8

Be more specific on what you need. Quandl would be a pretty general source which hasn't been mentioned yet. For macro data the St. Louis Fed is pretty good and thorough. Eurostat for European data. historicalstatistics.org for historical data.


7

Identifying assumption: assumptions made about the DGP that allows you to draw causal inference. E.g. exogeneity assumption for IV, parallel trends assumption in diff-in-diff. Identifying assumptions (lack of endogeneity in general) can never be statistically confirmed (a non-reject is good, but it's not confirmation). So assessment of plausibility consists ...


7

The following is the basic idea if we are to estimate the parameters by linear regression. Take the natural log of the production function $F(L,K)=L^aK^b$, you will then get $$\ln(F)=a\ln(L)+b\ln(K).$$ For each entity (e.g., firm) $i$, collect data on the production level $F_i$, the amount of labour $L_i$, and the amount of capital $K_i$. Note that ...


7

Sure you can, just that your interpretation of your variables in your analysis changes however. In this case you are analyzing how investment in differing factors of production affect output. I'd recommend that you may want to estimate a more flexible functional form like the Translog Production Function to check if your function is CES instead of just a ...


6

I don't have advice specific to error correcting model (ECM) setting, but in undergraduate applied econometric class they gave us the generic advice to continue to extend lags in the model until the residuals of the fitted model were serially uncorrelated. For example, in the US life expectancy data, residuals of male life expectancy is serially uncorrelated ...


6

Well, if you believe that treatment is endogenous (which depends on the problem at hand here and is not an inherent feature of the model), then using eligibility as an instrumental variable will help you to get rid of the biases due to the safe selection in treatment. (Incidentally, DID is intended to do the same, but won't do as good a job as a well chosen ...


6

Using corruption is part of it but a bit restrictive way to measure government "quality". You may use aggregate indicators as the one developed by the Worldwide Governance Indicators (WGI) project from the World Bank. They reports aggregate and individual governance indicators for over 200 countries and territories over the period 1996–, for six dimensions ...


5

In the benchmark hedonic price analysis, we assume a utility function of the general form $$U = U(x, z_1,...,z_n)$$ where "$x$" stands for the composite good, and $(z_1,...,z_n)$ are the characteristics of good $y$ that are valued by the consumer. Assume for simplicity (as is usually done in the literature, and as is the OP case), that the consumer will ...


5

I think the answer depends on who is impacted by your measure of distance and for what purpose. Kennan and Walker (2011) Econometrica measure distance between states as "the great circle distance between population centroids" in an attempt to model moving costs. They also include an indicator for whether or not the state is adjacent.


5

The Bureau of Economic Analysis (BEA) is the primary source for US economic data. Other US sources include the Dept of Labor, The Census Bureau, Dept of Commerce, and the US Energy Information Administration. Vizala combines data from a number of international sources. Other international sites/sources include The World Bank, UN Data Statistics Division, ...


5

Easiest fix: if you're worried about it you should value weight your results. This is suggest by, for instance, Kothari, Shanken and Sloan (1995). Firms that are delisted tend to have extremely small market cap, so value weighting gives them very little impact on summary statistics. Delisted returns should also be used, although I'm not sure how much impact ...


5

The omitted variable bias in gravity model is an important issue given that some factors are unobserved or difficult to quantity. To solve this issue trade economists tend to rely on various fixed effect estimators. But, the question is what is your variable of interest? Exporter-by-year and importer-by-year fixed effects For instance, if you are ...


5

If prices are constant then quantities are proportional to expenditures. Consider : $$ Y=AK^{\alpha}L^{\beta} = A(\frac{E_{K}}{r})^{\alpha}(\frac{E_{L}}{w})^{\beta} $$ $$ = (\frac{A}{r^\alpha w^\alpha})(E_{K})^{\alpha}(E_{L})^{\beta} $$ $$ = \tilde{A}(E_{K})^{\alpha}(E_{L})^{\beta} $$ If prices don't vary too much this may be an acceptable approximation. ...


4

Yes, the same authors Berry, Levinsohn, Pakes have written a second paper that uses both macro and micro data to estimate demand for automobiles as a function of the characteristics of the car. "Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market" http://dash.harvard.edu/bitstream/handle/1/3436404/...


4

First of all, let me tell you that you're doing great. I remember that, being a non-parametric method, it was more difficult for me to understand even the simplest things of DEA. Now, your interpretation of DEA is correct. When a firm is considered inefficient, it is because it could have gotten the same output but with a lower cost if it had used the best ...


4

This is a more "practical" answer, vs a deeper theoretical one, or even a specific one. Take it as "a broad answer to a broad question." Also, since it is a "Bayes vs frequentist" issue, at least the last few suggestions must be taken tongue-in-cheek. Step 1: Use simple models you understand to start. Lowering researcher error due to misapplication ...


4

This does not directly answer your question, but I will try to explain why I don't think that such well known papers exist. If submitted I don't think that such a paper would be accepted by the top five journals. This is because the journals also compete to stay relevant, to give surprising information. The phenomenon is known as publication bias. This was ...


4

Stone-Geary production functions attempt to reflect the real-world observation that for production to be feasible, there exist minimum thresholds in the quantities of inputs employed. This is not about initial investment but that one cannot use miniscule levels of inputs and obtain "very little" output -for production to even begin, one needs input amounts ...


4

The simple answer to why the Cobb-Douglas functional form is used is because it is at least a log-linear approximation to some higher-order production function. That is, suppose you take a functional form that looks like this: $\log Y_t = f(A, K, L)$. Then a linear approximation would look like the Cobb-Douglas production function. (For a small $1\%$ ...


3

Maybe another example will help here: Imagine you would like to know the effect of smoking on the probability of getting cancer. By simply comparing cancer rates of smokers and non-smokers you might get a biased estimate of this effect, because perhaps smokers also engage in a range of other unhealthy behaviors that increase cancer risk (e.g. heavy ...


3

The American Economic Association has a list of resources for Economists, including a page for data, there you find links to many institutions that offer all kinds of data, as well as further journals with data archives for the studies they publish. In the ReplicationWiki (that I work on) we have information on more than 2000 empirical studies and you can ...


3

The issue of selection into treatment based on some observable variable that does not enter the outcome equation is solved with a latent index approach or a Heckman 2-step method. A difficulty with Heckman 2-step is the requirement to find a valid instrument, but if you already have one, it will solve your endogenous treatment issue.


3

It appears that you suspect that the regressor "price" is endogenous, i.e. correlated with the error term, and that you consider what kind of instrument to use in order to tackle endogeneity. You think a possible instrument could be revenues or profits, because they are correlated with the price. Say, Revenues, denoted by $R$. But $$R_{ij} = P_{ij}\cdot ...


3

Ergodic and strict stationarity are the essentially the weakest assumptions for which you have a LLN, i.e. can do large sample estimation. Given the very liberal way applied econometricians use laws of large numbers, ergodicity and strict stationarity is almost always assumed. If that is in question, you're simply not in business for consistent estimation. ...


3

There's no magic. What you have to realize is that the result is conditional on the validity of the assumptions: A) Under the assumption that there is measurement error, then yes, the average of two measurements will be on average closer to the truth than a single opinion. This is very believable. We all do this kind of thing all the time. For example, when ...


3

Suppose we observe only $\tilde{x}$ and $\tilde{y}$ which are the true values measured with error: $$ \tilde{x} = x + u$$ $$ \tilde{y} = y + v.$$ We would like to estimate: $$ y = \beta x + \epsilon$$ but all we can really estimate is $$\tilde{y} = \hat{\beta} \tilde{x} + \zeta$$ However, if y is measured without error ($\tilde{y}=y$) we can use reverse ...


3

There are few things that need to be clarified before we want to understand the nature of goods. First concept is elasticity: which is just change of one variable in response to change in another variable, here increase/decrease in demand for good as a result of change in its price or consumer income. Mathematically: Price (point) elasticity: $\...


3

If the levels specification is deemed acceptable, $$y_t = \beta_0 + \beta_1 x_t + \beta_2 x_t^2$$ then it follows that the first-difference specification should not include a constant term in order to be methodologically consistent, $$\Delta y_t = \beta_1\Delta x_t + \beta_2\Delta x_t^2$$ since $\Delta \beta_0 = \beta_0 -\beta_0 = 0$. I suggest you run ...


3

There is a theoretical objection to using seasonally adjusted data, which I saw in an essay by Kalman (developer of the Kalman filter); sorry, I cannot find the reference. The argument is that seasonal adjustment is a filter, that has its own dynamics. You are then embedding these filter dynamics within the system that you are modelling. This would be a bad ...


3

I would consider county-level research macroeconomics. Microeconomics focuses on individuals and firms acting as decision makers. I think, looking at county-level aggregates, you are too high level to look at decision makers.


Only top voted, non community-wiki answers of a minimum length are eligible