# econometrics.it

Federico Belotti's niche on the web

## icio: Analysis of Inter-Country Input-Output tables

Together with Michele Mancini and Alessandro Borin, we have just released a preliminary version of icio, a new Stata command for Global Value Chains (GVCs) analysis.

icio exploits the Inter-Country Input-Output tables: the WIOD dataset is the default, but also OECD TiVA and user-provided tables can be loaded.

By simplifying the analysis of trade in value-added and countries' participation in GVCs, the command should be useful for researchers that are not actively engaged in the GVCs literature.

You can install the command by typing in the Stata command bar:

ssc install icio

Once the command is installed, by typing

icio_load, iciot(wiod) year(2014)

a Mata version of the WIOD 2014 table is downloaded and stored in memory.

Several options are available (see help icio).

The command can compute:

1. Value added in domestic and foreign demand, also at the sectoral level;
2. Koopman, Wang and Wei (2014) decomposition of aggregate exports;
3. Borin and Mancini (2017) decompositions of bilateral and bilateral-sectoral exports, retrieving for each bilateral flow, aside from the exporting and the importing country, also the country of origin of the value-added as well as the final destination market;
4. Measure of GVC-related trade, defined as goods and services crossing at least two borders (again following Borin and Mancini, 2017).

Of special note is that it is possible to define country-groups and to use user-provided IO tables. In the next release of the command, a dialog interface will be added.

HTH,
Federico

References

Borin, A., and M. Mancini (2017). Follow the value-added: tracking bilateral relations in Global Value Chains. MPRA Working Paper, No. 82692

Koopman, R., Z. Wang and S. Wei (2014). Tracing Value-Added and Double Counting in Gross Exports. American Economic Review, 104(2), 459-94

## sreg: A Stata command for spatial differencing estimation

A new Stata command for spatial differencing estimation is now available. sreg fits the following model:

$y_{ic}= \theta_z + \mathbf{x}_{ic}'\mathbf{\beta} + \epsilon_{ic}$

where $y_{ic}$ is the outcome of unit $i$ located in area $c$ with $c=1,\ldots,C$, $\mathbf{x}_{ic}$ is a $k$-vector of exogenous covariates, $\epsilon_{ic}$ is the idiosyncratic error and $\theta_z$ is an unobserved local effect for the unobserved location $z$, $z=1,\ldots,Z$, possibly at a finer spatial scale than $c$. Estimating this model by ordinary least squares ignoring $\theta_z$ gives a consistent estimate of $\mathbf{\beta}$ only if $\mathbb{E}(\theta_z|\mathbf{x}_{ic})=0$. If we set aside this unrealistic assumption and allow for arbitrary correlation between the local unobservables and the explanatory variables, i.e. $\mathbb{E}(\theta_z|\mathbf{x}_{ic})\neq 0$, a non-experimental approach to estimating the model involves, in some way, transforming the data to rule out $\theta_z$. An increasingly common way to deal with this issue is the so-called spatial differencing approach.

sreg implements the spatial differencing estimator described in Belotti et al. (2017), as well as different variance-covariance estimators, among which the dyadic-robust (Cameron and Miller, 2014) and the Duranton et al. (2011)'s analytically-corrected estimators. Of special note is that sreg also allow to apply the spatial differencing transformation to units located in contiguous clusters ("boundary-discontinuity" design).

The command was written together with Edoardo Di Porto and Gianluca Santoni.

You may install it by typing

net install sreg, from(http://www.econometrics.it/stata)

in your Stata command bar. Stata version 14.2 is required.

HTH,
Federico

## sftfe: A Stata command for fixed-effects stochastic frontier models estimation

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A new Stata command for the consistent estimation of fixed-effects stochastic frontier models is now available. sftfe fits the following fixed-effects stochastic frontier model:

$y_{it} = \alpha_i + x_{it}\beta + v_{it} \pm u_{it}$

where $v_{it}$ is a normally distributed error term and $u_{it}$ is a one-sided strictly non-negative term representing inefficiency. The sign of the $u_{it}$ term is positive or negative depending on whether the frontier describes a cost or production function, respectively. sftfe allows the underlying mean and variance of the inefficiency (as well as the variance of the idiosyncratic error) to be expressed as functions of exogenous covariates. Of special note is that sftfe allows the estimation of models in which the inefficiency is assumed to follow a first-order autoregressive process.

Technical details related to the estimators implemented in the command can be found in the article:

Belotti, F., Ilardi, G., (2017). Consistent Inference in Fixed-effects Stochastic Frontier Models, Journal of Econometrics.

The command was written together with Giuseppe Ilardi.

You may install it by typing

net install sftfe, all from(http://www.econometrics.it/stata)in your Stata command bar.

HTH,
Federico