Using the data provided by Fiji's ministry of health and medical services, we apply an implicit time-discrete SIR (susceptible people–infectious people–removed people) model that tracks the transmission and recovering rate at time, t to predict the trend of the coronavirus disease 2019 (COVID-19) pandemic in Fiji. The model implied time-varying transmission and recovery rates were calculated from 4 May 2021 to 9 October 2021. The estimator functions for these rates were determined, and a short-term (30 days) forecast was done. The model was validated with observed values of the active and recovered cases from 11 October 2021 to 9 December 2021. Statistical results reveal a good fit of profiles between model simulated and the reported COVID-19 data. The gradual decrease of the time-varying basic reproduction number with values below one towards the end of the study period suggest the government's success in controlling the epidemic. The mean reproduction number for the second wave of COVID-19 in Fiji was estimated as 2.7818. The results from this study can be used by researchers, the Fijian government, and the relevant health policy makers in making informed decisions should a third COVID-19 wave occur.

This paper studies the impact of immigration on the US macroeconomy. I identify structural vector autoregressions (SVARs) with time-varying parameters (TVPs) and stochastic volatility (SV) using a novel set of restrictions. The TVP-SV-SVARs are estimated on a quarterly sample including average labor productivity (ALP), hours worked, immigration, consumption, and term spread from 1953 to 2017. An immigration supply shock increases domestic ALP and hours worked over the business cycle horizons. Movements in immigration are explained by its own shock and to a lesser extent by the productivity and news shocks. IRFs driven by these shocks vary over the sample, especially around changes in immigration policy such as the Immigration Act of 1990. In contrast, the forecast error variance decompositions exhibit little change over the sample. Immigration plays an important role in the US macroeconomy.

In the economics literature, there are two dominant approaches for solving models with optimal experimentation (also called active learning). The first approach is based on the value function and the second on an approximation method. In principle the value function approach is the preferred method. However, it suffers from the curse of dimensionality and is only applicable to small problems with a limited number of policy variables. The approximation method allows for a computationally larger class of models, but may produce results that deviate from the optimal solution. Our simulations indicate that when the effects of learning are limited, the differences may be small. However, when there is sufficient scope for learning, the value function solution seems more aggressive in the use of the policy variable.

An expected utility model that includes output price and yield uncertainty was used to estimate cotton, com, and soybean acreage response equations for the Southeast. The model appeared to fit the soybean and corn data well, resulting in own-price elasticity estimates of 0.317 for com and 0.727 for soybeans. When applied to cotton acreage, however, the model did not yield satisfactory results. When elasticity was allowed to change over time, however, statistical results for the cotton equation improved, yielding an own-price elasticity of 0.915 at data means.

The U.S. paper industry has become increasingly concentrated and therefore been suspected of imperfect competition. In this study, the new empirical industrial organization approach is employed to measure the degree of oligopoly and oligopsony power in the U.S. paper industry simultaneously. The model is estimated by iterative three-stage least squares using annual data from 1955 to 2003. The results reveal that there has been significant oligopoly and oligopsony power in the U.S. paper industry, and the oligopoly power has been consistently lower than the oligopsony power.

We aim at establishing whether the institutional adoption of inflation targeting has changed the conduct of monetary policy. To do so, we test the hypothesis of inflation targeting translating into a stronger response to inflation in a Taylor rule with three alternative econometric models: a structural break model, a time-varying parameter model with stochastic volatility, and a Markov-switching VAR model. We conclude that inflation targeting has not led to a stronger response to inflation in the reaction function of the monetary authority. This result suggests that inflation targeting being meant to anchor inflation expectations through enhanced credibility and accountability, it may enable a central bank to stabilize inflation without pursuing aggressive action toward inflation variations.

In their landmark paper, Bray and Savin note that the constant-parameters model used by their agents to form expectations is misspecified and that, using standard econometric techniques, agents may be able to determine the time-varying nature of the model's parameters. Here, we consider the same type of model as employed by Bray and Savin except that our agents form expectations using a perceived model with parameters that vary with time. We assume agents use the Kalman filter to form estimates of these time-varying parameters. We find that, under certain restrictions on the structure of the stochastic process and on the value of the stability parameter, the model will converge to its rational expectations equilibrium. Further, the restrictions on the stability parameter required for convergence are identical to those found by Bray and Savin.