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on Discrete Choice Models |
| By: | Gerhardt, Michaela V.; Kanberger, Elke D.; Ziegler, Andreas |
| JEL: | R4 Q5 D12 C35 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc23:277675&r=dcm |
| By: | Jeffrey Mensch; Komal Malik |
| Abstract: | We analyze a problem of revealed preference given state-dependent stochastic choice data in which the payoff to a decision maker (DM) only depends on their beliefs about posterior means. Often, the DM must also learn about or pay attention to the state; in applied work on this subject, a convenient assumption is that the costs of such learning are linearly dependent in the distribution over posterior means. We provide testable conditions to identify whether this assumption holds. This allows for the use of information design techniques to solve the DM's problem. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.09496&r=dcm |
| By: | Clement E. Bohr; Mart\'i Mestieri; Emre Enes Yavuz |
| Abstract: | We provide four novel results for nonhomothetic Constant Elasticity of Substitution preferences (Hanoch, 1975). First, we derive a closed-form representation of the expenditure function of nonhomothetic CES under relatively flexible distributional assumptions of demand and price distribution parameters. Second, we characterize aggregate demand from heterogeneous households in closed-form, assuming that household total expenditures follow an empirically plausible distribution. Third, we leverage these results to study the Euler equation arising from standard intertemporal consumption-saving problems featuring within-period nonhomothetic CES preferences. Finally, we show that nonhomothetic CES expenditure shares arise as the solution of a discrete-choice logit problem. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06740&r=dcm |
| By: | Fu Ouyang; Thomas Tao Yang |
| Abstract: | This paper proposes a new method for estimating high-dimensional binary choice models. The model we consider is semiparametric, placing no distributional assumptions on the error term, allowing for heteroskedastic errors, and permitting endogenous regressors. Our proposed approaches extend the special regressor estimator originally proposed by Lewbel (2000). This estimator becomes impractical in high-dimensional settings due to the curse of dimensionality associated with high-dimensional conditional density estimation. To overcome this challenge, we introduce an innovative data-driven dimension reduction method for nonparametric kernel estimators, which constitutes the main innovation of this work. The method combines distance covariance-based screening with cross-validation (CV) procedures, rendering the special regressor estimation feasible in high dimensions. Using the new feasible conditional density estimator, we address the variable and moment (instrumental variable) selection problems for these models. We apply penalized least squares (LS) and Generalized Method of Moments (GMM) estimators with a smoothly clipped absolute deviation (SCAD) penalty. A comprehensive analysis of the oracle and asymptotic properties of these estimators is provided. Monte Carlo simulations are employed to demonstrate the effectiveness of our proposed procedures in finite sample scenarios. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.07067&r=dcm |
| By: | Buchali, Katrin; Grüb, Jens; Muijs, Matthias; Schwalbe, Ulrich |
| JEL: | D43 D83 L13 L49 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc23:277695&r=dcm |
| By: | Justyna Tanas |
| Abstract: | The article aims to identify the revealed preferences of buyers in the secondary housing market in Warsaw. The study was conducted based on data on real estate transactions made in the secondary market in 2016-2020 in Warsaw. The data was supplemented with information from the land register (section II - ownership), the real estate cadastre and using Google Street View. Based on hedonic models and unique datasets covering more than 25, 000 observations, preferences were investigated by gender, age and marital status. |
| Keywords: | buyers' preferences; housing market; revealed vs. stated preferences |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_329&r=dcm |
| By: | Baltagi, Badi H. (Syracuse University); Jimenez-Martin, Sergi (Universitat Pompeu Fabra); Labeaga, José M. (UNED); al Sadoon, Majid (Durham University Business School) |
| Abstract: | The properties of classical panel data estimators including fixed effect, first-differences, random effects, and generalized method of moments-instrumental variables estimators in both static as well as dynamic panel data models are investigated under sample selection. The correlation of the unobserved errors is shown not to be sufficient for the inconsistency of these estimators. A necessary condition for this to arise is the presence of common (and/or non-independent) non-deterministic covariates in the selection and outcome equations. When both equations do not have covariates in common and independent of each other, the fixed effects, and random effects estimators in static models with exogenous covariates are consistent. Furthermore, the first-differenced generalized method of moments estimator uncorrected for sample selection as well as the instrumental variables estimator uncorrected for sample selection are both consistent for autoregressive models even with endogenous covariates. The same results hold when both equations have no covariates in common but are correlated once we account for such correlation. Under the same circumstances, the system generalized method of moments estimator adding more moments from the levels equation has moderate bias. Alternatively, when both equations have common covariates the appropriate correction method is suggested. Serial correlation of the errors being a key determinant for that choice. The finite sample properties of the proposed estimators are evaluated using a Monte Carlo study. Two empirical illustrations are provided. |
| Keywords: | panel data, sample selection, generalized method of moments, fixed and random effects, differenced estimator |
| JEL: | J52 C23 C24 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16594&r=dcm |