Web14/12/ · IT Pro Today Homepage. If you're an IT pro, you undoubtedly have at least some geek in you — or you have a geek in your life WebBinary Options Trading does not give you ownership of the assets you invest in. Assets have predictable price fluctuations, you can use this to your advantage. There is a common misconception in the market that binary trades are far riskier than other trading strategies, as, if the prediction goes wrong, one may end up losing all the money WebS ¡Æ= x°qèš @÷À \ÿÿ`4™-V›Ýát¹=^Ÿßÿ—ß¬ÿŸ{- ó Uu Œ"PDè‹ùÜ46åì ãKR ®r wK e†‘"Ô!„Êí^ ïºÃ÷ùÒÓ ðÿ¿÷{í3[[email protected]& H9–q Web26/10/ · Key Findings. California voters have now received their mail ballots, and the November 8 general election has entered its final stage. Amid rising prices and economic uncertainty—as well as deep partisan divisions over social and political issues—Californians are processing a great deal of information to help them choose state constitutional Web09/02/ · Truth: Organizations need not make a binary choice between EPP or EDR. In fact, these are two distinct capabilities that hold limited value on their own. You can think of EPP as a car and EDR as an engine — one is virtually useless without the other. Misconception 2: EPP is a passive form of prevention ... read more

It directs revenues to regulatory costs, homelessness programs, and nonparticipating tribes. Some revenues would support state regulatory costs, possibly reaching the mid-tens of millions of dollars annually.

If the election were held today, would you vote yes or no on Proposition 27? Initiative Statute. It allocates tax revenues to zero-emission vehicle purchase incentives, vehicle charging stations, and wildfire prevention. If the election were held today, would you vote yes or no on Proposition 30?

Do you agree or disagree with these statements? Overall, do you approve or disapprove of the way that Joe Biden is handling his job as president? Overall, do you approve or disapprove of the way Alex Padilla is handling his job as US Senator?

Overall, do you approve or disapprove of the way Dianne Feinstein is handling her job as US Senator? Overall, do you approve or disapprove of the way the US Congress is handling its job? Do you think things in the United States are generally going in the right direction or the wrong direction?

How satisfied are you with the way democracy is working in the United States? Are you very satisfied, somewhat satisfied, not too satisfied, or not at all satisfied? These days, do you feel [rotate] [1] optimistic [or] [2] pessimistic that Americans of different political views can still come together and work out their differences?

What is your opinion with regard to race relations in the United States today? Would you say things are [rotate 1 and 2] [1] better , [2] worse , or about the same than they were a year ago? When it comes to racial discrimination, which do you think is the bigger problem for the country today—[rotate] [1] People seeing racial discrimination where it really does NOT exist [or] [2] People NOT seeing racial discrimination where it really DOES exist?

Next, Next, would you consider yourself to be politically: [read list, rotate order top to bottom]. Generally speaking, how much interest would you say you have in politics—a great deal, a fair amount, only a little, or none? Mark Baldassare is president and CEO of the Public Policy Institute of California, where he holds the Arjay and Frances Fearing Miller Chair in Public Policy. He is a leading expert on public opinion and survey methodology, and has directed the PPIC Statewide Survey since He is an authority on elections, voter behavior, and political and fiscal reform, and the author of ten books and numerous publications.

Before joining PPIC, he was a professor of urban and regional planning in the School of Social Ecology at the University of California, Irvine, where he held the Johnson Chair in Civic Governance. He has conducted surveys for the Los Angeles Times , the San Francisco Chronicle , and the California Business Roundtable. He holds a PhD in sociology from the University of California, Berkeley.

Dean Bonner is associate survey director and research fellow at PPIC, where he coauthors the PPIC Statewide Survey—a large-scale public opinion project designed to develop an in-depth profile of the social, economic, and political attitudes at work in California elections and policymaking. He has expertise in public opinion and survey research, political attitudes and participation, and voting behavior.

Before joining PPIC, he taught political science at Tulane University and was a research associate at the University of New Orleans Survey Research Center. He holds a PhD and MA in political science from the University of New Orleans.

Rachel Lawler is a survey analyst at the Public Policy Institute of California, where she works with the statewide survey team. In that role, she led and contributed to a variety of quantitative and qualitative studies for both government and corporate clients.

She holds an MA in American politics and foreign policy from the University College Dublin and a BA in political science from Chapman University. Deja Thomas is a survey analyst at the Public Policy Institute of California, where she works with the statewide survey team. Prior to joining PPIC, she was a research assistant with the social and demographic trends team at the Pew Research Center. In that role, she contributed to a variety of national quantitative and qualitative survey studies.

She holds a BA in psychology from the University of Hawaiʻi at Mānoa. This survey was supported with funding from the Arjay and Frances F. Ruben Barrales Senior Vice President, External Relations Wells Fargo. Mollyann Brodie Executive Vice President and Chief Operating Officer Henry J. Kaiser Family Foundation. Bruce E. Cain Director Bill Lane Center for the American West Stanford University. Jon Cohen Chief Research Officer and Senior Vice President, Strategic Partnerships and Business Development Momentive-AI.

Joshua J. Dyck Co-Director Center for Public Opinion University of Massachusetts, Lowell. Lisa García Bedolla Vice Provost for Graduate Studies and Dean of the Graduate Division University of California, Berkeley. Russell Hancock President and CEO Joint Venture Silicon Valley.

Sherry Bebitch Jeffe Professor Sol Price School of Public Policy University of Southern California. Carol S. Larson President Emeritus The David and Lucile Packard Foundation. Lisa Pitney Vice President of Government Relations The Walt Disney Company. Robert K. Ross, MD President and CEO The California Endowment.

Most Reverend Jaime Soto Bishop of Sacramento Roman Catholic Diocese of Sacramento. Helen Iris Torres CEO Hispanas Organized for Political Equality. David C. Wilson, PhD Dean and Professor Richard and Rhoda Goldman School of Public Policy University of California, Berkeley. Chet Hewitt, Chair President and CEO Sierra Health Foundation. Mark Baldassare President and CEO Public Policy Institute of California. Ophelia Basgal Affiliate Terner Center for Housing Innovation University of California, Berkeley.

Louise Henry Bryson Chair Emerita, Board of Trustees J. Paul Getty Trust. Sandra Celedon President and CEO Fresno Building Healthy Communities. Marisa Chun Judge, Superior Court of California, County of San Francisco. Steven A. Leon E. Panetta Chairman The Panetta Institute for Public Policy. Cassandra Walker Pye President Lucas Public Affairs. Gaddi H. Vasquez Retired Senior Vice President, Government Affairs Edison International Southern California Edison. The Public Policy Institute of California is dedicated to informing and improving public policy in California through independent, objective, nonpartisan research.

PPIC is a public charity. It does not take or support positions on any ballot measures or on any local, state, or federal legislation, nor does it endorse, support, or oppose any political parties or candidates for public office.

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Daily volume with indeed fluctuate up and down, as market conditions dictate. While everyone wishes to earn as much money as possibly , and must be actively trading binary options in order to do so, patience is often the most important key to success when trading binary options. One of the newest features of the binary options market allows for the ability to close trades before their expiration times. But recent trends have shown that brokers are becoming increasingly open to this feature and the increased account signups that have been seen indicate that traders are equally interested in the increased flexibility that is made available through these features.

But when exactly is the right time to close a trade prior to its contract expiry time? And what are the advantages of ending your trade early?

In recent years, we have seen events such as the Credit Crisis which led to extreme volatility in the financial markets. These rapid changes in price can make the outcomes for trades less predictable and this can lead to trades that are profitable one day and unprofitable the next. There are many reasons why situations like this might occur. Some of the most drastic events could come with events like a natural disaster, a surprise central bank decision to change interest rates, a disappointing corporate earnings report or an unexpectedly strong macroeconomic data release can all lead to unpredictable changes in asset prices.

To be sure, this can be a positive when the change falls in line with your trading direction. But it is nearly impossible to know when this favorable outcome will occur and when the news comes out on the opposing side, losses can be seen. This can be a highly frustrating and costly experience, as gains that were seen previously are suddenly wiped away. Unlike spot markets like Forex there were no defensive moves that binary options traders could take to preserve their gains.

Now, however, traders are able to close a profitable position using the early closure function whenever one of these unexpected events occurs. In other cases, trades will move in the wrong direction and create losses to a trading account. Here, the early closure function is also useful.

When it becomes clear that a trade is unlikely to turn positive before expiry, traders can close a trade early and reduce the amount of losses that would be seen later. These percentages will vary depending on which broker you use, and the market conditions seen when the option is bought back.

It should be remembered that the early closure function is not something that should be used to arbitrarily close trades. When trading binary options using market makers, the broker is on the other side of your position. If every trader used this function, losses would occur much less often and the market maker would eventually go out of business because of all the losses they would absorb.

Because of this, there are some rules in place when using this feature:. The relatively new early closure feature at the popular IQ option platform allows trader to protect their profits and prevent against potential losses when unforeseen events shift the market.

While there are some restrictions to trade binary options for this tool, the added level of trade structuring should be utilized in cases where a trade is unlikely to increase in profitability before the contract expires.

Given the dual nature of the binary options trading market, it makes sense to have a broader understanding of the general trends that are in place so that we can make the most informed trading binary options decisions and increase our chances of creating profitable trades.

When looking at the dominant trends that are in places in the markets, it tends to be a good idea to trade along with the momentum: When most asset prices are rising, CALL options tend to be a better choice. When most asset prices are falling, PUT options tend to be a better choice.

To describe which dominant trend is in place, the trading binary options community will usually use term like Bull Market or Bear Market but it is much less common to see a discussion which characteristics actually make up these economic environments. Here, we will look at the differences between Bull and Bear Markets so that traders can more easily identify the dominant trend in a market and to place binary options trades accordingly.

Bull Markets are typically characterized by a financial environment that is composed of a large number of assets that are increasing in value, or are expected to increase in value. In many cases, the term refers to the stock markets but for those in the trading community, the term is applicable for all asset types. Bull Markets are created by generally optimistic sentiment, rising consumer confidence and the wider expectation that companies will successfully generate profits.

One clear indication of the existence of a Bull Market can be seen in the price of commodities, in the changes in valuation of a national currency, and in the overall performance of the major stock indices.

When looking at price activity in all of these various asset classes, it becomes clear that price swings show higher highs and higher lows the definition of an uptrend. When all of these factors are seen in combination with one another, a Bull Market is in place and CALL options will generally be viewed as favorable when entering into trades. Psychology and news headlines in the financial media are also instrumental in these cases, as positive momentum tends to be contagious.

On the flip side of this is the Bear Market, which is typically characterized by a financial environment what a majority of trading assets are decreasing in value, or are expected to decrease in value. Again, this term can be applied to all asset classes and Bear Markets are typically created by pessimistic sentiment, declining consumer confidence and the general expectation that companies will perform weakly in terms of profit generation.

Indications of a Bear Market can be seen all major asset classes commodities, currencies and stock indices when it becomes clear that price swings show lower highs and lower lows in a broad sense which is the definition of a downtrend. The combination of these occurrences create Bear Markets and in these cases, traders tend to prefer PUT options when entering into trades.

Before we answer this crucial question, there is a need to understand that the binary option is a trading strategy, similar to various other trading strategies. It is not an out-of-the-world scheme to help traders make millions or to scam them. Trading binary options is as safe or as unsafe as you make it.

Yes, it is true that it entirely depends on whether you make binary trading safe for you. So, how is it really possible? From registering yourself with a binary options broker firm to making your trades, every step you take will decide how safe trading binary options is going to be for you.

As the first major step, you should carefully analyze different binary options broker firms, choose one registered with different relevant regulatory authorities, hold relevant licenses for investment activities, and have a verifiable track record. This will ensure that you are not scammed by someone who is using trading binary options as a cover for fraudulent activities.

It is always recommended to start investing in a binary options demo account. Once you gain a reasonable level of expertise, start investing in binary options starting with a low amount.

Never make this mistake unless you are fully confident in your abilities. Last but definitely not the least, never trade binary options in a way that you bet all your money on a single trade, no matter how amazing the odds may seem.

The fact of the matter is that even if you win big once or twice with such an approach, you will likely take a wrong position every once in a while and end up losing all the available funds. As discussed in an earlier section, investing all money in a single trade or in a single position is one of the biggest reasons why traders especially amateurs fail in trading binary options.

Remember, it is easier to blame the firm or the trading binary options strategy than to accept your shortcomings or wrong steps. Never look for shortcuts to earning big profits, and binary options trading will never be unsafe for you. It is true that some instances of scams and frauds in trading binary options have been reported recently. However, it does not mean that the whole binary options industry is a scam, as you would find instances of scams, frauds, and embezzlement in nearly every industry and business, such as real estate, stocks trading, and even commodities.

As a trader, you can avoid binary options scams by having a strong fundamental knowledge of the binary options industry and knowing some of the major indicators of scams, as discussed below:. Unrealistic promises and claims that are too good to be true may be among the initial indicators of binary options scams. Some legit firms, such as binary. Similarly, you can avoid scams in the binary options industry by registering with firms that have active licenses with relevant regulatory authorities.

We explained this in more details with Expert Option scam. Most renowned binary options firms do have these licenses, and their trades are continuously monitored by the legal and regulatory authorities; hence, eliminating any chances of scams.

But if you find a firm that makes big claims but has no mention of them being regulated, then this is a major indicator of a scam. You must avoid such firms at all costs. Legit binary options brokers usually offer a range of trading platforms, which may include Smart Trader, Meta Trader 5, and some in-house platforms.

At the same time, a legit firm is more likely to offer a free demo account for the newly registered traders to try their charts, signals, and platforms before risking real money. So, make sure to try the free binary options demo account in order to develop a t horough understanding of how the firm operates and the standard of services it offers to its clients. There is no denying the existence of some scams in the binary options industry. Some of them, like Bitcoin Revolution app became more popular with the cryptocurrency boom.

But it should not hold you back from trying a trading platform. Instead, it is your duty to perform due diligence of the firm in order to ensure legit trades. Remember that you must not be held hostage by a few scammers. Your financial future is in your hands, and by doing your homework and choosing the right binary options firm, you can secure your financial future as well as gain financial prosperity in the long run.

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Now you know why I was talking earlier about relationships that were proportional in nature. No approximation required; you just need to pick the size of your percentage increase ahead of time.

Finally, we can put the log on both. Plus, as a bonus, this kind of thinking works for any transformation. For people who own a car, that relationship might be quite strong and negative. For people who own a car but mostly get around by bike, that relationship might be quite weak. The relationship between gas prices and miles driven differs depending on car ownership.

We might want to allow the relationship to vary so we can model it properly. We might also be interested in whether the relationship is different between these groups. The Oster study we looked at in Chapter 4 is one such paper.

However, we have no way of representing this change in relationship with either a polynomial or a transformation. Instead we will need to use interaction terms. No good! The real difficulty with interaction terms is interpreting them. So how can we interpret interaction terms? The question of interpreting a model with interaction terms is actually two questions. The second is is how can I interpret the interaction term? But if you do know calculus, hey, calculus is pretty neat right?

If you ever see a calculus textbook with my name on it, you can be assured that it is either bad, or ghostwritten, or ghostwritten and bad. How about our other question? What is the interpretation of the interaction term itself? Often, the variable being interacted with is binary. What do we have from this equation? In Table How can we interpret this? The You need to take it all in at once. In fact, the effect is positive for the entire range of years actually in the data, despite that big negative Always interpret them with the relevant interactions.

It is! We can also say that the relationship is. There are a few things to keep in mind when it comes to using interaction terms. The first is to think very carefully about why you are including a given interaction term, because they are prone to fluke results if you go fishing.

We need to approach research and data with a specific design. Why do I emphasize this so strongly when it comes to interaction terms? First, because there are a lot of different interactions you could try , and a lot of them seem tantalizing. Does the effect of job training differ between genders? Between races? Between different ages? Sure, it might. We can tell ourselves a good story about why each of those might be great things to interact with job training.

So trying interactions all willy-nilly tends to lead to false positives. The temptation to try a bunch of stuff is extra-hard to resist when you have a null effect. But maybe the effect is only there for women! Try a race interaction. Or maybe only in Iowa. Try a state interaction. However, because there are nearly infinite different subgroups we could look at especially once you consider going deeper - maybe the effect is only there for women Pacific Islanders in Iowa!

Surely there are plenty of effects that are, in truth, only there for a certain subgroup. But fishing around with a bunch of interactions is going to give you way too many false positives. Second, even if we do have a strong idea of a difference in effect that might be there, interaction terms are noisy.

The difference between two noisy things will be noisier still. You need a lot more observations to precisely estimate an interaction term than to precisely estimate a full-sample estimate. Why is noisiness a problem? Because estimates that are noisy will more often get surprisingly big effects. The sampling variation is very wide with noisy estimates. That said, interaction terms are still very much worthwhile if you have a solid reason to include them.

In fact, many of the research designs in this second half of the book are based around interaction terms. Turns out you can get a lot of identification mileage out of looking at how the effect of your treatment variable differs between, say, conditions where it should have a causal effect and conditions where any effect it has is just back-door paths. So give yourself some time to practice interpreting and using them.

It will definitely pay off. Next, we will include an interaction term. In other words, we can predict the dependent variable by taking our predictors, multiplying them each by their coefficients, and adding everything up.

To be able to predict something with a linear function, it needs to behave like, well, a line. Also, it needs to continue off forever in either direction, i. That brings us to nonlinear regression. Plug in your data and it tells you the exact correct answer. making their estimates a little less stable. They can have a hard time with things like controls for categorical variable with lots of categories.

That said, while in some cases the nonlinear-regression cure is worse than the incorrect-linear-model disease, generally it is not, and you want to use the appropriate model. Why exactly is that? Generalized linear models are a way of extending what we already know about regression to a broader context where we need some nonlinearity.

GLM says this: take that regression model, and pass it through a function in order to make your prediction. So our regression equation is.

How does GLM let us run nonlinear regression? Probabilities should be between 0 and 1. There are infinitely many functions that satisfy these criteria, but two are most popular: the logit link function and the probit link function. These two link functions - probit and logit - produce nearly identical regression predictions.

It can also be computationally easier to estimate. The important thing, though, is that neither of them will make predictions outside of the range of 0 and 1, and their coefficients will be estimated in a context that is aware of those boundaries and so better-suited for those non-continuous dependent variables. Why not just stick with OLS?

Two problems with this argument. That will give you strange results too. We can see this in action in Figure On top of that graph we have the fitted OLS model as well as a fitted logit model the probit would look very similar. What do we see? As previously mentioned, we see the OLS prediction going outside the range of 0 and 1. But beyond that, the slopes of the OLS and logit models are different. This is especially true near the right or left sides of the graph.

What does this mean? But why is this? Almost nothing, right? It should be bigger! OLS is unable to account for this. This works in a different way to how the effect differed when we talked about polynomials. Interpreting a generalized linear model can be a little trickier than interpreting an OLS model. In one, very narrow and pretty useless sense, the interpretations are exactly the same.

The difference, however, is that with OLS, the index function is just a regression equation giving us the conditional mean of the dependent variable, and we know how to interpret a change in the dependent variable.

In GLM, the index function is the thing that gets put through the link function. Marginal effects basically translate things back into linear-model terms. Mathematically, the marginal effect is straightforward, although we do have to dip back into the calculus well once again. There is, however, a catch. That catch is that there is no one marginal effect for a given variable. I just said to look back at Figure Well… keep looking at it! Not to do with, say, a treatment being more effective for men than for women.

There are two common approaches to this issue, one of which I will heavily recommend over the other. This gives the marginal effect of an observation with completely average predictors. Who is this marginal effect for, anyway? But in this case, there are other issues, too, and we have better options. The nobody-really-has Plus, marginal effects at the mean take the mean of each variable independently, when really these variables are likely to be correlated.

What I recommend instead is the average marginal effect. Then you have the mean of the marginal effect across the sample, which gives you an idea of the representative marginal effect. Take the median, standard deviation, whatever. Or look at the whole distribution. But in general people focus on the mean here. What about probit? In the case of the R code, you will often see guides online telling you to use the logitmfx function from the mfx package, instead of margins.

This works fine, but do be aware that it defaults to the marginal effect at the mean instead of the average marginal effect. We want very badly to know that we understand what the sampling distribution is.

We bother calculating the standard error because we want to know the standard deviation of that sampling distribution. But if certain assumptions are violated, then the standard error will not describe the standard deviation of the sampling distribution of the coefficient.

That assumption about the normality of the error term is what lets us prove mathematically that the OLS coefficients are normally distributed. That said, there are some weird error-term distributions out there that really do mess things up. The second assumption is that the error term is independent and identically distributed. That is, we need to assume that the theoretical distribution of the error term is unrelated to the error terms of other observations and the other variables for the same observation independent as well as the same for each observation identically distributed.

What does this mean exactly? One way it could fail is autocorrelation , where error terms are correlated with each other in some way. The economy tends to go through up and down spells that last a few years, as in Figure Think about the errors here - there tend to be a few positive errors in a row, then a few negative errors.

Another common way that being independent and identically distributed could fail is in the presence of heteroskedasticity. A big long word! Now this is a real textbook. For example, say you were regressing how many Instagram followers someone has on the amount of time they spend posting daily. You might find that, as shown in Figure Small amounts of time means little variation in follower count and, accordingly, little variation in the error term.

This means that we just have to figure out what the standard deviation of that sampling distribution is. This calculation will require some way of accounting for that autocorrelation or that heteroskedasticity. The Delta method, Krinsky-Robb… so many fixes to learn. Here I will show you only the beginning of the ways your standard errors will be wrong. If you decide to continue doing research, you can prepare for a lifetime of being wrong about your standard errors in fantastic ways that you can only dream of today.

But since regular OLS standard errors simply estimate the same variance for the whole sample, it will understate how much things are changing. To provide one highly simplified example of this in action, see Figure On the left, we have heteroskedasticity. Imagine picking one observation from the cluster on the left and one from the cluster on the right and drawing a line between them. Now imagine doing it over and over.

Now move your hands up and down, independent of each other. Now try moving just one hand up and down. The angle changes much more rapidly! Now do the same thing on the right.

Given this problem, what can we do? Well, we can simply have our estimate of the standard error personalize what we think about the error variance.

One of the more common heteroskedasticity-robust sandwich estimator methods is Huber-White. This, in effect, weights observations with big residuals more when calculating the variance.

There are actually several different estimators that, in very similar ways but with minor tweaks, adjust standard errors for the presence of heteroskedasticity. We can then move along to errors that fail to be independent. Why would correlated errors require us to change how we calculate standard errors? Correlated errors will change the sampling distribution of the estimator in the same way that correlated data would change a sampling distribution of a mean. Say you were gathering samples of people and calculating their height.

If you randomly sampled people from all over the globe each time, that would give you a certain standard deviation for the sampling distribution of the average height. But if instead you randomly sampled families each time and calculated average height, the values would be correlated - your height is likely more similar to your parents than to some stranger. So the mean is swingier, and the sampling distribution has a larger standard deviation.

Some adjustment must be made. Each of the different ways they can be correlated calls for a different kind of fix. One common way in which errors can be correlated is across time - this is the time-based autocorrelation we discussed earlier. In the presence of autocorrelation we can use heteroskedasticity- and autocorrelation-consistent HAC standard errors. These tools are almost entirely absent from this book. A common approach to HAC standard errors is the Newey-West estimator, which is another form of sandwich estimator.

This estimator starts with the heteroskedasticity-robust standard error estimate, and then makes an adjustment. Why not just a fix for autocorrelation and not heteroskedasticity?

Because autocorrelation by its nature tends to introduce heteroskedasticity anyway. If errors are correlated, then at times where one observation has a small error, other nearby observations will also have small errors, producing low error variance. But at times where one observation has a large error, other nearby observations will also have large errors, producing high variance.

Low variance at some times and high variance at other times. That adjustment comes from first picking a number of lags - the number of time periods over which you expect errors to be correlated. We sum up those correlations, weighting the one-period lag by just below 1, then with declining weights for further lags.

Take that sum, multiply it by two, and add one. Autocorrelation across time is not the only form of autocorrelation, with correlation across geography also being highly consequential.

Another common way for errors to be correlated is in a hierarchical structure. Conveniently, in these cases, we can apply clustered standard errors. The most common form of clustered standard errors are Liang-Zeger standard errors, which are again a form of sandwich estimator.

With clustered SEs, you explicitly specify a grouping, such as classrooms. Well, for clustered standard errors, this matrix is block-diagonal, where each grouping has unrestricted nonzero values within its block, but the rest of the matrix is 0. First off, you can do both, and people often do. Second, including the indicator in the model suggests that group membership is an important predictor of the outcome and possibly on an important back door , while clustering suggests that group membership is related to the ability of the model to predict well.

The use of clustered standard errors can account for any sort of correlation between errors within each grouping. This sort of flexibility is a nice relief.

No need to make those restrictive assumptions about error terms having correlations of zero. Of course, statistical precision comes from assumptions.

First off, if you have a strong theoretical idea of when errors should be clustered together, such as in a classroom, go for it. Beyond this, another common approach is to cluster at the level of treatment. Something important to remember about the Liang-Zeger clustered standard errors, though, is that they work really well for large numbers of clusters. Say, more than See more about wild cluster bootstrap standard errors in Chapter So is it just sandwiches all the way down?

The bootstrap is about as close to magic as statistics gets. What are we trying to do with standard errors? We want to provide a measure of sampling distribution, right?

We want to get an idea of what would happen if we estimated the same statistic in a bunch of different samples. Sampling distribution. Huh, that makes sense. But hold on, how is it possible to estimate the statistic in a bunch of different samples? We only have the one sample! It uses the one sample we have, but re-samples it. Does this actually work? We resample again and get 1, 3, 4, 4, with a mean of 3. Do it again!

The next one is 1, 2, 4, 4, mean of 2. When you do a bootstrap you generally want to do it hundreds if not thousands of times. Now, our sample mean is The mean of our bootstrap distribution is If you do bootstrap and want your results to be replicable, be sure to set a random seed in your software before doing it.

A bit smaller than 9. This is showing us that maybe those theoretical-distribution assumptions were actually a little pessimistic about the precision in this particular instance. This is the basic process by which bootstrap standard errors can be calculated. A bit computationally intensive, definitely.

Just bootstrap it. But for an estimate with a decent sample size and some well-behaved independently-distributed data, the bootstrap can be a great option, especially if the regular standard errors are difficult to calculate in that context. Also, there are some alternate versions of the bootstrap discussed in Chapter 15 that address some of these problems. In each of the following code samples, I will show first how to implement heteroskedasticity-robust standard errors, heteroskedasticity- and autocorrelation-consistent standard errors, and then clustered standard errors.

One important thing to know about robust standard errors, especially when it comes to coding them up, is that there are actually many different ways of calculating them.

Each method uses different minor adjustments on the same idea. Some versions work better on small samples; others work better on certain kinds of heteroskedasticity. All of the methods below provide access to multiple different kinds of robust estimators often given names like HC0, HC1, HC2, HC3, and so on.

But different languages select different ones as defaults. For example, Stata uses HC1, while most R methods default to HC3. Next, we will address autocorrelation using Newey-West standard errors. Keep in mind that in each case you must select a maximum number of lags or have one be selected for you. But basically, closely examine the autocorrelation structure of your data to figure out how far to let the lags go. Because these are intended for use with time series data, we will first need to adjust our data to be a time series.

For the last of our sandwich estimators, we can use clustered standard errors. But since this is only a technical demonstration, we can do whatever. There are only two values of this, which is not a lot of clusters. We could get some strange results. Finally, we come to bootstrap standard errors. So… how many should you make? For most applications, a few thousand should be fine. The code below only shows a straightforward each-observation-is-independent bootstrap.

How long is this chapter? This chapter is topping 30, words - novella length! My fingers are tired. But I must press on. You WILL understand linear modeling. Oops, wait, hold on. My ring finger fell off. Alas, as with any statistical method, there are infinite little crevices and crannies to look into and wonder whether the method is truly appropriate for your data.

This is rarely true for a number of reasons. Some people are easier to survey or gather data on than others. When this occurs, obviously, the results are better-representative of the people who were more likely to be sampled. We can solve this issue with the application of sample weights , where each observation in the sample is given some measure of importance, with some observations being treated as more important and thus influential in the estimation than others.

This prevents the estimate from, say, calculating an overall bigger variance rather than just a variance that better-represents some observations. The problem I just mentioned - where certain people are more likely to be sampled than others - is a very common problem in the social sciences. The survey planners have a good idea about how their sampling was done, and so have a decent idea of how likely different individuals, households, firms, or whatever, were to be included in the sample.

Regression is the most common way in which we fit a line to explain variation. When it comes to identifying causal effects, regression is the most common way of estimating the relationship between two variables while controlling for others, allowing you to close back doors with those controls.

Not only will we be discussing it further in this chapter, but many of the methods described in other chapters of part 2 of the book are themselves based on regression. So go back and take a look at that. Those are the basics that more or less explain how regression works. And as long as that regression model looks like the population model the true relationship is well-described by the shape we choose it has a good chance of working pretty well.

What else do we need to know? However, in our actual data, that line is clearly insufficient. It will rarely predict any observation perfectly, much less all of the observations. So now our equation is:. Why the distinction? Well… sampling variation. All we can really see will be the residual, but we need to keep that error in mind. As we know from Chapter 3 , we want to describe the population. You can see the difference between a residual and an error in Figure The two lines on the graph represent the true model that I used to generate the data in the first place, and the OLS best-fit line I estimated using the randomly-generated sample.

Figure Where does it come from? So, for example, if the true model is given by Figure The downward part would cancel out with the upward part. If a variable is in the regression equation directly , then that closes any causal paths that go through that variable. These are saying, in effect, the same thing, just using lingo from different domains. When that happens - when our estimate on average gives us the wrong answer, we call that a bias in our estimate. Many of the others, though, relate to the sampling variation of the OLS estimates.

Conveniently, we have a good idea what that sampling variation looks like. We know that we can think of observations as being pulled from theoretical distributions. We also know that statistics like the mean can also be thought of as being pulled from theoretical distributions.

If you drew a whole bunch of samples of the population and took the mean each time, the distribution of means across the sample would follow a normal distribution. Then we can use the estimated mean we get to try to figure out what the population mean is. Regression coefficients also follow a normal distribution, and we know what the mean and standard deviation of that normal distribution is.

Or, at least we do if we make a few more assumptions about the error term. What is that normal distribution that the OLS coefficients follow? There are only three terms in the standard deviation, so only three things to change. The standard deviation of a sampling distribution is often referred to as a standard error.

How about those other assumptions we need to make about the error term? Okay, so why did we want to know the OLS coefficient distribution again? The same reason we want to think about any theoretical distribution - theoretical distributions let us use what we observe to come to the conclusion that certain theoretical distributions are unlikely. We can take this a step further towards the concept of hypothesis testing.

So with enough sample size we always reject the null. My opinion: use significance testing; it is useful and it helps you converse with other people doing research. And certainly never choose your model based on significance! The first half of this book is about much better ways to choose models. We can see how this works from a different angle in Figure Sometimes I still reject the null, even though the null is literally true.

But not often. Sampling variation will do that! This is silly, of course - we want to find the true value, not just go around rejecting nulls.

A well-estimated non-rejection is much more valuable than a rejection of the null from a bad analysis. But there seems to be something in human psychology that makes us not act as though this is true. Sometimes I fail to reject the null, even though the null is false. Sampling variation does this too! We can also look at the exact percentile we get and evaluate that. If you look back at Figure Using some known properties of the normal distribution, we can take a shortcut without having to go to the trouble of calculating percentiles.

Then, if that t-statistic is below But before we do, one aside on this whole concept of statistical significance. Rather, a plea. Having taught plenty of students in statistical methods, and, further, talked with plenty of people who have received teaching in statistical methods, the single greatest difference between what students are taught and what students learn is about statistical significance.

I think this is because significance is wily and tempting. Not to mention all the people trying to throw it into a volcano. Powerful, tempting, seemingly simple, but so easy to misuse, and so easy to let it take you over and make you do bad things.

So please keep the following things in mind and repeat them as a mantra at every possible moment until they live within you:. Keep in mind, generally: the point of significance testing is to think about not just the estimates themselves but also the precision of those estimates.

But there are other ways to keep precision in mind. You could ask what the range of reasonable null values is construct a confidence interval instead of focus on one in particular.

You could go full Bayesian and do whatever the heck it is those crazy cats get up to. Significance testing is just one way of doing it, and it has its pros and cons like everything else. We have a decent idea at this point of how to think about the line that an OLS estimation produces, as well as the statistical properties of its coefficients.

But how can we interpret the model as a whole? How can we make sense of a whole estimated regression at once? For that we can turn to the most common way that regression results are presented: the regression table. We might be curious whether chain restaurants get better health inspections than restaurants with fewer or only one location.

Some basic summary statistics for the data are in Table We have data on the inspection score with a maximum score of , the year the inspection was performed, and the number of locations that restaurant chain has. Table The first just regresses inspection score on the number of locations the chain has:. I then show the estimated results for both in Table What can we see in Table Each column represents a different regression.

The first column of results shows the results from the Equation The first thing we notice is that each of the variables used to predict Inspection Score gets their own set of two rows. Below the coefficient estimates, we have our measures of precision, in parentheses.

In particular, in this table these are the standard errors of the coefficients. There are a few different ways you can measure the precision of a coefficient estimate. In some fields, using a t-statistic coefficient divided by the standard error is more common than the standard error. The lower the p-value is, the more stars we get. Which p-value cutoff each number of stars corresponds to changes from field to field but should be described in the table note as it is here.

These stars are a way of, at a glance, being able to tell which of the coefficients are statistically significantly different from 0, which all of these are. Which of these exact statistics are present will vary from table to table, Some other common appearances here might be an information criterion or two like AIC or BIC, the Aikake and Bayes Information Criterion, respectively or the sum of squares. but in general these are either descriptions of the analysis being run, or measures of the quality of the model.

For the second, there are a billion different ways to measure the quality of the model. Missing from this particular table, but present in a number of standard regression-table output styles, is the residual standard error, sometimes also called the root mean squared error, or RMSE.

This is, simply, our estimate of the standard deviation of the error term based on what we see in the standard deviation of the residuals.

We take our predicted Inspection Score values based on our OLS model and subtract them from the actual values to get a residual. The bigger this number is, the bigger the average errors in prediction for the model are.

What can we do with all of these model-quality measures? These are generally measures of how well your dependent variable is predicted by your OLS model. Is that a concern? Gets their buns in a knot.

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