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As long as the main results/conclusions of your paper don't rely solely on the non-economics literature you cite, you should be okay. In other words, it's perfectly fine to use non-economics literature to motivate or even as part of the support for your thesis, as long as you also include proper economic arguments, i.e. theoretical models or econometric ...
9
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 ...
9
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 ...
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.
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There seem to be two things:
The first is that "perceived value" is not something that directly corresponds to something economists usually study. It might be related to, say, marriage patterns and household incomes, political power, or whatnot. If you can show that it is an important aspect of a problem that falls within the (vague) boundaries of ...
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
It may be interesting to exploit the homothetic separability of the CES utility function in $x$. It implies that
$$\frac{x_i}{x_j} = \left( \frac{\alpha_i}{\alpha_j}\frac{p_j}{p_i} \right)^\sigma $$
and after $log$-transformation:
$$\ln(x_i) - \ln(x_j) = \beta_{ij} + \sigma (\ln(p_j) - \ln(p_i)). $$
After adding a random term, this specification could be ...
7
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. ...
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 ...
6
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 $1\%$ ...
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
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 ...
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
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 ...
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
This answer closely follows the logic of estimation of translog cost function presented in Section 4.7 of Fumio Hayashi's "Econometrics".
Define for convenience the CES aggregate price index $P:=(\sum_i \alpha_i^\sigma p_i^{1-\sigma})^\frac{1}{1-\sigma}$. Then the Marshallian demand system in log form can be written as a system of linear equations
$...
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
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
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 ...
4
There are many ressources available online
Synth is a statistical software that implements synthetic control methods for causal inference in comparative case studies with aggregate data as described in Abadie and Gardeazabal (2003), Abadie, Diamond, and Hainmueller (2010), and Abadie, Diamond, and Hainmueller (2013). The Synth package for R is also ...
4
In this comment I simply show that
Under certain assumptions the problem is not an estimation problem, there is an exact solution for $\sigma$ and a solution for the structural errors $\alpha_i$ up to a normalisation.
The demand system considered is described by the Marshall demand function
$$x_k^\star(p,M) = \left(\frac{\alpha_j}{p_j}\right)^{\sigma}\frac{...
4
Depends on a trend. Trends come generally in two categories:
Deterministic trend - this can be controlled for using various methods. For example, in panel regression you could include time fixed effects that would correct for an effect of each time period on all firms and hence should control for a trend in data. This would look like this: $$y_{it} = \beta_{...
4
"Identification" is the most loaded term in econometrics. There are multiple cheap talk equilibria with regard to its meaning. It is used with different intended (but related and overlapping) meanings, in different contexts, by people with different orientations, with different levels of precision.
Therefore you will get a range of correct answers.
...
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Emsemble learning is beneficial in forecasting where statistical adequacy of any model in the ensemble is of limited importance. However, when models are used for inference, statistical adequacy is quite important. The correctnes of the assumed null distribution of the test statistic depends (to a larger or smaller degree) on the statistical adequacy of the ...
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