again so I think it's not so they are going to adjust only regularly but this is something we need to look into the data


As survivors and frivers, in this video I'm going to be discussing a more positive topic, self-validation. Please like, comment, share and subscribe. Click the bell icon to receive notifications for my future videos. If you would like to donate, my PayPal link is in the video description. If you are interested in one-on-one coaching, you can email me at narksfivercoaching.com. Self-validation Validation is the recognition or affirmation that a person's feelings or opinions are valid or worthwhile. An example of validation could be emotional support or encouragement. Validation does not mean that you agree or approve of the person's feelings or opinions. It is the action or process of affirming their feelings or opinions. Acknowledging the existence of them, validation helps to build relationships and regulate emotions. It is a way of showing that you understand and accept their thoughts and feelings just as they are. Self-validation is the acceptance of your own thoughts and feelings. It doesn't mean that you believe your thoughts or feelings are justified. It is normal to have thoughts which may be unexpected or don't reflect your principles or standards of behaviour. You may also have feelings that are not justified. The more you fight these unjustified thoughts or feelings or judge yourself for having them, the more emotional distress you may experience. Self-validation will allow you to manage your emotions more effectively. The acceptance of these emotions will calm you and help you to understand more about yourself. Self-validation strengthens your identity and improves your skills at managing your emotions. It will also improve your experience, knowledge and good judgement. Self-validation will involve mindfulness, which is the quality or state of being conscious or aware. Mindfulness involves focusing on your awareness, on the present moment, acknowledging and accepting your feelings, thoughts and bodily sensations. There are six levels to validating yourself. Level one is to be present. Like dissociating, daydreaming or preventing the development of your emotions. Listen to yourself. Feel the pain of the sadness, fear, guilt or shame you are experiencing. Level two, accurate reflection. Whatever you are experiencing, make it visible or understood. Acknowledge your internal experience and label it accurately. Based on what may have triggered that emotion and identify when the event may have occurred. Level three, guessing. You won't always know exactly what you are thinking or feeling. When this happens, you could imagine as though you are observing the situation from a third person point of view. Imagine as though you are someone else outside of the situation, observing what you are experiencing. Imagine how another person would feel if they had a similar experience and then apply those feelings to what you are experiencing, to see if it resonates. Level four, validating by history. You may have thoughts and feelings that are based on events that have happened in your past. Maybe when you argue with your relationship partner or friend, it reminds you of something that you experienced when you were a child. You may remember your parents fighting and how it caused your emotional distress. Validate your thoughts and feelings and tell yourself that it is acceptable and understandable for you to have those thoughts and emotions. Level five, normalising. Avoid fighting or judging any intense emotions. Sometimes people believe that their emotional reactions are not normal. We all have emotions and no one is happy all of the time. It's normal to experience sadness, anger, guilt, shame or any other emotion. It may not be pleasurable to experience these emotions but understand and accept that it is normal for you to experience them. Level six, radical genuineness. Be true to yourself. Proceed from genuine feelings. Do not let other people define you or make decisions for you that you should make for yourself. Rejecting yourself is one of the highest levels of invalidation. You are so much more than your behaviours. So much more than your thoughts and feelings. Though changing your thoughts, feelings or behaviours may relieve your pain or suffering. Keep a gratitude journal to recognise and celebrate all of the good things that have happened in your day. Write down the things that you have done well. The positive decisions you have made and the progress you have made. You can even write down the things that didn't require any action. Just being without judging the present moment, you may be tempted to seek external validation. When you do this, you are basically looking for someone else to approve of you or believe in you when you could just give yourself what you are seeking from them. Give yourself the validation first before you seek it from anyone else. Once you have given it to yourself, it is likely that you won't even desire it from anyone else. People seem to gravitate towards those who are self-validated. A person who is self-validated does not need anything from you. They are already full. They are willing to give you their time and energy and listen to what you have to say. A person who is self-validated can make you feel as though you are the most important person in the world. They have the power of delighting, attracting and fascinating people. When you practice self-validation, you become the charismatic, charming person that everyone gravitates towards. Thank you for watching. I hope this video resonated with you. Please like, comment, share and subscribe. Click the bell icon to receive notifications for my future videos. If you would like to donate, my PayPal link is in the video description. Coaching Inquiries, you can email me at narksevercoaching at gmail.com. We have a private reddit community where survivors and frivers can share their experiences and knowledge. If you are interested in being a part of this community, you can sign up for our Patreon page, the links in the video description. Check out the narksever merchandise store where you can purchase your own narksever t-shirt or mug. We have a variety of designs and it is a great way to support the channel. The link to our narksever merchandise store is in the video description. Continue to practice self-validation. It is essential for living with intense emotions. It will help you to regulate those emotions and build relationships. The more you practice self-validation, the easier it will become. Thank you for watching and I will talk to you soon.